From abacc4c41c4a1c8a8aaf769ba25e2e3da99c2207 Mon Sep 17 00:00:00 2001 From: wuchengwei Date: Wed, 6 Dec 2023 20:25:35 +0800 Subject: [PATCH 01/38] lid --- language_identification/jsonql.py | 1340 ++++++++++++++++++++++ language_identification/readMe.md | 0 language_identification/split_by_lang.py | 122 ++ requirements.txt | 5 +- 4 files changed, 1466 insertions(+), 1 deletion(-) create mode 100644 language_identification/jsonql.py create mode 100644 language_identification/readMe.md create mode 100644 language_identification/split_by_lang.py diff --git a/language_identification/jsonql.py b/language_identification/jsonql.py new file mode 100644 index 0000000..b5ab405 --- /dev/null +++ b/language_identification/jsonql.py @@ -0,0 +1,1340 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# + +""" +Manipulate files containing one json per line. +""" +import argparse +import collections +import contextlib +import functools +import glob +import gzip +import importlib +import inspect +import io +import itertools +import json +import logging +import multiprocessing +import os +import re +import sys +import tempfile +import time +import typing as tp +import warnings +import zlib +from pathlib import Path +from typing import ( + Callable, + Dict, + Iterable, + Iterator, + List, + Optional, + Sequence, + TextIO, + Tuple, + Union, +) + +import numpy as np +import psutil # type: ignore +import requests +from typing_extensions import Protocol + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)s %(process)d:%(name)s - %(message)s", + datefmt="%Y-%m-%d %H:%M", +) + +NEWLINE = " N3WL1N3 " + +FilterFn = Callable[[dict], bool] +FileDescriptor = Union[Path, List[Path], str] +WritableFileLike = Union[FileDescriptor, TextIO, "SimpleIO", None] +ReadableFileLike = Union[Iterable[str], FileDescriptor, None] + + +def io_parser(): + """Parser shared by all commands to get input/output files.""" + parser = argparse.ArgumentParser(add_help=False) + file_help = """File to read from. Can be specified several times for several files. + Be careful that bash will expand glob patterns **before** sending the args + to python. To use globs put it inside single quotes: + jsonql where --file 'data/perplexity/*.json' '{length} > 100' | head -1 + jsonql --file 'data/perplexity/*.json' where '{length} > 100' | head -1 + [Invalid] jsonql where '{length} > 100' --file data/perplexity/*.json | head -1 + [Invalid] jsonql where --file data/perplexity/*.json '{length} > 100' | head -1 + """ + parser.add_argument("-f", "--file", type=Path, action="append", help=file_help) + parser.add_argument("-o", "--output", type=Path, default="-") + parser.add_argument("--processes", type=int, default=1) + return parser + + +def get_parser(): + parser = argparse.ArgumentParser( + description="Read a set of json files and allow to query them" + ) + subparsers = parser.add_subparsers() + + def add_subparser(function, arguments): + doc = function.__doc__.split("\n")[0] + p = subparsers.add_parser(function.__name__, help=doc, parents=[io_parser()]) + p.set_defaults(command=function) + for k, v in arguments.items(): + p.add_argument(k, **v) + + add_subparser( + select, + { + "columns": dict(nargs="+", help="Extract the value of the given fields"), + "--skip_empty": dict( + action="store_true", help="Skip lines without the requested fields" + ), + "--separator": dict( + default="\t", help="Separator to use between the different columns" + ), + "--newline": dict( + default=NEWLINE, + help="Replace newlines found in the text by the given string", + ), + }, + ) + + add_subparser( + where, + { + "clauses": dict(nargs="+", help=""), + "--requires": dict( + action="append", help="Python module required by the clauses code." + ), + }, + ) + + add_subparser( + merge, + { + "columns": dict(nargs="+", help=""), + "--separator": dict( + default="\t", help="Separator to use between the different columns" + ), + "--newline": dict( + default=NEWLINE, help="Replace the given string by actual newlines" + ), + }, + ) + + add_subparser( + describe, + { + "columns": dict(nargs="*", help=""), + "--bins": dict( + default="auto", help="Number of bins for computing the histograms" + ), + "--cumulative": dict( + action="store_true", help="Compute cumulative histograms" + ), + "--weights": dict(type=str, help="Column used to weight histograms"), + }, + ) + + add_subparser(split, {"--pattern": dict(type=str)}) + add_subparser(shard, {}) + return parser + + +def _split_array(array, sep): + last = 0 + for i, x in enumerate(array): + if x != sep: + continue + yield array[last:i] + last = i + 1 + if last != len(array): + yield array[last:] + + +def main(raw_args): + parser = get_parser() + pipeline = [] + file = "-" + output = "-" + processes = 1 + + for args_group in _split_array(raw_args, "--"): + args = vars(parser.parse_args(args_group)) + command = args.pop("command") + file = args.pop("file") or file + output = args.pop("output") or output + processes = args.pop("processes") or processes + pipeline.append(as_pipe(command, args)) + + if not pipeline: + parser.print_help() + return + + run_pipes(*pipeline, file=Path(file), output=Path(output), processes=processes) + + +class Transformer: + """ + Wrapper around functions transforming documents. + + This allows `run_pipes` to automatically parallelize the pipeline. + Provides: + * Automatic logging. Logging can be changed with the `summary` method. + Loggin frequency with _log_freq (in second) or $JSONQL_LOG_FREQ env variable. + * Automatic parallelization without pickling. The transformers are shared + across processes, and the object is usually not pickled. + * Basic pickling / unpickling in case it's still needed. + By default will only pickle the arguments passed to the constructor. + * Delayed initialization. Internal state which is not pickable should be set + inside the `_prepare` function. + """ + + parallelisable: bool = True + expect_json: bool = False + warn_when_pickling: bool = False + ready: bool = False + + def __init_subclass__(cls, expect_json: bool = None): + """Detects if the subclass expects json as input.""" + spec = inspect.getfullargspec(cls.do) + if expect_json is None: + expect_json = spec.annotations.get(spec.args[1], None) == dict + + cls.expect_json = expect_json + + def __new__(cls, *args, **kwargs): + """Creates the transformer and save the arguments passed to the constructor.""" + t = super().__new__(cls) + Transformer.__init__(t, args, kwargs) + return t + + def __init__(self, state_args: tuple = None, state_kwargs: dict = None): + """ + Init the transformer counters. + + If state_args/state_kwargs are set they will override whatever was + originally passed to the subclass constructor. + """ + if state_args is not None: + self.__args = state_args + if state_kwargs is not None: + self.__kwargs = state_kwargs + + self.start_time = time.time() + self.__last_log = self.start_time + self.processed = 0 + # Log every 5 min unless specified other wise. + self._log_freq = int(os.environ.get("JSONQL_LOG_FREQ", 5 * 60)) + self.__cls = type(self) + self._logger = logging.getLogger(self.__cls.__name__) + + def __call__(self, x): + assert self.ready, f"{self} is not ready." + if x is None: + return + y = self.do(x) + self.processed += 1 + if time.time() - self.__last_log > self._log_freq: + self.log_summary() + return y + + def do(self, x): + raise NotImplementedError(f"'do' not implemented in {type(self)}") + + def summary(self) -> List[str]: + return [self.speed_summary()] + + def speed_summary(self) -> str: + delay = time.time() - self.start_time + h = delay / 3600 + s = self.processed / delay + return f"Processed {self.processed:_} documents in {h:.2}h ({s:5.1f} doc/s)." + + def log(self, message): + self._logger.info(message) + + def log_summary(self) -> None: + if not self.ready: + self.log("Not ready.") + return + summ = self.summary() or [] + for line in summ: + self.log(line) + self.__last_log = time.time() + + def map(self, source: Iterable) -> Iterator: + if self.ready: + for x in source: + yield self(x) + # since we have been prepared by caller, + # caller is also responsible for calling `close`. + return + else: + with self: + for x in source: + yield self(x) + + def __getstate__(self) -> Tuple[tuple, dict, bool]: + return (self.__args, self.__kwargs, self.expect_json) + + def __setstate__(self, state: Tuple[tuple, dict, bool]): + if self.warn_when_pickling: + warnings.warn(f"Unpickling transformer: {type(self)}. This can be slow.") + (args, kwargs, expect_json) = state + # When unpickling `__new__` isn't called so we have to doit ourselves. + Transformer.__init__(self, state_args=args, state_kwargs=kwargs) + type(self).__init__(self, *args, **kwargs) + assert self.expect_json == expect_json + # __setstate__ is called by multiprocessing right before calling + # the object so we need to initialize everything. + self.__enter__() + + def _prepare(self) -> None: + pass + + def __enter__(self) -> "Transformer": + # In multiprocessing __enter__ is always called twice, so we are idempotent. + # Because we call __enter__ when deserializing this transformer and + # also when the parent transformer is deserialized. + self.start_time = time.time() + if self.ready: + return self + self._prepare() + self.ready = True + return self + + def __exit__(self, *args) -> None: + self.close() + self.log_summary() + + def close(self) -> None: + pass + + +def as_pipe(transformer, kwargs): + if isinstance(transformer, type): + return transformer(**kwargs) + return lambda source: transformer(source, **kwargs) + + +def compose(fns: List[Transformer]) -> Transformer: + if len(fns) == 1: + return fns[0] + return MultiTransformer(fns) + + +class MultiTransformer(Transformer): + def __init__(self, transformers: List[Transformer]): + super().__init__() + self.transformers = transformers + + def __repr__(self) -> str: + pipeline = " | ".join(type(t).__name__ for t in self.transformers) + return f"<{pipeline}>" + + def do(self, x): + for t in self.transformers: + x = t(x) + return x + + def _prepare(self): + for t in self.transformers: + t.__enter__() + return self + + def __exit__(self, *args): + for t in self.transformers: + t.__exit__(*args) + + def summary(self): + return itertools.chain(*(t.summary() for t in self.transformers)) + + +class Mapper(Transformer): + def __init__(self, fn): + super().__init__() + self.fn = fn + + def do(self, x): + return self.fn(x) + + +def run_pipe( + command, + kwargs: dict = None, + file: ReadableFileLike = None, + output: WritableFileLike = None, +): + kwargs = kwargs or {} + if isinstance(kwargs, argparse.ArgumentParser): + kwargs = vars(kwargs.parse_args()) + file = file or Path(kwargs.pop("file", "-")) + output = output or Path(kwargs.pop("output", "-")) + + return run_pipes(as_pipe(command, kwargs), file=file, output=output) + + +def run_pipes( + *fns: Union[Transformer, Callable[[Iterable], Iterable]], + inputs: Iterable[dict] = None, + file: ReadableFileLike = None, + output: WritableFileLike = None, + processes: int = 1, + chunksize: int = 10_000, +): + """ + Run full document processing pipeline. + + - fns: list of functions to run over the documents. Can be: + * `Iterable -> Iterable` function + * jsonql.Transformer instance + Using transformers allow the pipeline to process documents in parallel. + - inputs: iterable to read the documents from + - file: if inputs is not given, will read documents from this file. + - output: writable file like. + - processes: number of processes to use. -1 means all CPU available. + - chunksize: chunksize for multiprocessing.Pool.imap_unordered + """ + expect_json = len(fns) and isinstance(fns[0], Transformer) and fns[0].expect_json + if expect_json and inputs is None: + fns = (JsonReader(),) + fns + transformers = [] + for t in fns: + if not isinstance(t, Transformer): + break + if not t.parallelisable: + break + transformers.append(t) + pipes = fns[len(transformers) :] + + log = logging.getLogger(__name__).info + if inputs is None: + data: Iterable = open_read(file) + else: + data = inputs + + if processes == -1: + processes = os.cpu_count() or 0 + + with contextlib.suppress(BrokenPipeError), contextlib.ExitStack() as stack: + if transformers: + log(f"preparing {transformers}") + transform = stack.enter_context(compose(transformers)) + if processes <= 1: + data = transform.map(data) + else: + p = multiprocessing.current_process() + log(f"Will start {processes} processes from {p.name}, Pid: {p.pid}") + pool = stack.enter_context( + multiprocessing.Pool( + processes=processes, + initializer=_set_global_transformer, + initargs=(transform,), + ) + ) + data = pool.imap_unordered( + _global_transformer, data, chunksize=chunksize + ) + + for fn in pipes: + if isinstance(fn, Transformer): + data = fn.map(data) + else: + data = fn(data) + + write_jsons(data, output) + + +# Allows to share transformer acroos subprocess. +# Used by `run_pipes` +_GLOBAL_TRANSFORMER: Optional[Transformer] = None + + +def _set_global_transformer(transformer: Transformer): + global _GLOBAL_TRANSFORMER + p = multiprocessing.current_process() + logging.info( + f"Started subprocess {p.name}:{p.pid} from {os.getppid()} for {transformer}" + ) + assert transformer.ready, f"{transformer} isn't ready" + _GLOBAL_TRANSFORMER = transformer + + +def _global_transformer(document: str) -> Optional[dict]: + assert _GLOBAL_TRANSFORMER is not None + return _GLOBAL_TRANSFORMER(document) + + +def lines(file: ReadableFileLike) -> Iterator[str]: + return (line.strip("\n") for line in open_read(file)) + + +def read_jsons(file: ReadableFileLike, strict=False) -> Iterator[dict]: + reader = JsonReader(strict=strict) + lines = open_read(file) + for line in lines: + if line is None: + continue + yield reader(line) + + reader.log_summary() + + +def write_jsons(source: Iterable[dict], file: WritableFileLike) -> None: + eol = os.linesep + with open_write(file) as o: + for res in source: + if res is None: + continue + if isinstance(res, dict): + json.dump(res, o, ensure_ascii=False) + o.write(eol) + continue + if isinstance(res, str): + res = res.rstrip("\n") + print(res, file=o) + + +class JsonReader(Transformer): + def __init__(self, strict: bool = False): + super().__init__() + self.ready = True + self.strict = strict + self.num_errors = 0 + + def do(self, line: str) -> Optional[dict]: + if line is None: + return None + if isinstance(line, dict): + return line + line = line.rstrip("\n") + if not line: + return None + try: + return json.loads(line) + except json.decoder.JSONDecodeError as e: + self.log_error(e) + if self.strict: + raise + return None + + def log_error(self, e: json.decoder.JSONDecodeError): + self.num_errors += 1 + if self.num_errors > 10: + return + + MAX_LEN = 80 + snippet, snippet_len = e.doc, len(e.doc) + col = e.pos + if snippet_len > MAX_LEN: + if col < MAX_LEN: + start = 0 + elif snippet_len - col < MAX_LEN: + start = snippet_len - MAX_LEN + else: + start = col - MAX_LEN // 2 + snippet = e.doc[start : start + MAX_LEN] + col = col - start + logging.warning( + "\n".join( + [ + f"Invalid json (length={len(e.doc)}) {e}", + snippet, + " " * (col - 1) + "^", + ] + ) + ) + + def summary(self): + summ = super().summary() + if self.num_errors > 0: + summ.append(f"Skipped {self.num_errors} invalid json.") + return summ + + +def compile_column(column, newline): + if callable(column): + return column + + if column == "*": + return json.dumps + + if re.match(r"[_a-z][_a-z0-9]*", column): + + def extract_col(doc): + v = doc.get(column, "") + if isinstance(v, str) and newline != "\n": + v = v.rstrip("\n").replace("\n", newline) + return v + + return extract_col + + return compile_expr(column) + + +def select(lines, columns, skip_empty=False, separator="\t", newline="\n"): + """Yields the content of the requested columns.""" + column_parsers = [compile_column(c, newline) for c in columns] + for doc in read_jsons(lines): + values = [] + empty = True + for parse_col in column_parsers: + v = parse_col(doc) + values.append(str(v) or "") + empty = empty and v is None + + if skip_empty and empty: + continue + + yield separator.join(values) + + +def compile_expr(clause: Union[str, FilterFn], requires: List[str] = None): + if not isinstance(clause, str): + return clause + + args_re = r"(?i:\{([_a-z][_a-z0-9]*)\})" + args_list = list(re.findall(args_re, clause)) + if not args_list: + # This is only a warning because you may want to have eg random sampling + # that doesn't depend on the document. + logging.warn( + f"Warning: No variable found in expression: <{clause}>\n" + "Variables should be written inside braces, eg: {language}=='en'" + ) + python_like = re.sub(args_re, r"doc.get('\1', None)", clause) + requires = requires or [] + modules = {r: importlib.import_module(r) for r in requires} + return eval(f"lambda doc: {python_like}", modules) + + +class where(Transformer): + """Filters the data using python code. + + Ex: `jsonql where 'len({text}) > 100'` + """ + + def __init__( + self, clauses: Sequence[Union[str, FilterFn]], requires: List[str] = [] + ): + super().__init__() + self.raw_clauses = clauses + self.requires = requires + self.n_selected = 0 + self.clauses: List[FilterFn] = [] + + def _prepare(self): + self.clauses = [compile_expr(c, self.requires) for c in self.raw_clauses] + + def do(self, doc: dict) -> Optional[dict]: + assert self.clauses + if not doc or not all((c(doc) for c in self.clauses)): + return None + self.n_selected += 1 + return doc + + def summary(self): + n_selected, n_docs = self.n_selected, self.processed + selectivity = n_selected / n_docs if n_docs else 0 + return [f"Selected {n_selected} documents out of {n_docs} ({selectivity:5.1%})"] + + +def merge(lines, columns, separator="\t", newline=NEWLINE): + """Reads tab separated columns and output a json using the given headers. + + Headers are of form {key}[%{type}] + {type} can be one of {"f": float, "i": int, "b": bool, "s": string}. + Default type is string. + A special header "_" means interpret this column as json, and append all other + columns to it. Must appear only once and on last position. + + Ex: + `echo '1\thello' | jsonql merge n t` --> `{"n": "1", "t": "hello"}` + `echo '1\thello" | jsonql merge n%i t` --> `{"n": 1, "t": "hello"}` + `echo '1\thello\t{"f": "bar"}' | jsonql merge n%i t _` --> `{"n": 1, "t": "hello", "f": "bar"}` + """ + handle_newlines = lambda s: s.replace(newline, "\n") + type_mapping: Dict[str, Callable] = { + "f": float, + "i": int, + "b": bool, + "s": handle_newlines, + } + type_parsing = [ + type_mapping.get(f.split("%")[-1], handle_newlines) for f in columns + ] + columns = [f.split("%")[0] for f in columns] + doc_index = columns.index("_") if "_" in columns else -1 + read_json = JsonReader() + + def parse(line): + parts = line.split(separator, len(columns) - 1) + doc: Dict[str, tp.Any] = {} + for i, value in enumerate(parts): + if columns[i] == "_": + doc.update(read_json(parts[doc_index])) + else: + try: + doc[columns[i]] = type_parsing[i](value) + except ValueError: + logging.error( + f"Error when parsing column {i} of line: {line[:100]}..." + ) + return doc + + for line in lines: + yield json.dumps(parse(line)) + + +class split(Transformer): + """Split a files in several smaller files based on the value of a field.""" + + # Not parallelisable since we are writing to files. + parallelisable = False + + def __init__( + self, + pattern: Union[Path, str] = None, + split_fn: Callable[[dict], str] = None, + mkdir: bool = False, + ): + super().__init__() + assert not ( + pattern and split_fn + ), "split can't have both a pattern and a split_fn" + if split_fn is not None: + self.split_fn = split_fn + else: + assert pattern, "split need either a pattern or a split_fn" + self.split_fn = self.make_split_fn(str(pattern)) + self.mkdir = mkdir + self.o: dict = {} + + def make_split_fn(self, pattern: str) -> Callable[[dict], str]: + candidates = list(re.findall(r"(?i:\{([_a-z][_a-z0-9]*)\})", pattern)) + return lambda doc: pattern.format(**{c: doc[c] for c in candidates}) + + def do(self, doc): + filename = self.split_fn(doc) + if not filename: + return + o = self.o.get(filename, None) + if o is None: + if self.mkdir: + Path(filename).parent.mkdir(parents=True, exist_ok=True) + self.o[filename] = open_write(filename) + print(json.dumps(doc, ensure_ascii=False), file=self.o[filename], flush=True) + + def summary(self): + summ = super().summary() + summ.append(f"Found {len(self.o)} splits.") + return summ + + def close(self): + for file in self.o.values(): + file.close() + + +def histogram(values, bins, weights): + hist, bins = np.histogram(values, bins=bins) + # n_bins = len(hist) + + if weights is not None: + # Bins can't be auto-determined if weights is supplied. + # So we first compute the bins without the weights then recompute + # the histogram with the weights. + hist, bins = np.histogram(values, bins=bins, weights=weights) + # cumsum = np.cumsum(hist) + # total = cumsum[-1] + + # for i in range(n_bins - 1): + # if cumsum[i] / total > 0.9: + # useful_range = np.linspace(bins[0], bins[i + 1], n_bins) + # new_bins = np.append(useful_range, [bins[-1]]) + # return np.histogram(values, bins=new_bins, weights=weights) + + return hist, bins + + +def _parse_bins(bins): + try: + if isinstance(bins, str): + if "," in bins: + bins = [int(b) for b in bins.split(",")] + else: + bins = int(bins) + except ValueError: + pass + return bins + + +ALL_DOCUMENTS = "" +MAX_LABEL_LEN = 100 + + +def bar_chart(hist, bins): + n = sum(hist) + max_h = max(hist) + out = [] + for i, h in enumerate(hist): + h_size = 80 * h // max_h + dh_size = 80 * (h - hist[i - 1]) // max_h + if h_size == 0 or dh_size == 0: + continue + bar = "█" * h_size + out.append(f"{bins[i]:8.3f} {bar:80} ({h:5d}, {h / n:5.1%}) {bins[i+1]:8.3f}") + out.append(f"{bins[-1]:8.3f}") + return out + + +def display_stats(stats, key, weights=None, bins="auto", cumulative=False): + out = [] + documents = stats[ALL_DOCUMENTS] + count = stats.get(key, 0) + r = count / documents if documents else 0 + out.append(f"Field {key} saw {count} times ({r:5.1%})") + + length = stats.get(key + ".length", None) + avg_length = length // count if length else 0 + if length is not None: + out[-1] += f", average length is {length // count}" + + values = stats.get(key + ".val", None) + if values: + out[-1] += f", histogram is: (bins={bins})" + if weights: + if weights not in stats: + logging.warn(f"Warning: weights column {weights} not found.") + if weights + ".val" not in stats: + logging.warn( + f"Warning: weights column {weights} is not a numeric column." + ) + weights = stats.get(weights + ".val") + hist, bins = histogram(values, _parse_bins(bins), weights) + if cumulative: + hist = np.cumsum(hist) + out += bar_chart(hist, bins) + + cnt = stats.get(key + ".cnt", None) + if avg_length < MAX_LABEL_LEN and cnt and max(cnt.values()) > 1: + cnt = sorted(cnt.items(), key=lambda kv: kv[1], reverse=True) + out[-1] += ", top 100 labels:" + for label, n in cnt[:100]: + if n < 5: + continue + out.append(f"{label:25}: {n:6} ({n / count:5.1%})") + + return out + + +def describe(source, columns=None, weights=None, **kwargs): + """Compute some statistics about a dataset. + + Stats can be restricted to a subset of columns.""" + MAX_HIST_SIZE = 100_000_000 + MAX_CNT_SIZE = 1000 + stats = {ALL_DOCUMENTS: 0} + needed = columns + [weights] if columns else None + + for doc in read_jsons(source): + stats[ALL_DOCUMENTS] += 1 + for k, v in doc.items(): + if needed and k not in needed: + continue + stats[k] = get_or_set(stats, k, 0) + 1 + if isinstance(v, str): + stats[k + ".length"] = get_or_set(stats, k + ".length", 0) + len(v) + if len(v) > MAX_LABEL_LEN: # Don't treat too long string as labels + continue + cnt = get_or_set(stats, k + ".cnt", collections.defaultdict(int)) + if v in cnt or len(cnt) < MAX_CNT_SIZE: + cnt[v] += 1 + elif type(v) in (int, float): + values = get_or_set(stats, k + ".val", []) + if len(values) < MAX_HIST_SIZE: + values.append(v) + elif type(v) is list and len(v) and type(v[0]) in (int, float): + values = get_or_set(stats, k + ".val", []) + if len(values) < MAX_HIST_SIZE: + values += v + elif type(v) is dict: + cnt = get_or_set(stats, k + ".cnt", collections.defaultdict(int)) + for label in v: + if label in cnt or len(cnt) < MAX_CNT_SIZE: + cnt[label] += 1 + + documents = stats[ALL_DOCUMENTS] + yield f"Stats computed on {documents} documents:" + for k in stats: + if columns and k not in columns: + continue + if "." in k or k == ALL_DOCUMENTS: + continue + for line in display_stats(stats, k, weights=weights, **kwargs): + yield line + + +def shard(lines): + """Shard a file in several smaller ones.""" + # The creation of the shard is handle in a generic way. Do we need this ? + return lines + + +# *** Utils *** + + +def get_or_set(dictionary, key, default): + if key not in dictionary: + dictionary[key] = default + return dictionary[key] + + +class SimpleIO(Protocol): + """A subset of methods from TextIO.""" + + def close(self) -> None: + ... + + def write(self, line: str) -> int: + ... + + def __enter__(self) -> "SimpleIO": + ... + + def __exit__(self, exc_type, exc_value, traceback): + ... + + +def open_read(filename: ReadableFileLike) -> Iterable[str]: + """Open the given file, list of files or files matching the given glob and read lines. + + `filename` is None or "-" -> reads from stdin + `filename` is a Path / str -> interprets filename as a glob and open files matching it + `filename` is a list -> opens sequentially all files from the list using `open_read` + `filename` is something else -> returns the object wrapped in a `nullcontext` + This allows to pass already openened files or iterables. + + `open_read` will decompress gzip files, given they have ".gz" suffix. + """ + if filename is None: + return sys.stdin + + if isinstance(filename, list): + assert isinstance(filename[0], Path) + if len(filename) == 0: + return [] + if len(filename) > 1: + return _yield_from(filename) + filename = tp.cast(Path, filename[0]) + if isinstance(filename, str): + if filename.startswith("http://") or filename.startswith("https://"): + return open_remote_file(filename) + + filename = Path(filename) + if not isinstance(filename, Path): + # we might have received an iterable, return it unmodified. + return filename # type: ignore + + # Expand glob patterns only when reading + files = [Path(f) for f in sorted(glob.glob(str(filename)))] + if len(files) > 1: + return _yield_from(files) + if len(files) == 1: + filename = files[0] + + assert isinstance(filename, Path) + + if filename.name.endswith("]"): + return block_reader(filename) + + logging.getLogger(__name__).info(f"Opening {filename} with mode 'rt'") + if filename.suffix == ".gz": + file: TextIO = gzip.open(filename, "rt") # type: ignore + else: + file = open(filename, "rt") + + return _close_when_exhausted(file) + + +def _close_when_exhausted(file: TextIO) -> Iterable[str]: + with file: + yield from file + + +def _yield_from(files: list) -> Iterable[str]: + for file in files: + yield from open_read(file) + + +def open_write( + filename: WritableFileLike, max_size: str = "4G" +) -> tp.ContextManager[TextIO]: + """Open the given file, list of files or files matching the given glob. + + The return value is a ContextManager meant to be used inside a `with` block: + ``` + with open_write("foo.txt") as o: + ... + + Write mode: + replaces "?" from filename by numbers ranging from 0 to 9, generatings files of size `max_size`. + If filename ends with ".gz", creates a blocked gzip file with random access. + """ + if filename is None: + return contextlib.nullcontext(sys.stdout) + + if isinstance(filename, list): + if len(filename) > 1: + return MultiFile(filename, "w", max_size) + else: + filename = tp.cast(Path, filename[0]) + if isinstance(filename, str): + filename = Path(filename) + if not isinstance(filename, Path): + assert hasattr(filename, "write"), f"{filename} doesn't have a .write method." + # We return a 'TextIO' even though we only check for `.write` method, + # this works better with eg `print`. + return contextlib.nullcontext(tp.cast(TextIO, filename)) + + mode = "wt" + if "?" in filename.name: + return sharded_file(filename, mode, max_size) + + logging.getLogger(__name__).info(f"Opening {filename} with mode {mode}") + # TODO: should we use another format ? + if filename.suffix == ".gz": + return BlockedGzipWriter(Path(filename), mode, block_size="64M") + + return open(filename, "wt") + + +def parse_size(size): + unit_map = {"B": 1, "K": 1024, "M": 1024 ** 2, "G": 1024 ** 3} + unit = size[-1].upper() + assert ( + unit in unit_map + ), f"Unsupported size unit for {size}. Use one of: {unit_map.keys()}." + return int(size[:-1]) * unit_map[unit] + + +class MultiFile(SimpleIO): + def __init__(self, files: Iterable[Path], mode="w", max_size="4G"): + self.name = str(files) + self.mode = mode + self.files = iter(files) + self.max_size = parse_size(max_size) + self.current_handle: Optional[TextIO] = None + self.current_block_size = 0 + self._open_next_handle() # Opening 1st handle allows to write directly. + + def write(self, content) -> int: + # Avoid splitting newlines to a new file. + # use current_block_size since it's faster than `tell()` + if content != "\n" and self.current_block_size >= self.max_size: + self._open_next_handle() + if self.current_handle is None: + raise Exception("No more files to write to...") + + written = self.current_handle.write(content) + self.current_block_size += written + return written + + def _open_next_handle(self) -> bool: + self.close() + file = next(self.files, None) + if file is None: + return False + + self.current_handle = open_write(file).__enter__() + self.current_block_size = 0 + return True + + def __enter__(self): + return self + + def __exit__(self, *exc_info): + self.close() + + @property + def closed(self): + return self.current_handle is None + + def close(self): + if self.current_handle is None: + return + + # log("Closing", self.current_handle.name, "with mode", self.current_handle.mode) + self.current_handle.__exit__(None, None, None) + self.current_handle = None + + +# not sure it helps since connections are reseted anyway. +_session = functools.lru_cache()(requests.Session) + + +def request_get_content(url: str, n_retry: int = 3) -> bytes: + """Retrieve the binary content at url. + + Retry on connection errors. + """ + t0 = time.time() + logging.info(f"Starting download of {url}") + for i in range(1, n_retry + 1): + try: + r = _session().get(url) + r.raise_for_status() + break + except requests.exceptions.RequestException as e: + # Sleep and try again on error, unless it's a 404. + message = e.args[0] if isinstance(e.args[0], str) else "" + if i == n_retry or "Client Error" in message: + raise e + warnings.warn( + f"Swallowed error {e} while downloading {url} ({i} out of {n_retry})" + ) + time.sleep(10 * 2 ** i) + dl_time = time.time() - t0 + dl_speed = len(r.content) / dl_time / 1024 + logging.info( + f"Downloaded {url} [{r.status_code}] took {dl_time:.0f}s ({dl_speed:.1f}kB/s)" + ) + return r.content + + +def open_remote_file(url: str, cache: Path = None) -> Iterable[str]: + """Download the files at the given url to memory and opens it as a file. + Assumes that the file is small, and fetch it when this function is called. + """ + if cache and cache.exists(): + return open_read(cache) + + # TODO: open the remote file in streaming mode. + # The hard part is that we need to write the content on disk at the same time, + # to implement disk caching. + raw_bytes = request_get_content(url) + content = io.BytesIO(raw_bytes) + if url.endswith(".gz"): + f: TextIO = gzip.open(content, mode="rt") # type: ignore + else: + f = io.TextIOWrapper(content) + + if cache and not cache.exists(): + # The file might have been created while downloading/writing. + tmp_cache = _tmp(cache) + tmp_cache.write_bytes(raw_bytes) + if not cache.exists(): + tmp_cache.replace(cache) + else: + tmp_cache.unlink() + + return _close_when_exhausted(f) + + +def sharded_file(file_pattern: Path, mode: str, max_size: str = "4G") -> MultiFile: + folder, name = file_pattern.parent, file_pattern.name + assert "?" in name, f"Can't expand give file_pattern: {file_pattern}" + + n = name.count("?") + assert 0 < n < 8 + assert "?" * n in name, f"The '?' need to be adjacents in {file_pattern}" + assert "r" not in mode + files = (folder / name.replace("?" * n, f"%0{n}d" % i) for i in range(10 ** n)) + + return MultiFile(files, mode, max_size) + + +class SplitFile: + def __init__(self, filename: Path, chunk: int, n_chunks: int, mode: str = "r"): + assert mode == "r" + size = os.path.getsize(filename) + self.handle = open(filename, mode) + start = chunk * size // n_chunks + self.end: int = (chunk + 1) * size // n_chunks + + if start > 0: + self.handle.seek(start - 1) + # Skip incomplete line. This avoid crashing when reading eg the middle + # of a unicode char. `self.handle.buffer` is a binary file reader. + self.handle.buffer.readline() # type: ignore + + def __enter__(self): + return self + + def __iter__(self): + while True: + line = self.handle.readline() + if not line: + return + + yield line + if self.handle.tell() >= self.end: + return + + def readlines(self): + return list(self.__iter__()) + + def close(self): + self.handle.close() + + def __exit__(self, *args): + self.close() + + +def get_block_readers(filename: Path, n_readers, mode="t"): + index_filename = filename.parent / (filename.name + ".index") + if not index_filename.exists(): + return [gzip.open(filename, "r" + mode)] + index: List[int] = np.load(index_filename) + n_chunks = len(index) + chunk_per_reader = int(np.ceil(n_chunks / n_readers)) + n_readers = int(np.ceil(n_chunks / chunk_per_reader)) + + start = 0 + readers = [] + for i in range(n_readers): + end = index[min((i + 1) * chunk_per_reader - 1, n_chunks - 1)] + r = _blocked_gzip_reader(filename, start, end, mode) + readers.append(r) + start = end + return readers + + +def block_reader(filename: Path) -> Iterable[str]: + root, pattern = str(filename)[:-1].split("[", 1) + assert root.endswith(".gz"), "Can only read block of a .gz file for now." + + ii, nn = pattern.strip().split("/") + i, n_readers = int(ii), int(nn) + + index_filename = root + ".index" + assert os.path.exists( + index_filename + ), f"Index {index_filename} not found for {filename}" + index: List[int] = np.load(index_filename) + n_chunks = len(index) + chunk_per_reader = int(np.ceil(n_chunks / n_readers)) + n_readers = int(np.ceil(n_chunks / chunk_per_reader)) + # I'm not sure how to handle the case where there is less reader than expected. + # Currently we return empty readers. + + start = 0 + if i > 0: + start = index[min((i - 1) * chunk_per_reader, n_chunks - 1)] + end = index[min(i * chunk_per_reader, n_chunks - 1)] + return _blocked_gzip_reader(root, start, end, mode="t") + + +def _blocked_gzip_reader(filename, start, end, mode="t") -> Iterable[str]: + handle = gzip.open(filename, "r" + mode) + handle.seek(start) + try: + while handle.tell() < end: + line = handle.readline() + if not line: + break + yield line + finally: + handle.close() + + +class BlockedGzipWriter(MultiFile): + """Writes a Gzip files which can be read by block. + + Decreasing the block size may hurt compression, but provides more split points. + """ + + def __init__(self, filename: Path, mode: str, block_size: str = "256M"): + assert "w" in mode + self.filename = Path(filename) + self.index: List[int] = [] + self.zipfile: Optional[gzip.GzipFile] = None + super().__init__([], mode, block_size) + + def _open_next_handle(self) -> bool: + """Here we never actually close/open handles, + we just write the end of block sequence.""" + if not self.current_handle: + mode = self.mode + "t" + self.current_handle = tp.cast(TextIO, gzip.open(self.filename, mode)) + assert isinstance(self.current_handle.buffer, gzip.GzipFile) + self.zipfile = self.current_handle.buffer + return True + + # Use Z_FULL_FLUSH to allow random access: + # https://github.com/madler/zlib/blob/cacf7f1d4e3d44d871b605da3b647f07d718623f/zlib.h#L313 + self.current_handle.buffer.flush(zlib_mode=zlib.Z_FULL_FLUSH) # type: ignore + self.index.append(self.current_handle.tell()) + self.current_block_size = 0 + return True + + def flush(self): + assert self.current_handle is not None + self.current_handle.flush() + + def close(self): + if self.current_handle is None: + return + self.current_handle.flush() + self.index.append(self.current_handle.tell()) + self.current_handle.close() + self.current_handle = None + index = np.array(self.index, dtype=np.uint64) + with open(str(self.filename) + ".index", "wb") as o: + np.save(o, index) + + +def grouper(iterable, n): + group = [] + for x in iterable: + group.append(x) + if len(group) == n: + yield group + group = [] + if group: + yield group + + +PROCESS = psutil.Process() + + +def mem_footprint_gb(pid=None): + rss = PROCESS.memory_info().rss + return rss / 1_000_000_000 + + +def _tmp(output: Path) -> Path: + suffix = "".join(output.suffixes) + suffix = ".tmp" + suffix + prefix = output.name[: -len(suffix)] + _, tmp_path = tempfile.mkstemp(dir=output.parent, prefix=prefix, suffix=suffix) + return Path(tmp_path) + + +@functools.lru_cache() +def _tmp_dir() -> Path: + job_id = os.environ.get("SLURM_JOB_ID") + if job_id: + return Path("/scratch/slurm_tmpdir") / job_id + + checkpoint = Path("/checkpoint") / os.environ.get("USER", "") + if checkpoint.exists(): + tmp = checkpoint / "tmp" + tmp.mkdir(exist_ok=True) + return tmp + + return Path("/tmp") + + +if __name__ == "__main__": + multiprocessing.set_start_method("fork") + main(sys.argv[1:]) diff --git a/language_identification/readMe.md b/language_identification/readMe.md new file mode 100644 index 0000000..e69de29 diff --git a/language_identification/split_by_lang.py b/language_identification/split_by_lang.py new file mode 100644 index 0000000..fa70b64 --- /dev/null +++ b/language_identification/split_by_lang.py @@ -0,0 +1,122 @@ +from pathlib import Path +import fasttext # type: ignore +from typing import Dict, Optional + +from language_identification import jsonql + + +def predict(model, text: str, k: int = 1): + labels, scores = model.predict(text, k=k) + labels = [l.replace("__label__", "") for l in labels] + return labels, scores + + +class Classifier(jsonql.Transformer): + def __init__( + self, + model: Path, + field: str, + out_field: str, + threshold: float = 0, + top: int = 1, + language: str = None, + rounding: int = 2, + ): + super().__init__() + self.model = model + assert model.exists(), f"Model {model} doesn't exist." + self.field = field + self.out_field = out_field + self.threshold = threshold + self.top = top + self.language = language + self.rounding = rounding + # Fasttext model is a C object and can't be pickled + self.fasttext_model: fasttext._FastText = None + self.n_doc, self.n_accepted, self.n_ignored, self.n_disagreement = 0, 0, 0, 0 + self.cnt: Dict[str, int] = {} + + def _prepare(self): + self.log(f"Loading {self.model}") + self.fasttext_model = fasttext.load_model(str(self.model)) + + def predict(self, text): + return predict(self.fasttext_model, text.replace("\n", ""), k=self.top) + + def do(self, doc: dict) -> Optional[dict]: + text = doc.get(self.field, None) + if not text: + return None + + if self.language and doc.get("language") != self.language: + self.n_ignored += 1 + return doc + + self.n_doc += 1 + labels, scores = self.predict(text) + scores.round(self.rounding, out=scores) + for l in labels: + self.cnt[l] = self.cnt.get(l, 0) + 1 + + if self.top == 1: + existing_label = doc.get(self.out_field, None) + if existing_label and labels[0] != existing_label: + self.n_disagreement += 1 + + if all(s < self.threshold for s in scores): + return None + + self.n_accepted += 1 + if self.top == 1: + doc[self.out_field] = labels[0] + doc[self.out_field + "_score"] = scores[0] + else: + doc[self.out_field] = {l: s for l, s in zip(labels, scores)} + return doc + + def summary(self): + n_doc, n_accepted, n_disagreement, cnt, out_field = ( + self.n_doc, + self.n_accepted, + self.n_disagreement, + self.cnt, + self.out_field, + ) + summ = super().summary() + if self.threshold > 0: + ratio = n_accepted / n_doc if n_doc else 0 + summ.append(f"Kept {n_accepted} docs over {n_doc} ({ratio :.1%})") + summ.append(f"Found {len(cnt)} {out_field} labels: {cnt}") + + disagreement = n_disagreement / n_doc if n_doc else 0 + if disagreement: + summ.append(f"{out_field} disagreement is at {disagreement:.1%}.") + return summ + + def __repr__(self): + return f"Classifier({self.model})" + + +if __name__ == "__main__": + model = Path("bin/lid.bin") + + classifier = Classifier(model, "text", "lang") + classifier.__enter__() + doc_zh = dict(text="面向大模型研究领域的高效易用数据处理工貝包") + doc_en = dict(text="Efficient and Easy-to-Use Data Processing Workbags for Large Modeling Research Domain") + doc_ru = dict(text="Эффективные и простые в использовании рабочие пакеты для обработки данных в области исследования больших моделей") + doc_fr = dict(text="Sacs de travail efficaces et faciles à utiliser pour le traitement des données dans le domaine de la recherche sur les grands modèles") + doc_de = dict(text="Effiziente und einfach zu verwendende Datenverarbeitungs-Workbags für große Modellforschungsbereiche") + doc_ja = dict(text="大規模モデル研究領域のための効率的で使いやすいデータ処理ワークバッグ") + results_zh = classifier(doc_zh) + results_en = classifier(doc_en) + results_ru = classifier(doc_ru) + results_fr = classifier(doc_fr) + results_de = classifier(doc_de) + results_ja = classifier(doc_ja) + print(f"language type: {results_zh.get('lang')}, language source:{results_zh.get('lang_score')} , original text: {results_zh.get('text')} .") + print(f"language type: {results_en.get('lang')}, language source:{results_en.get('lang_score')} , original text: {results_en.get('text')} .") + print(f"language type: {results_ru.get('lang')}, language source:{results_ru.get('lang_score')} , original text: {results_ru.get('text')} .") + print(f"language type: {results_fr.get('lang')}, language source:{results_fr.get('lang_score')} , original text: {results_fr.get('text')} .") + print(f"language type: {results_de.get('lang')}, language source:{results_de.get('lang_score')} , original text: {results_de.get('text')} .") + print(f"language type: {results_ja.get('lang')}, language source:{results_ja.get('lang_score')} , original text: {results_ja.get('text')} .") diff --git a/requirements.txt b/requirements.txt index 91c11d8..5709a52 100644 --- a/requirements.txt +++ b/requirements.txt @@ -8,4 +8,7 @@ transformers==4.20.1 loguru>=0.6.0 numpy>=1.21.5 pandas>=1.3.5 -nltk>=3.7 \ No newline at end of file +nltk>=3.7 +fasttext>=0.9.1 +psutil>=5.6.3 +typing_extensions \ No newline at end of file From 575264dc88919d223800f66d2d052827192df699 Mon Sep 17 00:00:00 2001 From: wuchengwei Date: Fri, 8 Dec 2023 20:34:11 +0800 Subject: [PATCH 02/38] add new function --- all2txt/outputs1.json | 14074 +++++++++++++++++++++++++++++ all2txt/pdf2txt.py | 92 + gpt4_data_gen/README.md | 11 + gpt4_data_gen/example.py | 51 + gpt4_data_gen/prompt_template.py | 55 + gpt4_data_gen/strategy.py | 174 + gpt4_data_gen/utils.py | 30 + 7 files changed, 14487 insertions(+) create mode 100644 all2txt/outputs1.json create mode 100644 all2txt/pdf2txt.py create mode 100644 gpt4_data_gen/README.md create mode 100644 gpt4_data_gen/example.py create mode 100644 gpt4_data_gen/prompt_template.py create mode 100644 gpt4_data_gen/strategy.py create mode 100644 gpt4_data_gen/utils.py diff --git a/all2txt/outputs1.json b/all2txt/outputs1.json new file mode 100644 index 0000000..e78570d --- /dev/null +++ b/all2txt/outputs1.json @@ -0,0 +1,14074 @@ +[ + { + "element_id": "bf952612b8f83c3ffa49ce97e0f9ddfa", + "metadata": { + 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+ "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 1 + }, + "text": "\u2020University of Washington", + "type": "Title" + }, + { + "element_id": "9aeac2fae108ceb3fd175c44cf60d896", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 370.1, + 132.9 + ], + [ + 370.1, + 146.2 + ], + [ + 473.2, + 146.2 + ], + [ + 473.2, + 132.9 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 1 + }, + "text": "\u2021Princeton University", + "type": "Title" + }, + { + "element_id": "0b97b34003d9f698993202539c813abc", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 97.0, + 147.4 + ], + [ + 97.0, + 187.2 + ], + [ + 503.5, 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"languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 1 + }, + "text": "Abstract", + "type": "Title" + }, + { + "element_id": "40fd09620fd4ef591cfff19f2339b21e", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 88.6, + 245.7 + ], + [ + 88.6, + 446.9 + ], + [ + 274.9, + 446.9 + ], + [ + 274.9, + 245.7 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 711, + "text": ".", + "url": "Hfootnote.2" + } + ], + "page_number": 1, + "parent_id": "d21b4a64a2d8656a0fdf7ab2e89a4916" + }, + "text": "Open-domain question answering relies on ef- \ufb01cient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented us- ing dense representations alone, where em- beddings are learned from a small number of questions and passages by a simple dual- encoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene- BM25 system greatly by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks.1", + "type": "NarrativeText" + }, + { + "element_id": "6b86b273ff34fce19d6b804eff5a3f57", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 457.2 + ], + [ + 72.0, + 469.2 + ], + [ + 78.0, + 469.2 + ], + [ + 78.0, + 457.2 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 1, + "parent_id": "d21b4a64a2d8656a0fdf7ab2e89a4916" + }, + "text": "1", + "type": "UncategorizedText" + }, + { + "element_id": "b605350bc00209520b7cd8f546322663", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 89.9, + 457.2 + ], + [ + 89.9, + 469.2 + ], + [ + 154.8, + 469.2 + ], + [ + 154.8, + 457.2 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 1 + }, + "text": "Introduction", + "type": "Title" + }, + { + "element_id": "c64f44389bb8fe5071732a8740943dd4", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 71.2, + 478.7 + ], + [ + 71.2, + 706.4 + ], + [ + 292.1, + 706.4 + ], + [ + 292.1, + 478.7 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 37, + "text": "Voorhees", + "url": "cite.voorhees1999trec" + }, + { + "start_index": 47, + "text": "1999", + "url": "cite.voorhees1999trec" + }, + { + "start_index": 216, + "text": "Ferrucci", + "url": "cite.ferrucci2012introduction" + }, + { + "start_index": 226, + "text": "2012", + "url": "cite.ferrucci2012introduction" + }, + { + "start_index": 230, + "text": ");", + "url": "cite.moldovan2003performance" + }, + { + "start_index": 242, + "text": "etal", + "url": "cite.moldovan2003performance" + }, + { + "start_index": 250, + "text": "2003", + "url": "cite.moldovan2003performance" + }, + { + "start_index": 591, + "text": "Chenetal .,", + "url": "cite.chen2017reading" + }, + { + "start_index": 604, + "text": "2017", + "url": "cite.chen2017reading" + }, + { + "start_index": 757, + "text": ",", + "url": "Hfootnote.3" + } + ], + "page_number": 1, + "parent_id": "b605350bc00209520b7cd8f546322663" + }, + "text": "Open-domain question answering (QA) (Voorhees, 1999) is a task that answers factoid questions us- ing a large collection of documents. While early QA systems are often complicated and consist of multiple components (Ferrucci (2012); Moldovan et al. (2003), inter alia), the advances of reading comprehension models suggest a much simpli\ufb01ed two-stage framework: (1) a context retriever \ufb01rst selects a small subset of passages where some of them contain the answer to the question, and then (2) a machine reader can thoroughly exam- ine the retrieved contexts and identify the correct answer (Chen et al., 2017). Although reducing open-domain QA to machine reading is a very rea- sonable strategy, a huge performance degradation is often observed in practice2, indicating the needs of improving retrieval.", + "type": "NarrativeText" + }, + { + "element_id": "77616626377536e9398824b1b38effcb", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 84.7, + 712.7 + ], + [ + 84.7, + 734.1 + ], + [ + 290.3, + 734.1 + ], + [ + 290.3, + 712.7 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 1, + "parent_id": "b605350bc00209520b7cd8f546322663" + }, + "text": "\u2217Equal contribution 1The code and trained models have been released at", + "type": "NarrativeText" + }, + { + "element_id": "3ae8896a0aad690779c3404686696b43", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 735.1 + ], + [ + 72.0, + 744.1 + ], + [ + 224.5, + 744.1 + ], + [ + 224.5, + 735.1 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 1 + }, + "text": "https://github.com/facebookresearch/DPR.", + "type": "Title" + }, + { + "element_id": "72b6d39b162b30069b5784a826ffe271", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 84.7, + 744.5 + ], + [ + 84.7, + 754.9 + ], + [ + 290.3, + 754.9 + ], + [ + 290.3, + 744.5 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 1 + }, + "text": "2For instance, the exact match score on SQuAD v1.1 drops", + "type": "Title" + }, + { + "element_id": "f011683bf9057985e43cf3cd5e786014", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 303.0, + 224.9 + ], + [ + 303.0, + 574.6 + ], + [ + 527.4, + 574.6 + ], + [ + 527.4, + 224.9 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 74, + "text": "( Zaragoza , 2009 ), whichmatcheskeywordsef\ufb01", + "url": "cite.robertson2009probabilistic" + }, + { + "start_index": 89, + "text": "Zaragoza", + "url": "cite.robertson2009probabilistic" + }, + { + "start_index": 99, + "text": "2009", + "url": "cite.robertson2009probabilistic" + }, + { + "start_index": 1210, + "text": "ShrivastavaandLi", + "url": "cite.NIPS2014_5329" + }, + { + "start_index": 1230, + "text": "2014", + "url": "cite.NIPS2014_5329" + }, + { + "start_index": 1237, + "text": "Guoetal", + "url": "cite.guo2016quantization" + }, + { + "start_index": 1249, + "text": "2016", + "url": "cite.guo2016quantization" + } + ], + "page_number": 1, + "parent_id": "72b6d39b162b30069b5784a826ffe271" + }, + "text": "Retrieval in open-domain QA is usually imple- mented using TF-IDF or BM25 (Robertson and Zaragoza, 2009), which matches keywords ef\ufb01- ciently with an inverted index and can be seen as representing the question and context in high- dimensional, sparse vectors (with weighting). Con- versely, the dense, latent semantic encoding is com- plementary to sparse representations by design. For example, synonyms or paraphrases that consist of completely different tokens may still be mapped to vectors close to each other. Consider the question \u201cWho is the bad guy in lord of the rings?\u201d, which can be answered from the context \u201cSala Baker is best known for portraying the villain Sauron in the Lord of the Rings trilogy.\u201d A term-based system would have dif\ufb01culty retrieving such a context, while a dense retrieval system would be able to better match \u201cbad guy\u201d with \u201cvillain\u201d and fetch the cor- rect context. Dense encodings are also learnable by adjusting the embedding functions, which pro- vides additional \ufb02exibility to have a task-speci\ufb01c representation. With special in-memory data struc- tures and indexing schemes, retrieval can be done ef\ufb01ciently using maximum inner product search (MIPS) algorithms (e.g., Shrivastava and Li (2014); Guo et al. (2016)).", + "type": "NarrativeText" + }, + { + "element_id": "a3892a174dd8dceadf65495b936bf1e7", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 306.9, + 578.3 + ], + [ + 306.9, + 765.3 + ], + [ + 527.5, + 765.3 + ], + [ + 527.5, + 578.3 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 259, + "text": "Leeetal .,", + "url": "cite.lee2019latent" + }, + { + "start_index": 271, + "text": "2019", + "url": "cite.lee2019latent" + } + ], + "page_number": 1, + "parent_id": "72b6d39b162b30069b5784a826ffe271" + }, + "text": "However, it is generally believed that learn- ing a good dense vector representation needs a large number of labeled pairs of question and con- texts. Dense retrieval methods have thus never be shown to outperform TF-IDF/BM25 for open- domain QA before ORQA (Lee et al., 2019), which proposes a sophisticated inverse cloze task (ICT) objective, predicting the blocks that contain the masked sentence, for additional pretraining. The question encoder and the reader model are then \ufb01ne- tuned using pairs of questions and answers jointly. Although ORQA successfully demonstrates that dense retrieval can outperform BM25, setting new state-of-the-art results on multiple open-domain", + "type": "NarrativeText" + }, + { + "element_id": "6fca521f401a70e685cdee3b7f122df7", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 755.9 + ], + [ + 72.0, + 764.9 + ], + [ + 267.8, + 764.9 + ], + [ + 267.8, + 755.9 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 1 + }, + "text": "from above 80% to less than 40% (Yang et al., 2019a).", + "type": "Title" + }, + { + "element_id": "4ffbfe02a6da5ac2ffa586205e4603c9", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 65.2 + ], + [ + 72.0, + 171.0 + ], + [ + 292.2, + 171.0 + ], + [ + 292.2, + 65.2 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 2, + "parent_id": "6fca521f401a70e685cdee3b7f122df7" + }, + "text": "QA datasets, it also suffers from two weaknesses. First, ICT pretraining is computationally intensive and it is not completely clear that regular sentences are good surrogates of questions in the objective function. Second, because the context encoder is not \ufb01ne-tuned using pairs of questions and answers, the corresponding representations could be subop- timal.", + "type": "NarrativeText" + }, + { + "element_id": "3bed499e55636392d074ef00589538d0", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 71.6, + 174.6 + ], + [ + 71.6, + 647.1 + ], + [ + 292.2, + 647.1 + ], + [ + 292.2, + 174.6 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 229, + "text": "Devlinetal", + "url": "cite.devlin2018bert" + }, + { + "start_index": 244, + "text": "2019", + "url": "cite.devlin2018bert" + }, + { + "start_index": 282, + "text": "(", + "url": "cite.bromley1994signature" + }, + { + "start_index": 291, + "text": "etal .,", + "url": "cite.bromley1994signature" + }, + { + "start_index": 299, + "text": "1994", + "url": "cite.bromley1994signature" + }, + { + "start_index": 982, + "text": "Leeetal .,", + "url": "cite.lee2019latent" + }, + { + "start_index": 994, + "text": "2019", + "url": "cite.lee2019latent" + }, + { + "start_index": 1000, + "text": "Kwiatkowskietal .,", + "url": "cite.kwiatkowski2019natural" + }, + { + "start_index": 1020, + "text": "2019", + "url": "cite.kwiatkowski2019natural" + } + ], + "page_number": 2, + "parent_id": "6fca521f401a70e685cdee3b7f122df7" + }, + "text": "In this paper, we address the question: can we train a better dense embedding model using only pairs of questions and passages (or answers), with- out additional pretraining? By leveraging the now standard BERT pretrained model (Devlin et al., 2019) and a dual-encoder architecture (Bromley et al., 1994), we focus on developing the right training scheme using a relatively small number of question and passage pairs. Through a series of careful ablation studies, our \ufb01nal solution is surprisingly simple: the embedding is optimized for maximizing inner products of the question and relevant passage vectors, with an objective compar- ing all pairs of questions and passages in a batch. Our Dense Passage Retriever (DPR) is exception- ally strong. It not only outperforms BM25 by a large margin (65.2% vs. 42.9% in Top-5 accuracy), but also results in a substantial improvement on the end-to-end QA accuracy compared to ORQA (41.5% vs. 33.3%) in the open Natural Questions setting (Lee et al., 2019; Kwiatkowski et al., 2019). Our contributions are twofold. First, we demon- strate that with the proper training setup, sim- ply \ufb01ne-tuning the question and passage encoders on existing question-passage pairs is suf\ufb01cient to greatly outperform BM25. Our empirical results also suggest that additional pretraining may not be needed. Second, we verify that, in the context of open-domain question answering, a higher retrieval precision indeed translates to a higher end-to-end QA accuracy. By applying a modern reader model to the top retrieved passages, we achieve compara- ble or better results on multiple QA datasets in the open-retrieval setting, compared to several, much complicated systems.", + "type": "NarrativeText" + }, + { + "element_id": "b9cecd7cdf8e46c955bd793d1c2118df", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 662.4 + ], + [ + 72.0, + 674.4 + ], + [ + 152.8, + 674.4 + ], + [ + 152.8, + 662.4 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 2 + }, + "text": "2 Background", + "type": "Title" + }, + { + "element_id": "b5205fe2d106125396334bae254a782a", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 71.6, + 686.7 + ], + [ + 71.6, + 765.3 + ], + [ + 290.3, + 765.3 + ], + [ + 290.3, + 686.7 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 2, + "parent_id": "b9cecd7cdf8e46c955bd793d1c2118df" + }, + "text": "The problem of open-domain QA studied in this paper can be described as follows. Given a factoid question, such as \u201cWho \ufb01rst voiced Meg on Family Guy?\u201d or \u201cWhere was the 8th Dalai Lama born?\u201d, a system is required to answer it using a large corpus of diversi\ufb01ed topics. More speci\ufb01cally, we assume", + "type": "NarrativeText" + }, + { + "element_id": "9992ab80b044f68cf51f36e3de7b6e10", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 306.9, + 65.2 + ], + [ + 306.9, + 421.5 + ], + [ + 527.5, + 421.5 + ], + [ + 527.5, + 65.2 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 341, + "text": "p2", + "url": "Hfootnote.4" + }, + { + "start_index": 936, + "text": "Chenetal .,", + "url": "cite.chen2017reading" + }, + { + "start_index": 949, + "text": "2017", + "url": "cite.chen2017reading" + }, + { + "start_index": 953, + "text": ").", + "url": "Hfootnote.5" + } + ], + "page_number": 2, + "parent_id": "b9cecd7cdf8e46c955bd793d1c2118df" + }, + "text": "the extractive QA setting, in which the answer is restricted to a span appearing in one or more pas- sages in the corpus. Assume that our collection contains D documents, d1, d2, \u00b7 \u00b7 \u00b7 , dD. We \ufb01rst split each of the documents into text passages of equal lengths as the basic retrieval units3 and get M total passages in our corpus C = {p1, p2, . . . , pM }, where each passage pi can be viewed as a sequence 2 , \u00b7 \u00b7 \u00b7 , w(i) 1 , w(i) of tokens w(i) |pi|. Given a question q, the task is to \ufb01nd a span w(i) s+1, \u00b7 \u00b7 \u00b7 , w(i) s , w(i) from one of the passages pi that can answer the question. Notice that to cover a wide variety of domains, the corpus size can easily range from millions of docu- ments (e.g., Wikipedia) to billions (e.g., the Web). As a result, any open-domain QA system needs to include an ef\ufb01cient retriever component that can se- lect a small set of relevant texts, before applying the reader to extract the answer (Chen et al., 2017).4 Formally speaking, a retriever R : (q, C) \u2192 CF is a function that takes as input a question q and a corpus C and returns a much smaller \ufb01lter set of texts CF \u2282 C, where |CF | = k (cid:28) |C|. For a \ufb01xed k, a retriever can be evaluated in isolation on top-k retrieval accuracy, which is the fraction of ques- tions for which CF contains a span that answers the question.", + "type": "NarrativeText" + }, + { + "element_id": "3f79bb7b435b05321651daefd374cdc6", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 491.9, + 197.4 + ], + [ + 491.9, + 205.3 + ], + [ + 495.8, + 205.3 + ], + [ + 495.8, + 197.4 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 2 + }, + "text": "e", + "type": "Title" + }, + { + "element_id": "09b350258cc8e8c2bbc4d2164016b892", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 307.3, + 434.2 + ], + [ + 307.3, + 446.2 + ], + [ + 485.1, + 446.2 + ], + [ + 485.1, + 434.2 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 2 + }, + "text": "3 Dense Passage Retriever (DPR)", + "type": "Title" + }, + { + "element_id": "205fbcb8766db03aca41834da8bdfe53", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 306.8, + 456.5 + ], + [ + 306.8, + 602.9 + ], + [ + 527.5, + 602.9 + ], + [ + 527.5, + 456.5 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 466, + "text": ". 1", + "url": "subsection.4.1" + } + ], + "page_number": 2, + "parent_id": "09b350258cc8e8c2bbc4d2164016b892" + }, + "text": "We focus our research in this work on improv- ing the retrieval component in open-domain QA. Given a collection of M text passages, the goal of our dense passage retriever (DPR) is to index all the passages in a low-dimensional and continuous space, such that it can retrieve ef\ufb01ciently the top k passages relevant to the input question for the reader at run-time. Note that M can be very large (e.g., 21 million passages in our experiments, de- scribed in Section 4.1) and k is usually small, such as 20\u2013100.", + "type": "NarrativeText" + }, + { + "element_id": "ed67b4f9e3be54965e9e1d2b8e5fda98", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 307.3, + 615.1 + ], + [ + 307.3, + 626.0 + ], + [ + 376.4, + 626.0 + ], + [ + 376.4, + 615.1 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 2 + }, + "text": "3.1 Overview", + "type": "Title" + }, + { + "element_id": "f14ec690f6e77f6f9314775b93a641db", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 307.3, + 633.0 + ], + [ + 307.3, + 684.6 + ], + [ + 527.5, + 684.6 + ], + [ + 527.5, + 633.0 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 2, + "parent_id": "ed67b4f9e3be54965e9e1d2b8e5fda98" + }, + "text": "Our dense passage retriever (DPR) uses a dense encoder EP (\u00b7) which maps any text passage to a d- dimensional real-valued vectors and builds an index for all the M passages that we will use for retrieval.", + "type": "NarrativeText" + }, + { + "element_id": "fac7e303e228e8ad71c8639b55b6da4e", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 307.0, + 693.8 + ], + [ + 307.0, + 744.1 + ], + [ + 527.0, + 744.1 + ], + [ + 527.0, + 693.8 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 2, + "parent_id": "ed67b4f9e3be54965e9e1d2b8e5fda98" + }, + "text": "3The ideal size and boundary of a text passage are func- tions of both the retriever and reader. We also experimented with natural paragraphs in our preliminary trials and found that using \ufb01xed-length passages performs better in both retrieval and \ufb01nal QA accuracy, as observed by Wang et al. (2019).", + "type": "NarrativeText" + }, + { + "element_id": "ddc41519eb7ad91c8843e3fb24add5a8", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 307.3, + 744.5 + ], + [ + 307.3, + 764.9 + ], + [ + 526.7, + 764.9 + ], + [ + 526.7, + 744.5 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 21, + "text": "Seoetal .,", + "url": "cite.seo2019real" + }, + { + "start_index": 33, + "text": "2019", + "url": "cite.seo2019real" + }, + { + "start_index": 44, + "text": "Robertsetal", + "url": "cite.roberts2020much" + } + ], + "page_number": 2, + "parent_id": "ed67b4f9e3be54965e9e1d2b8e5fda98" + }, + "text": "4Exceptions include (Seo et al., 2019) and (Roberts et al., 2020), which retrieves and generates the answers, respectively.", + "type": "NarrativeText" + }, + { + "element_id": "22f2c00bed24ac090fef5c4b109e71b7", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 71.6, + 65.0 + ], + [ + 71.6, + 143.9 + ], + [ + 291.5, + 143.9 + ], + [ + 291.5, + 65.0 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 3, + "parent_id": "ed67b4f9e3be54965e9e1d2b8e5fda98" + }, + "text": "At run-time, DPR applies a different encoder EQ(\u00b7) that maps the input question to a d-dimensional vector, and retrieves k passages of which vectors are the closest to the question vector. We de\ufb01ne the similarity between the question and the passage using the dot product of their vectors:", + "type": "NarrativeText" + }, + { + "element_id": "3c4a2d2202c0aaec37196c177dd405d5", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 120.6, + 154.0 + ], + [ + 120.6, + 169.7 + ], + [ + 241.7, + 169.7 + ], + [ + 241.7, + 154.0 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 3, + "parent_id": "ed67b4f9e3be54965e9e1d2b8e5fda98" + }, + "text": "sim(q, p) = EQ(q)(cid:124)EP (p).", + "type": "UncategorizedText" + }, + { + "element_id": "a5382d7617cbb8481f61a748a1eb216f", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 71.6, + 182.9 + ], + [ + 71.6, + 437.7 + ], + [ + 292.2, + 437.7 + ], + [ + 292.2, + 182.9 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 674, + "text": "MussmannandErmon", + "url": "cite.mussmann2016learning" + }, + { + "start_index": 694, + "text": "2016", + "url": "cite.mussmann2016learning" + }, + { + "start_index": 700, + "text": "RamandGray", + "url": "cite.ram2012maximum" + }, + { + "start_index": 714, + "text": "2012", + "url": "cite.ram2012maximum" + }, + { + "start_index": 806, + "text": ". 2", + "url": "section*.13" + }, + { + "start_index": 820, + "text": "),", + "url": "appendix.B" + } + ], + "page_number": 3, + "parent_id": "ed67b4f9e3be54965e9e1d2b8e5fda98" + }, + "text": "Although more expressive model forms for measur- ing the similarity between a question and a passage do exist, such as networks consisting of multiple layers of cross attentions, the similarity function needs to be decomposable so that the represen- tations of the collection of passages can be pre- computed. Most decomposable similarity functions are some transformations of Euclidean distance (L2). For instance, cosine is equivalent to inner product for unit vectors and the Mahalanobis dis- tance is equivalent to L2 distance in a transformed space. Inner product search has been widely used and studied, as well as its connection to cosine similarity and L2 distance (Mussmann and Ermon, 2016; Ram and Gray, 2012). As our ablation study \ufb01nds other similarity functions perform compara- bly (Section 5.2; Appendix B), we thus choose the simpler inner product function and improve the dense passage retriever by learning better encoders.", + "type": "NarrativeText" + }, + { + "element_id": "fc0e66fbc43692b8d2cd53aebc7601e6", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 448.2 + ], + [ + 72.0, + 527.0 + ], + [ + 292.1, + 527.0 + ], + [ + 292.1, + 448.2 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 152, + "text": "Devlinetal .,", + "url": "cite.devlin2018bert" + }, + { + "start_index": 167, + "text": "2019", + "url": "cite.devlin2018bert" + } + ], + "page_number": 3, + "parent_id": "ed67b4f9e3be54965e9e1d2b8e5fda98" + }, + "text": "Encoders Although in principle the question and passage encoders can be implemented by any neu- ral networks, in this work we use two independent BERT (Devlin et al., 2019) networks (base, un- cased) and take the representation at the [CLS] token as the output, so d = 768.", + "type": "NarrativeText" + }, + { + "element_id": "e5577179ffda2c6c2756b128045bf7bf", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 71.7, + 537.5 + ], + [ + 71.7, + 657.7 + ], + [ + 292.2, + 657.7 + ], + [ + 292.2, + 537.5 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 113, + "text": "Johnsonetal .,", + "url": "cite.johnson2017billion" + }, + { + "start_index": 129, + "text": "2017", + "url": "cite.johnson2017billion" + } + ], + "page_number": 3, + "parent_id": "ed67b4f9e3be54965e9e1d2b8e5fda98" + }, + "text": "Inference During inference time, we apply the passage encoder EP to all the passages and index them using FAISS (Johnson et al., 2017) of\ufb02ine. FAISS is an extremely ef\ufb01cient, open-source li- brary for similarity search and clustering of dense vectors, which can easily be applied to billions of vectors. Given a question q at run-time, we derive its embedding vq = EQ(q) and retrieve the top k passages with embeddings closest to vq.", + "type": "NarrativeText" + }, + { + "element_id": "22e5bda5d100c1c1defaf10962bbe992", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 668.8 + ], + [ + 72.0, + 679.7 + ], + [ + 137.0, + 679.7 + ], + [ + 137.0, + 668.8 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 3 + }, + "text": "3.2 Training", + "type": "Title" + }, + { + "element_id": "39abf2efb588fac56bdc1e05c881e601", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 71.7, + 686.7 + ], + [ + 71.7, + 765.3 + ], + [ + 292.1, + 765.3 + ], + [ + 292.1, + 686.7 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 65, + "text": "1", + "url": "equation.3.1" + }, + { + "start_index": 159, + "text": "Kulis", + "url": "cite.kulis2013metric" + }, + { + "start_index": 166, + "text": "2013", + "url": "cite.kulis2013metric" + } + ], + "page_number": 3, + "parent_id": "22e5bda5d100c1c1defaf10962bbe992" + }, + "text": "Training the encoders so that the dot-product sim- ilarity (Eq. (1)) becomes a good ranking function for retrieval is essentially a metric learning prob- lem (Kulis, 2013). The goal is to create a vector space such that relevant pairs of questions and pas- sages will have smaller distance (i.e., higher simi-", + "type": "NarrativeText" + }, + { + "element_id": "fd0ad9026eee596b7072a762941f60be", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 277.5, + 158.0 + ], + [ + 277.5, + 168.9 + ], + [ + 290.3, + 168.9 + ], + [ + 290.3, + 158.0 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 3, + "parent_id": "22e5bda5d100c1c1defaf10962bbe992" + }, + "text": "(1)", + "type": "UncategorizedText" + }, + { + "element_id": "59dc5d35508a682e88d90cf55ea6e624", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 307.3, + 65.2 + ], + [ + 307.3, + 104.0 + ], + [ + 525.7, + 104.0 + ], + [ + 525.7, + 65.2 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 3, + "parent_id": "22e5bda5d100c1c1defaf10962bbe992" + }, + "text": "larity) than the irrelevant ones, by learning a better embedding function. Let D = {(cid:104)qi, p+", + "type": "NarrativeText" + }, + { + "element_id": "f17b9817a078291c14e55b7202500999", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 306.9, + 89.9 + ], + [ + 306.9, + 184.5 + ], + [ + 527.4, + 184.5 + ], + [ + 527.4, + 89.9 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 3, + "parent_id": "22e5bda5d100c1c1defaf10962bbe992" + }, + "text": "i , p\u2212 i=1 be the training data that consists of m instances. Each instance contains one question qi and one relevant (positive) passage p+ i , along with n irrelevant (neg- ative) passages p\u2212 i,j. We optimize the loss function as the negative log likelihood of the positive pas- sage:", + "type": "NarrativeText" + }, + { + "element_id": "6d14583d41dfb1867339ba6604a43148", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 415.2, + 89.9 + ], + [ + 415.2, + 105.7 + ], + [ + 463.3, + 105.7 + ], + [ + 463.3, + 89.9 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 3, + "parent_id": "22e5bda5d100c1c1defaf10962bbe992" + }, + "text": "i,1, \u00b7 \u00b7 \u00b7 , p\u2212", + "type": "UncategorizedText" + }, + { + "element_id": "7a61269c5667ad430435dd2d81979866", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 456.7, + 90.5 + ], + [ + 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{ + "element_id": "c8dae2af59c4799d1e39b344aac8ba4d", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 329.5, + 224.6 + ], + [ + 329.5, + 235.5 + ], + [ + 372.2, + 235.5 + ], + [ + 372.2, + 224.6 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 3 + }, + "text": "= \u2212 log", + "type": "Title" + }, + { + "element_id": "32465a610c663202b31d33648f295b5c", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 444.7, + 231.2 + ], + [ + 444.7, + 249.3 + ], + [ + 508.6, + 249.3 + ], + [ + 508.6, + 231.2 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 3 + }, + "text": "j=1 esim(qi,p\u2212 i,j )", + "type": "Title" + }, + { + "element_id": "2ebe028b09bf728e110a69a96356e98a", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 375.3, + 232.3 + ], + [ + 375.3, + 246.7 + ], + [ + 415.6, + 246.7 + ], + [ + 415.6, + 232.3 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 3 + }, + "text": "esim(qi,p+", + "type": "Title" + }, + { + "element_id": "0c078217696e35060ba3ccd86db14528", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 410.0, + 232.0 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"2023-09-04T16:02:28", + "links": [ + { + "start_index": 933, + "text": ". 2", + "url": "section*.10" + } + ], + "page_number": 3, + "parent_id": "2ebe028b09bf728e110a69a96356e98a" + }, + "text": "Positive and negative passages For retrieval problems, it is often the case that positive examples are available explicitly, while negative examples need to be selected from an extremely large pool. For instance, passages relevant to a question may be given in a QA dataset, or can be found using the answer. All other passages in the collection, while not speci\ufb01ed explicitly, can be viewed as irrelevant by default. In practice, how to select negative ex- amples is often overlooked but could be decisive for learning a high-quality encoder. We consider three different types of negatives: (1) Random: any random passage from the corpus; (2) BM25: top passages returned by BM25 which don\u2019t contain the answer but match most question tokens; (3) Gold: positive passages paired with other questions which appear in the training set. We will discuss the impact of different types of negative passages and training schemes in Section 5.2. Our best model uses gold passages from the same mini-batch and one BM25 negative passage. In particular, re-using gold passages from the same batch as negatives can make the computation ef\ufb01cient while achiev- ing great performance. We discuss this approach below.", + "type": "NarrativeText" + }, + { + "element_id": "0d3b6e30f877cab114de963fffd73e70", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 306.0, + 605.1 + ], + [ + 306.0, + 765.3 + ], + [ + 527.5, + 765.3 + ], + [ + 527.5, + 605.1 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 3, + "parent_id": "2ebe028b09bf728e110a69a96356e98a" + }, + "text": "In-batch negatives Assume that we have B questions in a mini-batch and each one is asso- ciated with a relevant passage. Let Q and P be the (B \u00d7d) matrix of question and passage embeddings in a batch of size B. S = QPT is a (B \u00d7 B) ma- trix of similarity scores, where each row of which corresponds to a question, paired with B passages. In this way, we reuse computation and effectively train on B2 (qi, pj) question/passage pairs in each batch. Any (qi, pj) pair is a positive example when i = j, and negative otherwise. This creates B train- ing instances in each batch, where there are B \u2212 1", + "type": "NarrativeText" + }, + { + "element_id": "0e77e68ba5473d98840c3212f4a8cb80", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 512.8, + 197.3 + ], + [ + 512.8, + 208.2 + ], + [ + 525.5, + 208.2 + ], + [ + 525.5, + 197.3 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 3, + "parent_id": "2ebe028b09bf728e110a69a96356e98a" + }, + "text": "(2)", + "type": "UncategorizedText" + }, + { + "element_id": "cdb4ee2aea69cc6a83331bbe96dc2caa", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 510.3, + 224.6 + ], + [ + 510.3, + 235.5 + ], + [ + 513.3, + 235.5 + ], + [ + 513.3, + 224.6 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 3, + "parent_id": "2ebe028b09bf728e110a69a96356e98a" + }, + "text": ".", + "type": "UncategorizedText" + }, + { + "element_id": "c7d4828d505e88fd6d50c5d097350afb", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 65.2 + ], + [ + 72.0, + 76.1 + ], + [ + 229.8, + 76.1 + ], + [ + 229.8, + 65.2 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 4 + }, + "text": "negative passages for each question.", + "type": "Title" + }, + { + "element_id": "c7cba915e3a62932889a7f6c3c31050b", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 79.2 + ], + [ + 72.0, + 157.8 + ], + [ + 291.2, + 157.8 + ], + [ + 291.2, + 79.2 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 73, + "text": "Yihetal .,", + "url": "cite.yih2011learning" + }, + { + "start_index": 85, + "text": "2011", + "url": "cite.yih2011learning" + }, + { + "start_index": 125, + "text": "Hendersonetal .,", + "url": "cite.henderson2017efficient" + }, + { + "start_index": 143, + "text": "2017", + "url": "cite.henderson2017efficient" + }, + { + "start_index": 149, + "text": "Gillicketal .,", + "url": "cite.gillick-etal-2019-learning" + }, + { + "start_index": 165, + "text": "2019", + "url": "cite.gillick-etal-2019-learning" + } + ], + "page_number": 4, + "parent_id": "c7d4828d505e88fd6d50c5d097350afb" + }, + "text": "The trick of in-batch negatives has been used in the full batch setting (Yih et al., 2011) and more recently for mini-batch (Henderson et al., 2017; Gillick et al., 2019). It has been shown to be an effective strategy for learning a dual-encoder model that boosts the number of training examples.", + "type": "NarrativeText" + }, + { + "element_id": "ece34411811a0a732567bb7b7282ef86", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 171.2 + ], + [ + 72.0, + 183.2 + ], + [ + 191.9, + 183.2 + ], + [ + 191.9, + 171.2 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 4 + }, + "text": "4 Experimental Setup", + "type": "Title" + }, + { + "element_id": "becf92039254407410319cad3e1f4847", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 194.1 + ], + [ + 72.0, + 218.5 + ], + [ + 290.5, + 218.5 + ], + [ + 290.5, + 194.1 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 4, + "parent_id": "ece34411811a0a732567bb7b7282ef86" + }, + "text": "In this section, we describe the data we used for experiments and the basic setup.", + "type": "NarrativeText" + }, + { + "element_id": "907e15b3e47cd91541fe1063ef3b224e", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 231.4 + ], + [ + 72.0, + 242.3 + ], + [ + 241.4, + 242.3 + ], + [ + 241.4, + 231.4 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 4 + }, + "text": "4.1 Wikipedia Data Pre-processing", + "type": "Title" + }, + { + "element_id": "cf2cef2c4e225e8618898ecc733b13b1", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 71.5, + 249.8 + ], + [ + 71.5, + 450.4 + ], + [ + 292.2, + 450.4 + ], + [ + 292.2, + 249.8 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 11, + "text": "Leeetal .,", + "url": "cite.lee2019latent" + }, + { + "start_index": 23, + "text": "2019", + "url": "cite.lee2019latent" + }, + { + "start_index": 187, + "text": "(", + "url": "cite.chen2017reading" + }, + { + "start_index": 193, + "text": "etal .,", + "url": "cite.chen2017reading" + }, + { + "start_index": 201, + "text": "2017", + "url": "cite.chen2017reading" + }, + { + "start_index": 532, + "text": "Wangetal .,", + "url": "cite.wang2019multi" + }, + { + "start_index": 545, + "text": "2019", + "url": "cite.wang2019multi" + }, + { + "start_index": 599, + "text": ".", + "url": "Hfootnote.6" + } + ], + "page_number": 4, + "parent_id": "907e15b3e47cd91541fe1063ef3b224e" + }, + "text": "Following (Lee et al., 2019), we use the English Wikipedia dump from Dec. 20, 2018 as the source documents for answering questions. We \ufb01rst apply the pre-processing code released in DrQA (Chen et al., 2017) to extract the clean, text-portion of articles from the Wikipedia dump. This step re- moves semi-structured data, such as tables, info- boxes, lists, as well as the disambiguation pages. We then split each article into multiple, disjoint text blocks of 100 words as passages, serving as our basic retrieval units, following (Wang et al., 2019), which results in 21,015,324 passages in the end.5 Each passage is also prepended with the title of the Wikipedia article where the passage is from, along with an [SEP] token.", + "type": "NarrativeText" + }, + { + "element_id": "6a27ae9ecb0eb52c884fd80f3b85c394", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 463.2 + ], + [ + 72.0, + 474.1 + ], + [ + 233.5, + 474.1 + ], + [ + 233.5, + 463.2 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 4 + }, + "text": "4.2 Question Answering Datasets", + "type": "Title" + }, + { + "element_id": "7b8f4715f2ea2d367c504bd2d865f25a", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 71.5, + 481.6 + ], + [ + 71.5, + 724.5 + ], + [ + 292.1, + 724.5 + ], + [ + 292.1, + 481.6 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 97, + "text": "Leeetal .,", + "url": "cite.lee2019latent" + }, + { + "start_index": 109, + "text": "2019", + "url": "cite.lee2019latent" + }, + { + "start_index": 248, + "text": "Kwiatkowskietal", + "url": "cite.kwiatkowski2019natural" + }, + { + "start_index": 268, + "text": "2019", + "url": "cite.kwiatkowski2019natural" + }, + { + "start_index": 465, + "text": "Joshietal .,", + "url": "cite.joshi-etal-2017-triviaqa" + }, + { + "start_index": 479, + "text": "2017", + "url": "cite.joshi-etal-2017-triviaqa" + }, + { + "start_index": 595, + "text": "Berantetal .,", + "url": "cite.berant2013semantic" + }, + { + "start_index": 610, + "text": "2013", + "url": "cite.berant2013semantic" + }, + { + "start_index": 737, + "text": "Baudi\u02c7sand\u02c7Sediv ` y", + "url": "cite.baudivs2015modeling" + } + ], + "page_number": 4, + "parent_id": "6a27ae9ecb0eb52c884fd80f3b85c394" + }, + "text": "We use the same \ufb01ve QA datasets and train- ing/dev/testing splitting method as in previous work (Lee et al., 2019). Below we brie\ufb02y describe each dataset and refer readers to their paper for the details of data preparation. Natural Questions (NQ) (Kwiatkowski et al., 2019) was designed for end-to-end question an- swering. The questions were mined from real Google search queries and the answers were spans in Wikipedia articles identi\ufb01ed by annotators. TriviaQA (Joshi et al., 2017) contains a set of trivia questions with answers that were originally scraped from the Web. WebQuestions (WQ) (Berant et al., 2013) consists of questions selected using Google Suggest API, where the answers are entities in Freebase. CuratedTREC (TREC) (Baudi\u02c7s and \u02c7Sediv`y, 2015) sources questions from TREC QA tracks", + "type": "NarrativeText" + }, + { + "element_id": "f854d7756ec1a89cd14f9f30ed5d33d6", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 734.5 + ], + [ + 72.0, + 764.9 + ], + [ + 291.8, + 764.9 + ], + [ + 291.8, + 734.5 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 10, + "text": "Wangetal", + "url": "cite.wang2019multi" + }, + { + "start_index": 23, + "text": "2019", + "url": "cite.wang2019multi" + } + ], + "page_number": 4, + "parent_id": "6a27ae9ecb0eb52c884fd80f3b85c394" + }, + "text": "5However, Wang et al. (2019) also propose splitting docu- ments into overlapping passages, which we do not \ufb01nd advan- tageous compared to the non-overlapping version.", + "type": "NarrativeText" + }, + { + "element_id": "4411e525721e7dd801755882fd2361b2", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 313.3, + 66.6 + ], + [ + 313.3, + 75.6 + ], + [ + 342.1, + 75.6 + ], + [ + 342.1, + 66.6 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 4 + }, + "text": "Dataset", + "type": "Title" + }, + { + "element_id": "e99c90db87c0e400832edcedcc1b14e3", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 410.2, + 66.6 + ], + [ + 410.2, + 75.6 + ], + [ + 431.5, + 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The two columns of Train denote the original training ex- amples in the dataset and the actual questions used for training DPR after \ufb01ltering. See text for more details.", + "type": "NarrativeText" + }, + { + "element_id": "2c9cb5e777bfcb3ce2cab29cc5b11ed0", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 306.9, + 215.8 + ], + [ + 306.9, + 389.7 + ], + [ + 527.5, + 389.7 + ], + [ + 527.5, + 215.8 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 105, + "text": "Rajpurkaretal .,", + "url": "cite.rajpurkar2016squad" + }, + { + "start_index": 123, + "text": "2016", + "url": "cite.rajpurkar2016squad" + }, + { + "start_index": 604, + "text": ". 1", + "url": "subsection.5.1" + } + ], + "page_number": 4, + "parent_id": "219a3bd3f501a2476d0f41dcee9ce459" + }, + "text": "as well as various Web sources and is intended for open-domain QA from unstructured corpora. SQuAD v1.1 (Rajpurkar et al., 2016) is a popu- lar benchmark dataset for reading comprehension. Annotators were presented with a Wikipedia para- graph, and asked to write questions that could be answered from the given text. Although SQuAD has been used previously for open-domain QA re- search, it is not ideal because many questions lack context in absence of the provided paragraph. 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If none of the top 100 retrieved passages has the answer, the ques- tion will be discarded. For SQuAD and Natural Questions, since the original passages have been split and processed differently than our pool of candidate passages, we match and replace each gold passage with the corresponding passage in the candidate pool.7 We discard the questions when the matching is failed due to different Wikipedia versions or pre-processing. 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"While it is good to have the \ufb02exibility to adapt the retriever to each dataset, it would also be de- sirable to obtain a single retriever that works well across the board. To this end, we train a multi- dataset encoder by combining training data from all datasets excluding SQuAD.8 In addition to DPR, we also present the results of BM25, the traditional retrieval method9 and BM25+DPR, using a linear combination of their scores as the new ranking function. Speci\ufb01cally, we obtain two initial sets of top-2000 passages based on BM25 and DPR, respectively, and rerank the union of them using BM25(q,p) + \u03bb \u00b7 sim(q, p) as the ranking function. We used \u03bb = 1.1 based on the retrieval accuracy in the development set.", + "type": "NarrativeText" + }, + { + "element_id": "bb8182a74607429eeb51c6b02e9ecdac", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 307.0, + 389.5 + ], + [ + 307.0, + 447.5 + ], + [ + 527.2, + 447.5 + ], + [ + 527.2, + 389.5 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 5, + "parent_id": "ae242bdd9c62f1617946f518f448b323" + }, + "text": "Figure 1: Retriever top-k accuracy with different num- bers of training examples used in our dense passage re- triever vs BM25. The results are measured on the de- velopment set of Natural Questions. Our DPR trained using 1,000 examples already outperforms BM25.", + "type": "NarrativeText" + }, + { + "element_id": "f75b5bc8b0c551bcbb1614d50aba4ef0", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 307.3, + 474.3 + ], + [ + 307.3, + 566.5 + ], + [ + 527.4, + 566.5 + ], + [ + 527.4, + 474.3 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 5, + "parent_id": "ae242bdd9c62f1617946f518f448b323" + }, + "text": "tiple datasets, TREC, the smallest dataset of the \ufb01ve, bene\ufb01ts greatly from more training examples. In contrast, Natural Questions and WebQuestions improve modestly and TriviaQA degrades slightly. Results can be improved further in some cases by combining DPR with BM25 in both single- and multi-dataset settings.", + "type": "NarrativeText" + }, + { + "element_id": "5537a1e812e5808d8f32f5cdda497a0b", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 585.2 + ], + [ + 72.0, + 596.1 + ], + [ + 158.1, + 596.1 + ], + [ + 158.1, + 585.2 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 5 + }, + "text": "5.1 Main Results", + "type": "Title" + }, + { + "element_id": "84f5b33d08884f697fae0d91f7b1f381", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 71.6, + 602.9 + ], + [ + 71.6, + 695.1 + ], + [ + 292.1, + 695.1 + ], + [ + 292.1, + 602.9 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 0, + "text": "Table2comparesdifferentpassageretrievalsys temson\ufb01veQAdatasets , usingthetop - ( k \u2208{", + "url": "table.caption.7" + } + ], + "page_number": 5, + "parent_id": "5537a1e812e5808d8f32f5cdda497a0b" + }, + "text": "Table 2 compares different passage retrieval sys- tems on \ufb01ve QA datasets, using the top-k accuracy (k \u2208 {20, 100}). With the exception of SQuAD, DPR performs consistently better than BM25 on all datasets. The gap is especially large when k is small (e.g., 78.4% vs. 59.1% for top-20 accuracy on Natural Questions). When training with mul-", + "type": "NarrativeText" + }, + { + "element_id": "be5a91236f3f537bdd6f17fa70b1cadf", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 307.3, + 570.7 + ], + [ + 307.3, + 690.0 + ], + [ + 527.4, + 690.0 + ], + [ + 527.4, + 570.7 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 420, + "text": ".(", + "url": "cite.lee2019latent" + }, + { + "start_index": 423, + "text": "2019", + "url": "cite.lee2019latent" + } + ], + "page_number": 5, + "parent_id": "5537a1e812e5808d8f32f5cdda497a0b" + }, + "text": "We conjecture that the lower performance on SQuAD is due to two reasons. First, the annota- tors wrote questions after seeing the passage. As a result, there is a high lexical overlap between passages and questions, which gives BM25 a clear advantage. Second, the data was collected from only 500+ Wikipedia articles and thus the distribu- tion of training examples is extremely biased, as argued previously by Lee et al. 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Figure 1 illustrates the top-k retrieval accuracy with respect to different num- bers of training examples, measured on the devel- opment set of Natural Questions. As is shown, a dense passage retriever trained using only 1,000 ex- amples already outperforms BM25. This suggests that with a general pretrained language model, it is possible to train a high-quality dense retriever with a small number of question\u2013passage pairs. 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"filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 6, + "parent_id": "44a6ad8737433180d6729f93f0bf4c24" + }, + "text": "Table 3: Comparison of different training schemes, measured as top-k retrieval accuracy on Natural Ques- #N: number of negative tions (development set). examples, IB: in-batch training. G.+BM25(1) and G.+BM25(2) denote in-batch training with 1 or 2 ad- ditional BM25 negatives, which serve as negative pas- sages for all questions in the batch.", + "type": "NarrativeText" + }, + { + "element_id": "cc643f4aca3015b926b2f6a989d7e312", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 307.3, + 305.6 + ], + [ + 307.3, + 384.3 + ], + [ + 525.7, + 384.3 + ], + [ + 525.7, + 305.6 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 179, + "text": ":", + "url": "appendix.A" + } + ], + "page_number": 6, + "parent_id": "44a6ad8737433180d6729f93f0bf4c24" + }, + "text": "Our experiments on Natural Questions show that switching to distantly-supervised passages (using the highest-ranked BM25 passage that contains the answer), has only a small impact: 1 point lower top-k accuracy for retrieval. Appendix A contains more details.", + "type": "NarrativeText" + }, + { + "element_id": "5375bcf4b99ff5452870d20b06412013", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 307.3, + 394.4 + ], + [ + 307.3, + 554.5 + ], + [ + 527.4, + 554.5 + ], + [ + 527.4, + 394.4 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 446, + "text": "(", + "url": "cite.burges2005learning" + }, + { + "start_index": 454, + "text": "etal .,", + "url": "cite.burges2005learning" + }, + { + "start_index": 462, + "text": "2005", + "url": "cite.burges2005learning" + }, + { + "start_index": 546, + "text": ". detailscanbefoundinAppendixB", + "url": "appendix.B" + } + ], + "page_number": 6, + "parent_id": "44a6ad8737433180d6729f93f0bf4c24" + }, + "text": "Similarity and loss Besides dot product, cosine and Euclidean L2 distance are also commonly used as decomposable similarity functions. We test these alternatives and \ufb01nd that L2 performs compara- ble to dot product, and both of them are superior to cosine. Similarly, in addition to negative log- likelihood, a popular option for ranking is triplet loss, which compares a positive passage and a nega- tive one directly with respect to a question (Burges et al., 2005). Our experiments show that using triplet loss does not affect the results much. More details can be found in Appendix B.", + "type": "NarrativeText" + }, + { + "element_id": "3f08ef7c7ccb9aa36188a1b578710b2a", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 306.9, + 564.6 + ], + [ + 306.9, + 765.3 + ], + [ + 527.5, + 765.3 + ], + [ + 527.5, + 564.6 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 6, + "parent_id": "44a6ad8737433180d6729f93f0bf4c24" + }, + "text": "Cross-dataset generalization One interesting question regarding DPR\u2019s discriminative training is how much performance degradation it may suf- fer from a non-iid setting. In other words, can it still generalize well when directly applied to a different dataset without additional \ufb01ne-tuning? To test the cross-dataset generalization, we train DPR on Natural Questions only and test it directly on the smaller WebQuestions and CuratedTREC datasets. We \ufb01nd that DPR generalizes well, with 3-5 points loss from the best performing \ufb01ne-tuned model in top-20 retrieval accuracy (69.9/86.3 vs. 75.0/89.1 for WebQuestions and TREC, respec- tively), while still greatly outperforming the BM25 baseline (55.0/70.9).", + "type": "NarrativeText" + }, + { + "element_id": "ee2253c61c597d8c61f15972ad2e5089", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 65.1 + ], + [ + 72.0, + 76.0 + ], + [ + 190.6, + 76.0 + ], + [ + 190.6, + 65.1 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 7 + }, + "text": "5.3 Qualitative Analysis", + "type": "Title" + }, + { + "element_id": "442af6d23319e7a9da68f75c5dc99c5d", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 71.6, + 83.3 + ], + [ + 71.6, + 175.5 + ], + [ + 292.1, + 175.5 + ], + [ + 292.1, + 83.3 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 275, + "text": ".", + "url": "appendix.C" + } + ], + "page_number": 7, + "parent_id": "ee2253c61c597d8c61f15972ad2e5089" + }, + "text": "Although DPR performs better than BM25 in gen- eral, passages retrieved by these two methods dif- fer qualitatively. Term-matching methods like BM25 are sensitive to highly selective keywords and phrases, while DPR captures lexical variations or semantic relationships better. See Appendix C for examples and more discussion.", + "type": "NarrativeText" + }, + { + "element_id": "2f3324a4fd6c56e27746984e7c300f16", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 187.9 + ], + [ + 72.0, + 198.8 + ], + [ + 189.6, + 198.8 + ], + [ + 189.6, + 187.9 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 7 + }, + "text": "5.4 Run-time Ef\ufb01ciency", + "type": "Title" + }, + { + "element_id": "13ad5dd59d4604e139396a5dc5e3be67", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 71.0, + 206.0 + ], + [ + 71.0, + 379.5 + ], + [ + 292.1, + 379.5 + ], + [ + 292.1, + 206.0 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 295, + "text": "- @ 2 . 20GHzand512GBmemory . FAISSin - memoryindexforreal - valuedvectors10", + "url": "Hfootnote.11" + } + ], + "page_number": 7, + "parent_id": "2f3324a4fd6c56e27746984e7c300f16" + }, + "text": "The main reason that we require a retrieval compo- nent for open-domain QA is to reduce the number of candidate passages that the reader needs to con- sider, which is crucial for answering user\u2019s ques- tions in real-time. We pro\ufb01led the passage retrieval speed on a server with Intel Xeon CPU E5-2698 v4 @ 2.20GHz and 512GB memory. With the help of FAISS in-memory index for real-valued vectors10, DPR can be made incredibly ef\ufb01cient, processing 995.0 questions per second, returning top 100 pas- sages per question. In contrast, BM25/Lucene (im- plemented in Java, using \ufb01le index) processes 23.7 questions per second per CPU thread.", + "type": "NarrativeText" + }, + { + "element_id": "47b1ce6dc0ff03626e4fab9309140792", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 382.4 + ], + [ + 72.0, + 501.7 + ], + [ + 292.1, + 501.7 + ], + [ + 292.1, + 382.4 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 7, + "parent_id": "2f3324a4fd6c56e27746984e7c300f16" + }, + "text": "On the other hand, the time required for building an index for dense vectors is much longer. Com- puting dense embeddings on 21-million passages is resource intensive, but can be easily parallelized, taking roughly 8.8 hours on 8 GPUs. However, building the FAISS index on 21-million vectors on a single server takes 8.5 hours. In comparison, building an inverted index using Lucene is much cheaper and takes only about 30 minutes in total.", + "type": "NarrativeText" + }, + { + "element_id": "af38cab6a4df868953e136c2c1dc139f", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 514.7 + ], + [ + 72.0, + 526.6 + ], + [ + 266.7, + 526.6 + ], + [ + 266.7, + 514.7 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 7 + }, + "text": "6 Experiments: Question Answering", + "type": "Title" + }, + { + "element_id": "780a2c6307da2ffe51d7aae906ab7af0", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 537.2 + ], + [ + 72.0, + 561.7 + ], + [ + 290.3, + 561.7 + ], + [ + 290.3, + 537.2 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 7, + "parent_id": "af38cab6a4df868953e136c2c1dc139f" + }, + "text": "In this section, we experiment with how different passage retrievers affect the \ufb01nal QA accuracy.", + "type": "NarrativeText" + }, + { + "element_id": "6c2139d41aaa0b6853052a20a6f229bf", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 574.1 + ], + [ + 72.0, + 585.0 + ], + [ + 204.4, + 585.0 + ], + [ + 204.4, + 574.1 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 7 + }, + "text": "6.1 End-to-end QA System", + "type": "Title" + }, + { + "element_id": "bac800be9f132d9bc309033d5b488248", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 71.5, + 592.2 + ], + [ + 71.5, + 725.0 + ], + [ + 292.2, + 725.0 + ], + [ + 292.2, + 592.2 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 7, + "parent_id": "6c2139d41aaa0b6853052a20a6f229bf" + }, + "text": "We implement an end-to-end question answering system in which we can plug different retriever systems directly. Besides the retriever, our QA sys- tem consists of a neural reader that outputs the answer to the question. Given the top k retrieved passages (up to 100 in our experiments), the reader assigns a passage selection score to each passage. In addition, it extracts an answer span from each passage and assigns a span score. The best span from the passage with the highest passage selection", + "type": "NarrativeText" + }, + { + "element_id": "f87ad5942b745ce926611c47eda2a3a0", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 734.5 + ], + [ + 72.0, + 764.9 + ], + [ + 291.4, + 764.9 + ], + [ + 291.4, + 734.5 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 7, + "parent_id": "6c2139d41aaa0b6853052a20a6f229bf" + }, + "text": "10FAISS con\ufb01guration: we used HNSW index type on CPU, neighbors to store per node = 512, construction time search depth = 200, search depth = 128.", + "type": "NarrativeText" + }, + { + "element_id": "a5b595988bcba207a47b29d1d80756d4", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 306.9, + 65.2 + ], + [ + 306.9, + 198.1 + ], + [ + 527.4, + 198.1 + ], + [ + 527.4, + 65.2 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 349, + "text": "1", + "url": "equation.3.1" + }, + { + "start_index": 465, + "text": "Wangetal .,", + "url": "cite.wang2019multi" + }, + { + "start_index": 478, + "text": "2019", + "url": "cite.wang2019multi" + }, + { + "start_index": 484, + "text": "2018", + "url": "cite.wang2018r" + }, + { + "start_index": 490, + "text": "Linetal .,", + "url": "cite.lin2018denoising" + }, + { + "start_index": 502, + "text": "2018", + "url": "cite.lin2018denoising" + } + ], + "page_number": 7, + "parent_id": "6c2139d41aaa0b6853052a20a6f229bf" + }, + "text": "score is chosen as the \ufb01nal answer. The passage selection model serves as a reranker through cross- attention between the question and the passage. Al- though cross-attention is not feasible for retrieving relevant passages in a large corpus due to its non- decomposable nature, it has more capacity than the dual-encoder model sim(q, p) as in Eq. (1). Apply- ing it to selecting the passage from a small number of retrieved candidates has been shown to work well (Wang et al., 2019, 2018; Lin et al., 2018).", + "type": "NarrativeText" + }, + { + "element_id": "905d86cc9d6f01520a08df6cf1d0e8fd", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 307.3, + 198.7 + ], + [ + 307.3, + 293.3 + ], + [ + 527.4, + 293.3 + ], + [ + 527.4, + 198.7 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 7, + "parent_id": "6c2139d41aaa0b6853052a20a6f229bf" + }, + "text": "Speci\ufb01cally, let Pi \u2208 RL\u00d7h (1 \u2264 i \u2264 k) be a BERT (base, uncased in our experiments) rep- resentation for the i-th passage, where L is the maximum length of the passage and h the hidden dimension. 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We compute a span score of the s-th to t-th words from the i-th passage as Pstart,i(s) \u00d7 Pend,i(t), and a passage selection score of the i-th passage as Pselected(i).", + "type": "NarrativeText" + }, + { + "element_id": "ac970c11f53f7030a85f7faa7b9080a4", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 402.3, + 369.7 + ], + [ + 402.3, + 384.0 + ], + [ + 455.9, + 384.0 + ], + [ + 455.9, + 369.7 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 7, + "parent_id": "6c2139d41aaa0b6853052a20a6f229bf" + }, + "text": ", . . . , P[CLS]", + "type": "UncategorizedText" + }, + { + "element_id": "6b86b273ff34fce19d6b804eff5a3f57", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 381.0, + 378.6 + ], + [ + 381.0, + 386.5 + ], + [ + 385.2, + 386.5 + ], + [ + 385.2, + 378.6 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 7, + "parent_id": "6c2139d41aaa0b6853052a20a6f229bf" + }, + "text": "1", + "type": "UncategorizedText" + }, + { + "element_id": "8254c329a92850f6d539dd376f4816ee", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 435.1, + 379.0 + ], + [ + 435.1, + 386.9 + ], + [ + 439.5, + 386.9 + ], + [ + 439.5, + 379.0 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 7 + }, + "text": "k", + "type": "Title" + }, + { + "element_id": "bf7b0392ed7eea8785844d1f7274dbe1", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 306.9, + 455.0 + ], + [ + 306.9, + 682.7 + ], + [ + 527.5, + 682.7 + ], + [ + 527.5, + 455.0 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 7, + "parent_id": "8254c329a92850f6d539dd376f4816ee" + }, + "text": "During training, we sample one positive and \u02dcm \u2212 1 negative passages from the top 100 passages returned by the retrieval system (BM25 or DPR) for each question. \u02dcm is a hyper-parameter and we use \u02dcm = 24 in all the experiments. The training ob- jective is to maximize the marginal log-likelihood of all the correct answer spans in the positive pas- sage (the answer string may appear multiple times in one passage), combined with the log-likelihood of the positive passage being selected. We use the batch size of 16 for large (NQ, TriviaQA, SQuAD) and 4 for small (TREC, WQ) datasets, and tune k on the development set. For experiments on small datasets under the Multi setting, in which using other datasets is allowed, we \ufb01ne-tune the reader trained on Natural Questions to the target dataset. All experiments were done on eight 32GB GPUs.", + "type": "NarrativeText" + }, + { + "element_id": "c4f34f099e4e35e7bc557654eb3cea6d", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 307.3, + 695.4 + ], + [ + 307.3, + 706.4 + ], + [ + 365.8, + 706.4 + ], + [ + 365.8, + 695.4 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 7 + }, + "text": "6.2 Results", + "type": "Title" + }, + { + "element_id": "b7e968d482b29979b9280789fd3c64b3", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 306.9, + 713.8 + ], + [ + 306.9, + 765.3 + ], + [ + 527.4, + 765.3 + ], + [ + 527.4, + 713.8 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 51, + "text": ",", + "url": "table.caption.15" + }, + { + "start_index": 136, + "text": "Chenetal", + "url": "cite.chen2017reading" + }, + { + "start_index": 149, + "text": "2017", + "url": "cite.chen2017reading" + }, + { + "start_index": 155, + "text": "Leeetal .,", + "url": "cite.lee2019latent" + }, + { + "start_index": 167, + "text": "2019", + "url": "cite.lee2019latent" + } + ], + "page_number": 7, + "parent_id": "c4f34f099e4e35e7bc557654eb3cea6d" + }, + "text": "Table 4 summarizes our \ufb01nal end-to-end QA re- sults, measured by exact match with the reference answer after minor normalization as in (Chen et al., 2017; Lee et al., 2019). 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The \ufb01rst block of results are copied from their cited papers. REALMWiki and REALMNews are the same model but pretrained on Wikipedia and CC-News, respectively. Single and Multi denote that our Dense Passage Retriever (DPR) is trained using individual or combined training datasets (all except SQuAD). For WQ and TREC in the Multi setting, we \ufb01ne-tune the reader trained on NQ.", + "type": "NarrativeText" + }, + { + "element_id": "c6880f922271aa2e47f61c48580e9344", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 71.6, + 342.2 + ], + [ + 71.6, + 718.9 + ], + [ + 292.1, + 718.9 + ], + [ + 292.1, + 342.2 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 720, + "text": "Leeetal .,", + "url": "cite.lee2019latent" + }, + { + "start_index": 732, + "text": "2019", + "url": "cite.lee2019latent" + }, + { + "start_index": 790, + "text": "( etal ., 2020 ). Whilebothmethodsincludeaddi", + "url": "cite.guu2020realm" + }, + { + "start_index": 795, + "text": "etal .,", + "url": "cite.guu2020realm" + }, + { + "start_index": 803, + "text": "2020", + "url": "cite.guu2020realm" + } + ], + "page_number": 8, + "parent_id": "0d35103ea53a7509e1b33954e6b46ec4" + }, + "text": "see that higher retriever accuracy typically leads to better \ufb01nal QA results: in all cases except SQuAD, answers extracted from the passages retrieved by DPR are more likely to be correct, compared to those from BM25. For large datasets like NQ and TriviaQA, models trained using multiple datasets (Multi) perform comparably to those trained using the individual training set (Single). Conversely, on smaller datasets like WQ and TREC, the multi- dataset setting has a clear advantage. Overall, our DPR-based models outperform the previous state- of-the-art results on four out of the \ufb01ve datasets, with 1% to 12% absolute differences in exact match accuracy. It is interesting to contrast our results to those of ORQA (Lee et al., 2019) and also the concurrently developed approach, REALM (Guu et al., 2020). While both methods include addi- tional pretraining tasks and employ an expensive end-to-end training regime, DPR manages to out- perform them on both NQ and TriviaQA, simply by focusing on learning a strong passage retrieval model using pairs of questions and answers. The additional pretraining tasks are likely more useful only when the target training sets are small. Al- though the results of DPR on WQ and TREC in the single-dataset setting are less competitive, adding more question\u2013answer pairs helps boost the perfor- mance, achieving the new state of the art.", + "type": "NarrativeText" + }, + { + "element_id": "0841fd3196f534a1e9ed52d9a37a175c", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 306.9, + 342.2 + ], + [ + 306.9, + 651.4 + ], + [ + 527.4, + 651.4 + ], + [ + 527.4, + 342.2 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 73, + "text": ".", + "url": "cite.lee2019latent" + }, + { + "start_index": 31, + "text": "2019", + "url": "cite.lee2019latent" + }, + { + "start_index": 401, + "text": ",", + "url": "appendix.D" + } + ], + "page_number": 8, + "parent_id": "0d35103ea53a7509e1b33954e6b46ec4" + }, + "text": "trained, following Lee et al. (2019). This approach obtains a score of 39.8 EM, which suggests that our strategy of training a strong retriever and reader in isolation can leverage effectively available supervi- sion, while outperforming a comparable joint train- ing approach with a simpler design (Appendix D). One thing worth noticing is that our reader does consider more passages compared to ORQA, al- though it is not completely clear how much more time it takes for inference. While DPR processes up to 100 passages for each question, the reader is able to \ufb01t all of them into one batch on a sin- gle 32GB GPU, thus the latency remains almost identical to the single passage case (around 20ms). The exact impact on throughput is harder to mea- sure: ORQA uses 2-3x longer passages compared to DPR (288 word pieces compared to our 100 tokens) and the computational complexity is super- linear in passage length. We also note that we found k = 50 to be optimal for NQ, and k = 10 leads to only marginal loss in exact match accu- racy (40.8 vs. 41.5 EM on NQ), which should be roughly comparable to ORQA\u2019s 5-passage setup.", + "type": "NarrativeText" + }, + { + "element_id": "6a25d12fe6bee59c3cbbab072fe440a8", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 307.3, + 664.2 + ], + [ + 307.3, + 676.2 + ], + [ + 396.4, + 676.2 + ], + [ + 396.4, + 664.2 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 8 + }, + "text": "7 Related Work", + "type": "Title" + }, + { + "element_id": "daa6a2e7025eb22d33b70734407c0b25", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 727.3 + ], + [ + 72.0, + 765.3 + ], + [ + 292.1, + 765.3 + ], + [ + 292.1, + 727.3 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 8, + "parent_id": "6a25d12fe6bee59c3cbbab072fe440a8" + }, + "text": "To compare our pipeline training approach with joint learning, we run an ablation on Natural Ques- tions where the retriever and reader are jointly", + "type": "NarrativeText" + }, + { + "element_id": "41bf1fd799946a7f92e455b2c7ccb3b6", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 307.0, + 686.7 + ], + [ + 307.0, + 765.3 + ], + [ + 527.4, + 765.3 + ], + [ + 527.4, + 686.7 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 72, + "text": "Voorhees", + "url": "cite.voorhees1999trec" + }, + { + "start_index": 82, + "text": "1999", + "url": "cite.voorhees1999trec" + } + ], + "page_number": 8, + "parent_id": "6a25d12fe6bee59c3cbbab072fe440a8" + }, + "text": "Passage retrieval has been an important compo- nent for open-domain QA (Voorhees, 1999). It not only effectively reduces the search space for answer extraction, but also identi\ufb01es the support context for users to verify the answer. Strong sparse vector space models like TF-IDF or BM25 have", + "type": "NarrativeText" + }, + { + "element_id": "93e281a97d4e9e284ff31e9cc6123a88", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 71.5, + 65.2 + ], + [ + 71.5, + 171.0 + ], + [ + 292.1, + 171.0 + ], + [ + 292.1, + 65.2 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 76, + "text": "Chenetal .,", + "url": "cite.chen2017reading" + }, + { + "start_index": 89, + "text": "2017", + "url": "cite.chen2017reading" + }, + { + "start_index": 93, + "text": "; etal ., 2019a , b ; Nieetal ., 2019 ; Minetal ., 2019a", + "url": "cite.yang2019end" + }, + { + "start_index": 100, + "text": "etal .,", + "url": "cite.yang2019end" + }, + { + "start_index": 108, + "text": "2019a", + "url": "cite.yang2019end" + }, + { + "start_index": 114, + "text": "b", + "url": "cite.Yang2019Data" + }, + { + "start_index": 117, + "text": "Nieetal .,", + "url": "cite.nie2019revealing" + }, + { + "start_index": 129, + "text": "2019", + "url": "cite.nie2019revealing" + }, + { + "start_index": 135, + "text": "Minetal .,", + "url": "cite.min2019discrete" + }, + { + "start_index": 147, + "text": "2019a", + "url": "cite.min2019discrete" + }, + { + "start_index": 154, + "text": "Wolfsonetal .,", + "url": "cite.wolfson2020break" + }, + { + "start_index": 170, + "text": "2020", + "url": "cite.wolfson2020break" + }, + { + "start_index": 332, + "text": "Minetal .,", + "url": "cite.min2019knowledge" + }, + { + "start_index": 344, + "text": "2019b", + "url": "cite.min2019knowledge" + }, + { + "start_index": 349, + "text": ";", + "url": "cite.asai2020learning" + }, + { + "start_index": 356, + "text": "etal .,", + "url": "cite.asai2020learning" + }, + { + "start_index": 364, + "text": "2020", + "url": "cite.asai2020learning" + } + ], + "page_number": 9, + "parent_id": "6a25d12fe6bee59c3cbbab072fe440a8" + }, + "text": "been used as the standard method applied broadly to various QA tasks (e.g., Chen et al., 2017; Yang et al., 2019a,b; Nie et al., 2019; Min et al., 2019a; Wolfson et al., 2020). Augmenting text-based re- trieval with external structured information, such as knowledge graph and Wikipedia hyperlinks, has also been explored recently (Min et al., 2019b; Asai et al., 2020).", + "type": "NarrativeText" + }, + { + "element_id": "b30b032166032e8d0f57688f4f1fc1dd", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 71.6, + 179.5 + ], + [ + 71.6, + 502.0 + ], + [ + 292.2, + 502.0 + ], + [ + 292.2, + 179.5 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 107, + "text": "Deerwesteretal .,", + "url": "cite.deerwester1990indexing" + }, + { + "start_index": 126, + "text": "1990", + "url": "cite.deerwester1990indexing" + }, + { + "start_index": 251, + "text": "Yihetal .,", + "url": "cite.yih2011learning" + }, + { + "start_index": 263, + "text": "2011", + "url": "cite.yih2011learning" + }, + { + "start_index": 269, + "text": "Huangetal .,", + "url": "cite.huang2013learning" + }, + { + "start_index": 283, + "text": "2013", + "url": "cite.huang2013learning" + }, + { + "start_index": 287, + "text": ";", + "url": "cite.gillick-etal-2019-learning" + }, + { + "start_index": 297, + "text": "etal .,", + "url": "cite.gillick-etal-2019-learning" + }, + { + "start_index": 305, + "text": "2019", + "url": "cite.gillick-etal-2019-learning" + }, + { + "start_index": 885, + "text": "NogueiraandCho", + "url": "cite.nogueira2019passage" + }, + { + "start_index": 903, + "text": "2019", + "url": "cite.nogueira2019passage" + }, + { + "start_index": 909, + "text": "Humeauetal .,", + "url": "cite.humeau2020poly" + }, + { + "start_index": 924, + "text": "2020", + "url": "cite.humeau2020poly" + }, + { + "start_index": 959, + "text": "KhattabandZaharia", + "url": "cite.khattab2020colbert" + }, + { + "start_index": 980, + "text": "2020", + "url": "cite.khattab2020colbert" + } + ], + "page_number": 9, + "parent_id": "6a25d12fe6bee59c3cbbab072fe440a8" + }, + "text": "The use of dense vector representations for re- trieval has a long history since Latent Semantic Analysis (Deerwester et al., 1990). Using labeled pairs of queries and documents, discriminatively trained dense encoders have become popular re- cently (Yih et al., 2011; Huang et al., 2013; Gillick et al., 2019), with applications to cross-lingual document retrieval, ad relevance prediction, Web search and entity retrieval. Such approaches com- plement the sparse vector methods as they can po- tentially give high similarity scores to semantically relevant text pairs, even without exact token match- ing. The dense representation alone, however, is typically inferior to the sparse one. While not the focus of this work, dense representations from pre- trained models, along with cross-attention mecha- nisms, have also been shown effective in passage or dialogue re-ranking tasks (Nogueira and Cho, 2019; Humeau et al., 2020). Finally, a concurrent work (Khattab and Zaharia, 2020) demonstrates the feasibility of full dense retrieval in IR tasks. Instead of employing the dual-encoder framework, they introduced a late-interaction operator on top of the BERT encoders.", + "type": "NarrativeText" + }, + { + "element_id": "06d742e7aa6f6d4514031e40bda93395", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 510.5 + ], + [ + 72.0, + 765.3 + ], + [ + 292.2, + 765.3 + ], + [ + 292.2, + 510.5 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 65, + "text": ".(", + "url": "cite.das2019multi" + }, + { + "start_index": 68, + "text": "2019", + "url": "cite.das2019multi" + }, + { + "start_index": 227, + "text": "Seoetal", + "url": "cite.seo2019real" + }, + { + "start_index": 239, + "text": "2019", + "url": "cite.seo2019real" + }, + { + "start_index": 480, + "text": "Leeetal", + "url": "cite.lee2019latent" + }, + { + "start_index": 492, + "text": "2019", + "url": "cite.lee2019latent" + }, + { + "start_index": 688, + "text": "Guuetal", + "url": "cite.guu2020realm" + }, + { + "start_index": 700, + "text": "2020", + "url": "cite.guu2020realm" + }, + { + "start_index": 770, + "text": "indexingthepassagesdur ingtraining . recentlybeenimprovedbyXiongetal", + "url": "cite.Xiong2020ProgressivelyPD" + }, + { + "start_index": 886, + "text": "2020b", + "url": "cite.Xiong2020ProgressivelyPD" + } + ], + "page_number": 9, + "parent_id": "6a25d12fe6bee59c3cbbab072fe440a8" + }, + "text": "Dense retrieval for open-domain QA has been explored by Das et al. (2019), who propose to re- trieve relevant passages iteratively using reformu- lated question vectors. As an alternative approach that skips passage retrieval, Seo et al. (2019) pro- pose to encode candidate answer phrases as vectors and directly retrieve the answers to the input ques- tions ef\ufb01ciently. Using additional pretraining with the objective that matches surrogates of questions and relevant passages, Lee et al. (2019) jointly train the question encoder and reader. Their approach outperforms the BM25 plus reader paradigm on multiple open-domain QA datasets in QA accuracy, and is further extended by REALM (Guu et al., 2020), which includes tuning the passage encoder asynchronously by re-indexing the passages dur- ing training. The pretraining objective has also recently been improved by Xiong et al. (2020b). In contrast, our model provides a simple and yet", + "type": "NarrativeText" + }, + { + "element_id": "d1901aa1bfb8cc149e914018f1143797", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 307.3, + 65.2 + ], + [ + 307.3, + 103.2 + ], + [ + 527.4, + 103.2 + ], + [ + 527.4, + 65.2 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 9, + "parent_id": "6a25d12fe6bee59c3cbbab072fe440a8" + }, + "text": "effective solution that shows stronger empirical per- formance, without relying on additional pretraining or complex joint training schemes.", + "type": "NarrativeText" + }, + { + "element_id": "9b41b843780ccbd32b5817c1d22d121f", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 306.9, + 105.9 + ], + [ + 306.9, + 306.5 + ], + [ + 527.5, + 306.5 + ], + [ + 527.5, + 105.9 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 132, + "text": "Xiongetal", + "url": "cite.Xiong2020ANCE" + }, + { + "start_index": 146, + "text": "2020a", + "url": "cite.Xiong2020ANCE" + }, + { + "start_index": 427, + "text": "IzacardandGrave", + "url": "cite.Izacard2020fid" + }, + { + "start_index": 446, + "text": "2020", + "url": "cite.Izacard2020fid" + }, + { + "start_index": 452, + "text": "Lewisetal .,", + "url": "cite.lewis2020rag" + }, + { + "start_index": 466, + "text": "2020b", + "url": "cite.lewis2020rag" + }, + { + "start_index": 551, + "text": "Lewisetal .,", + "url": "cite.lewis-etal-2020-bart" + }, + { + "start_index": 565, + "text": "2020a", + "url": "cite.lewis-etal-2020-bart" + }, + { + "start_index": 580, + "text": "Raf", + "url": "cite.raffel2019T5" + }, + { + "start_index": 585, + "text": "feletal .,", + "url": "cite.raffel2019T5" + }, + { + "start_index": 597, + "text": "2019", + "url": "cite.raffel2019T5" + } + ], + "page_number": 9, + "parent_id": "6a25d12fe6bee59c3cbbab072fe440a8" + }, + "text": "DPR has also been used as an important mod- ule in very recent work. For instance, extending the idea of leveraging hard negatives, Xiong et al. (2020a) use the retrieval model trained in the pre- vious iteration to discover new negatives and con- struct a different set of examples in each training iteration. Starting from our trained DPR model, they show that the retrieval performance can be further improved. Recent work (Izacard and Grave, 2020; Lewis et al., 2020b) have also shown that DPR can be combined with generation models such as BART (Lewis et al., 2020a) and T5 (Raf- fel et al., 2019), achieving good performance on open-domain QA and other knowledge-intensive tasks.", + "type": "NarrativeText" + }, + { + "element_id": "57cf5cd97bc68a0b5b414f3a4bd37ce6", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 307.3, + 318.4 + ], + [ + 307.3, + 330.3 + ], + [ + 382.3, + 330.3 + ], + [ + 382.3, + 318.4 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 9 + }, + "text": "8 Conclusion", + "type": "Title" + }, + { + "element_id": "e2a3ea98a8f72a9b455cf1e7b566216f", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 306.9, + 340.2 + ], + [ + 306.9, + 513.7 + ], + [ + 527.4, + 513.7 + ], + [ + 527.4, + 340.2 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 9, + "parent_id": "57cf5cd97bc68a0b5b414f3a4bd37ce6" + }, + "text": "In this work, we demonstrated that dense retrieval can outperform and potentially replace the tradi- tional sparse retrieval component in open-domain question answering. While a simple dual-encoder approach can be made to work surprisingly well, we showed that there are some critical ingredients to training a dense retriever successfully. Moreover, our empirical analysis and ablation studies indicate that more complex model frameworks or similarity functions do not necessarily provide additional val- ues. As a result of improved retrieval performance, we obtained new state-of-the-art results on multiple open-domain question answering benchmarks.", + "type": "NarrativeText" + }, + { + "element_id": "4be0f860ee3560910d50596233fe802e", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 307.3, + 525.6 + ], + [ + 307.3, + 537.5 + ], + [ + 400.8, + 537.5 + ], + [ + 400.8, + 525.6 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 9 + }, + "text": "Acknowledgments", + "type": "Title" + }, + { + "element_id": "84e4539ac39232472a1473ccab786909", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 306.8, + 547.4 + ], + [ + 306.8, + 571.8 + ], + [ + 525.5, + 571.8 + ], + [ + 525.5, + 547.4 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 9, + "parent_id": "4be0f860ee3560910d50596233fe802e" + }, + "text": "We thank the anonymous reviewers for their helpful comments and suggestions.", + "type": "NarrativeText" + }, + { + "element_id": "69824d3b0e70ca6aaa0da1613b65fd91", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 307.3, + 596.4 + ], + [ + 307.3, + 608.4 + ], + [ + 362.8, + 608.4 + ], + [ + 362.8, + 596.4 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 9 + }, + "text": "References", + "type": "Title" + }, + { + "element_id": "0f7bab4a5f829a443e580076e6e4c1eb", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 307.3, + 615.7 + ], + [ + 307.3, + 669.5 + ], + [ + 527.2, + 669.5 + ], + [ + 527.2, + 615.7 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 9, + "parent_id": "69824d3b0e70ca6aaa0da1613b65fd91" + }, + "text": "Akari Asai, Kazuma Hashimoto, Hannaneh Hajishirzi, Richard Socher, and Caiming Xiong. 2020. 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Learning discriminative In Com- projections for text similarity measures. putational Natural Language Learning (CoNLL), pages 247\u2013256.", + "type": "NarrativeText" + }, + { + "element_id": "1129f613984b33625a968f9d37b0e9ae", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 64.3 + ], + [ + 72.0, + 76.3 + ], + [ + 193.1, + 76.3 + ], + [ + 193.1, + 64.3 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 12 + }, + "text": "A Distant Supervision", + "type": "Title" + }, + { + "element_id": "0e24275c9776a54f5d3b7ea1130e7979", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 71.5, + 87.7 + ], + [ + 71.5, + 274.7 + ], + [ + 292.1, + 274.7 + ], + [ + 292.1, + 87.7 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 462, + "text": "werunLucene", + "url": "table.caption.18" + } + ], + "page_number": 12, + "parent_id": "1129f613984b33625a968f9d37b0e9ae" + }, + "text": "When training our \ufb01nal DPR model using Natural Questions, we use the passages in our collection that best match the gold context as the positive passages. As some QA datasets contain only the question and answer pairs, it is thus interesting to see when using the passages that contain the answers as positives (i.e., the distant supervision setting), whether there is a signi\ufb01cant performance degradation. Using the question and answer to- gether as the query, we run Lucene-BM25 and pick the top passage that contains the answer as the pos- itive passage. Table 5 shows the performance of DPR when trained using the original setting and the distant supervision setting.", + "type": "NarrativeText" + }, + { + "element_id": "50646ff11d73781cd15ffd1a2848423d", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 288.8 + ], + [ + 72.0, + 300.7 + ], + [ + 269.8, + 300.7 + ], + [ + 269.8, + 288.8 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 12 + }, + "text": "B Alternative Similarity Functions &", + "type": "Title" + }, + { + "element_id": "d6799db9c490ab134288cd1874060434", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 91.5, + 302.7 + ], + [ + 91.5, + 314.7 + ], + [ + 152.8, + 314.7 + ], + [ + 152.8, + 302.7 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 12 + }, + "text": "Triplet Loss", + "type": "Title" + }, + { + "element_id": "b107ab4ae2f9c81b7b37cf8160f8313e", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 326.1 + ], + [ + 72.0, + 472.5 + ], + [ + 292.1, + 472.5 + ], + [ + 292.1, + 326.1 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 402, + "text": "ble6summarizestheresults", + "url": "table.caption.19" + } + ], + "page_number": 12, + "parent_id": "d6799db9c490ab134288cd1874060434" + }, + "text": "In addition to dot product (DP) and negative log- likelihood based on softmax (NLL), we also exper- iment with Euclidean distance (L2) and the triplet loss. We negate L2 similarity scores before ap- plying softmax and change signs of question-to- positive and question-to-negative similarities when applying the triplet loss on dot product scores. The margin value of the triplet loss is set to 1. Ta- ble 6 summarizes the results. All these additional experiments are conducted using the same hyper- parameters tuned for the baseline (DP, NLL).", + "type": "NarrativeText" + }, + { + "element_id": "0756b4dd4618410eaee4d84ec1bf6d47", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 71.6, + 475.7 + ], + [ + 71.6, + 554.4 + ], + [ + 292.2, + 554.4 + ], + [ + 292.2, + 475.7 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 81, + "text": "(", + "url": "table.caption.18" + }, + { + "start_index": 42, + "text": "baseline", + "url": "table.caption.19" + } + ], + "page_number": 12, + "parent_id": "d6799db9c490ab134288cd1874060434" + }, + "text": "Note that the retrieval accuracy for our \u201cbaseline\u201d settings reported in Table 5 (Gold) and Table 6 (DP, NLL) is slightly better than those reported in Table 3. This is due to a better hyper-parameter setting used in these analysis experiments, which is documented in our code release.", + "type": "NarrativeText" + }, + { + "element_id": "1464dafdbd8a709f69fd4e6afe389f12", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 72.0, + 568.5 + ], + [ + 72.0, + 580.4 + ], + [ + 195.6, + 580.4 + ], + [ + 195.6, + 568.5 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 12 + }, + "text": "C Qualitative Analysis", + "type": "Title" + }, + { + "element_id": "1604f362b7a852f61d885b9a656e67b0", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 71.6, + 591.8 + ], + [ + 71.6, + 765.3 + ], + [ + 292.1, + 765.3 + ], + [ + 292.1, + 591.8 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "links": [ + { + "start_index": 466, + "text": ".", + "url": "table.caption.20" + } + ], + "page_number": 12, + "parent_id": "1464dafdbd8a709f69fd4e6afe389f12" + }, + "text": "Although DPR performs better than BM25 in gen- eral, the retrieved passages of these two retrievers actually differ qualitatively. Methods like BM25 are sensitive to highly selective keywords and phrases, but cannot capture lexical variations or se- mantic relationships well. In contrast, DPR excels at semantic representation, but might lack suf\ufb01cient capacity to represent salient phrases which appear rarely. Table 7 illustrates this phenomenon with two examples. In the \ufb01rst example, the top scor- ing passage from BM25 is irrelevant, even though keywords such as England and Ireland appear mul- tiple times. 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"type": "UncategorizedText" + }, + { + "element_id": "6f6d1b5be8e60cb62d67e1b6746034ee", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 307.0, + 295.5 + ], + [ + 307.0, + 329.6 + ], + [ + 525.5, + 329.6 + ], + [ + 525.5, + 295.5 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 12, + "parent_id": "cf1ca67c9797769a98b9425d7c96df06" + }, + "text": "Table 6: Retrieval Top-k accuracy on the development set of Natural Questions using different similarity and loss functions.", + "type": "NarrativeText" + }, + { + "element_id": "834219e459a166853aea1182c77716f0", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 306.9, + 354.0 + ], + [ + 306.9, + 432.9 + ], + [ + 527.5, + 432.9 + ], + [ + 527.5, + 354.0 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 12, + "parent_id": "cf1ca67c9797769a98b9425d7c96df06" + }, + "text": "the correct answer, presumably by matching \u201cbody of water\u201d with semantic neighbors such as sea and channel, even though no lexical overlap exists. The second example is one where BM25 does better. The salient phrase \u201cThoros of Myr\u201d is critical, and DPR is unable to capture it.", + "type": "NarrativeText" + }, + { + "element_id": "ef488b07af1bbd378249119d61f660da", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 307.3, + 446.5 + ], + [ + 307.3, + 458.5 + ], + [ + 487.2, + 458.5 + ], + [ + 487.2, + 446.5 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 12 + }, + "text": "D Joint Training of Retriever and", + "type": "Title" + }, + { + "element_id": "d8554253f56d58e88bc82df1e41507c3", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 327.9, + 460.5 + ], + [ + 327.9, + 472.4 + ], + [ + 365.0, + 472.4 + ], + [ + 365.0, + 460.5 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 12 + }, + "text": "Reader", + "type": "Title" + }, + { + "element_id": "7ac7a6f956ede9bfe760593409635be3", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 306.8, + 483.4 + ], + [ + 306.8, + 765.3 + ], + [ + 527.4, + 765.3 + ], + [ + 527.4, + 483.4 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 12, + "parent_id": "d8554253f56d58e88bc82df1e41507c3" + }, + "text": "We \ufb01x the passage encoder in our joint-training scheme while allowing only the question encoder to receive backpropagation signal from the com- bined (retriever + reader) loss function. This allows us to leverage the HNSW-based FAISS index for ef\ufb01cient low-latency retrieving, without reindexing the passages during model updates. Our loss func- tion largely follows ORQA\u2019s approach, which uses log probabilities of positive passages selected from the retriever model, and correct spans and passages selected from the reader model. Since the passage encoder is \ufb01xed, we could use larger amount of retrieved passages when calculating the retriever loss. Speci\ufb01cally, we get top 100 passages for each question in a mini-batch and use the method similar to in-batch negative training: all retrieved passages\u2019 vectors participate in the loss calculation for all questions in a batch. Our training batch size is set to 16, which effectively gives 1,600 passages per question to calculate retriever loss. The reader still uses 24 passages per question, which are selected", + "type": "NarrativeText" + }, + { + "element_id": "289aff12b04274cb04b8f7dbf486ba8b", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 78.0, + 66.8 + ], + [ + 78.0, + 73.8 + ], + [ + 104.7, + 73.8 + ], + [ + 104.7, + 66.8 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 13 + }, + "text": "Question", + "type": "Title" + }, + { + "element_id": "2bd77150c35e0e6bd9b74d395558ac6a", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 175.4, + 66.8 + ], + [ + 175.4, + 73.8 + ], + [ + 253.9, + 73.8 + ], + [ + 253.9, + 66.8 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 13, + "parent_id": "289aff12b04274cb04b8f7dbf486ba8b" + }, + "text": "Passage received by BM25", + "type": "NarrativeText" + }, + { + "element_id": "c1c8b1a89f037dc7216a9c02a5240d14", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 357.8, + 66.8 + ], + [ + 357.8, + 73.8 + ], + [ + 434.7, + 73.8 + ], + [ + 434.7, + 66.8 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 13, + "parent_id": "289aff12b04274cb04b8f7dbf486ba8b" + }, + "text": "Passage retrieved by DPR", + "type": "NarrativeText" + }, + { + "element_id": "40e116ff1cd3767ab02e86a1b0ebfec1", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 78.0, + 80.3 + ], + [ + 78.0, + 95.2 + ], + [ + 162.6, + 95.2 + ], + [ + 162.6, + 80.3 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 13, + "parent_id": "289aff12b04274cb04b8f7dbf486ba8b" + }, + "text": "What is the body of water between England and Ireland?", + "type": "NarrativeText" + }, + { + "element_id": "f7d8ed42f450e472bb21de2b090d18f0", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 175.4, + 80.3 + ], + [ + 175.4, + 151.0 + ], + [ + 346.6, + 151.0 + ], + [ + 346.6, + 80.3 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 13, + "parent_id": "289aff12b04274cb04b8f7dbf486ba8b" + }, + "text": "Title:British Cycling . . . England is not recognised as a region by the UCI, and there is no English cycling team outside the Commonwealth Games. For those occasions, British Cycling selects and sup- ports the England team. Cycling is represented on the Isle of Man by the Isle of Man Cycling Association. Cycling in Northern Ireland is organised under Cycling Ulster, part of the all-Ireland governing body Cycling Ireland. Until 2006, a rival governing body existed, . . .", + "type": "NarrativeText" + }, + { + "element_id": "d54f229d405f23c8d49d5db798b9622c", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 357.8, + 80.3 + ], + [ + 357.8, + 151.0 + ], + [ + 529.0, + 151.0 + ], + [ + 529.0, + 80.3 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 13, + "parent_id": "289aff12b04274cb04b8f7dbf486ba8b" + }, + "text": "Title: Irish Sea . . . Annual traf\ufb01c between Great Britain and Ireland amounts to over 12 million passengers and of traded goods. The Irish Sea is connected to the North Atlantic at both its northern and southern ends. To the north, the connection is through the North Channel between Scotland and Northern Ireland and the Malin Sea. The southern end is linked to the Atlantic through the St George\u2019s Channel between Ireland and Pem- brokeshire, and the Celtic Sea. . . .", + "type": "NarrativeText" + }, + { + "element_id": "201fd3e1ef676f0fd2cca2eb3bc46f29", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 78.0, + 157.5 + ], + [ + 78.0, + 172.4 + ], + [ + 158.0, + 172.4 + ], + [ + 158.0, + 157.5 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 13 + }, + "text": "Who plays Thoros of Myr in Game of Thrones?", + "type": "Title" + }, + { + "element_id": "b85e40c9de307419a1ed797e08dff0e4", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 175.1, + 157.5 + ], + [ + 175.1, + 220.3 + ], + [ + 345.6, + 220.3 + ], + [ + 345.6, + 157.5 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 13, + "parent_id": "201fd3e1ef676f0fd2cca2eb3bc46f29" + }, + "text": "Title: No One (Game of Thrones) . . . He may be \u201dno one,\u201d but there\u2019s still enough of a person left in him to respect, and admire who this girl is and what she\u2019s become. Arya \ufb01nally tells us something that we\u2019ve kind of known all along, that she\u2019s not no one, she\u2019s Arya Stark of Winterfell.\u201d \u201dNo One\u201d saw the reintroduction of Richard Dormer and Paul Kaye, who portrayed Beric Dondarrion and Thoros of Myr, respectively, in the third season, . . .", + "type": "NarrativeText" + }, + { + "element_id": "2c3c7715ec7eeb841efcf5f6717c720d", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 357.6, + 157.5 + ], + [ + 357.6, + 228.2 + ], + [ + 529.0, + 228.2 + ], + [ + 529.0, + 157.5 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 13, + "parent_id": "201fd3e1ef676f0fd2cca2eb3bc46f29" + }, + "text": "Title: P\u02daal Sverre Hagen P\u02daal Sverre Valheim Hagen (born 6 November 1980) is a Nor- wegian stage and screen actor. He appeared in the Norwe- gian \ufb01lm \u201dMax Manus\u201d and played Thor Heyerdahl in the Oscar-nominated 2012 \ufb01lm \u201dKon-Tiki\u201d. Pl Hagen was born in Stavanger, Norway, the son of Roar Hagen, a Norwegian cartoonist who has long been associated with Norway\u00b4s largest daily, \u201dVG\u201d. He lived in Jtten, a neighborhood in the city of Stavanger in south-western Norway. . . .", + "type": "NarrativeText" + }, + { + "element_id": "5d2d29cd635ce2010df757d324c46143", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 71.6, + 242.3 + ], + [ + 71.6, + 264.3 + ], + [ + 525.5, + 264.3 + ], + [ + 525.5, + 242.3 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 13, + "parent_id": "201fd3e1ef676f0fd2cca2eb3bc46f29" + }, + "text": "Table 7: Examples of passages returned from BM25 and DPR. Correct answers are written in blue and the content words in the question are written in bold.", + "type": "NarrativeText" + }, + { + "element_id": "609c73cc5109431d01616ad302b31c09", + "metadata": { + "coordinates": { + "layout_height": 841.89, + "layout_width": 595.276, + "points": [ + [ + 71.6, + 288.1 + ], + [ + 71.6, + 434.5 + ], + [ + 290.7, + 434.5 + ], + [ + 290.7, + 288.1 + ] + ], + "system": "PixelSpace" + }, + "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", + "filename": "2004.04906.pdf", + "filetype": "application/pdf", + "languages": [ + "eng" + ], + "last_modified": "2023-09-04T16:02:28", + "page_number": 13, + "parent_id": "201fd3e1ef676f0fd2cca2eb3bc46f29" + }, + "text": "from the top 5 positive and top 30 negative passages (from the set of top 100 passages retrieved from the same question). The question encoder\u2019s initial state is taken from a DPR model previously trained on the NQ dataset. The reader\u2019s initial state is a BERT-base model. In terms of the end-to-end QA results, our joint-training scheme does not provide better results compared to the usual retriever/reader training pipeline, resulting in the same 39.8 exact match score on NQ dev as in our regular reader model training.", + "type": "NarrativeText" + } +] \ No newline at end of file diff --git a/all2txt/pdf2txt.py b/all2txt/pdf2txt.py new file mode 100644 index 0000000..543a0fc --- /dev/null +++ b/all2txt/pdf2txt.py @@ -0,0 +1,92 @@ +from unstructured.partition.auto import partition + +# +# # +# # elements = partition(filename="/Users/wuchengwei/Documents/privateText/人大/LLMQA/lunwen/LLMQA/2004.04906.pdf") +# # print("\n\n".join([str(el) for el in elements])) +# +# +# from unstructured.partition.pdf import partition_pdf +# +# # Returns a List[Element] present in the pages of the parsed pdf document +# # elements = partition_pdf("/Users/wuchengwei/Documents/privateText/人大/LLMQA/lunwen/LLMQA/2004.04906.pdf") +# # print(elements) +# # print("\n\n".join([str(el) for el in elements])) +# +# +# # from unstructured.documents.elements import NarrativeText +# # from unstructured.partition.text_type import sentence_count +# # +# # for element in elements[:100]: +# # if isinstance(element, NarrativeText) and sentence_count(element.text) > 2: +# # print(element) +# # print("\n") + + +from unstructured.partition.pdf import partition_pdf + +# +filename = "/Users/wuchengwei/Documents/privateText/人大/LLMQA/lunwen/LLMQA/2004.04906.pdf" +# +elements = partition_pdf(filename=filename, infer_table_structure=True, strategy="hi_res") +# print(elements) +# print("\n\n".join([str(el) for el in elements])) + +tables = [el for el in elements if el.category == "Table"] +print("===tables===>" + str(tables)) +for table in tables: + print(table) + print(table.metadata.text_as_html) + # print(table[0].metadata.text_as_html) +tables_FigureCaption = [el for el in elements if el.category == "FigureCaption"] +tables_NarrativeText = [el for el in elements if el.category == "NarrativeText"] +tables_ListItem = [el for el in elements if el.category == "ListItem"] +tables_Title = [el for el in elements if el.category == "Title"] +tables_Address = [el for el in elements if el.category == "Address"] +tables_PageBreak = [el for el in elements if el.category == "PageBreak"] +tables_Header = [el for el in elements if el.category == "Header"] +tables_Footer = [el for el in elements if el.category == "Footer"] +tables_UncategorizedText = [el for el in elements if el.category == "UncategorizedText"] +tables_Image = [el for el in elements if el.category == "Image"] +tables_Formula = [el for el in elements if el.category == "Formula"] +print("===tables_FigureCaption===>" + str(tables_FigureCaption)) +for FigureCaption in tables_FigureCaption: + print(FigureCaption) +print("===tables_NarrativeText===>" + str(tables_NarrativeText)) +for NarrativeText in tables_NarrativeText: + print(NarrativeText) +print("===tables_ListItem===>" + str(tables_ListItem)) +for ListItem in tables_ListItem: + print(ListItem) +print("===tables_Title===>" + str(tables_Title)) +for Title in tables_Title: + print(Title) +print("===tables_Address===>" + str(tables_Address)) +for Address in tables_Address: + print(Address) +print("===tables_PageBreak===>" + str(tables_PageBreak)) +for PageBreak in tables_PageBreak: + print(PageBreak) +print("===tables_Header===>" + str(tables_Header)) +for Header in tables_Header: + print(Header) +print("===tables_Footer===>" + str(tables_Footer)) +for Footer in tables_Footer: + print(Footer) +print("===tables_UncategorizedText===>" + str(tables_UncategorizedText)) +for UncategorizedText in tables_UncategorizedText: + print(UncategorizedText) +print("===tables_Image===>" + str(tables_Image)) +for Image in tables_Image: + print(Image) +print("===tables_Formula===>" + str(tables_Formula)) +for Formula in tables_Formula: + print(Formula) +# +# print(tables[0].text) +# print(tables[0].metadata.text_as_html) + +from unstructured.staging.base import elements_to_json, elements_from_json + +# filename = "/Users/wuchengwei/Downloads/code/zhiyuan/wuchengwei_FlagData/FlagData/output/outputs1.json" +# elements_to_json(elements, filename=filename) diff --git a/gpt4_data_gen/README.md b/gpt4_data_gen/README.md new file mode 100644 index 0000000..fc172be --- /dev/null +++ b/gpt4_data_gen/README.md @@ -0,0 +1,11 @@ +### 基于OpenAI接口的数据增强模块 + +利用OpenAI接口，以三种不同策略，构建一系列针对不同能力的单轮SFT数据。策略包括： + ++ ImitateGenerator：以若干案例样本为模板，扩增数据。支持同时生成多种语言数据。 ++ AbilityExtractionGenerator: 利用OpenAI接口，归纳出若干案例样本中包含的能力。根据这个能力集合，生成新样本和答案。 ++ AbilityDirectGenerator: 根据指定的能力类型，或者任务类型，直接生成与该能力或任务相关的新样本。例如，指定能力为“逻辑推理”，则可生成一系列逻辑推理题目及答案。为增强生成样本的多样性，支持排除已生成样本。 + +具体示例见`example.py` + + \ No newline at end of file diff --git a/gpt4_data_gen/example.py b/gpt4_data_gen/example.py new file mode 100644 index 0000000..23f1bce --- /dev/null +++ b/gpt4_data_gen/example.py @@ -0,0 +1,51 @@ +from strategy import * +from utils import * +import pandas as pd +import random +import copy +import sys +sys.path.append('/home/baaidial/duli/sft_aug/gpt4_n') +from multiprocess_call_api import run_multiprocess, get_gpt4_response +import timeout_decorator +import openai +import json +import re +import os + +sample_ls = [] + +TIMEOUT = 240 + +openai.api_key = '314c94b92ba04eda88ad930d51c1ae69' # os.getenv("AZURE_OPENAI_KEY") +openai.api_base = 'https://baaiaquila.openai.azure.com/' #os.getenv("AZURE_OPENAI_ENDPOINT") # your endpoint should look like the following https://YOUR_RESOURCE_NAME.openai.azure.com/ +openai.api_type = 'azure' +openai.api_version = '2023-05-15' # this may change in the future + +deployment_name='gpt-4' #This will correspond to the custom name you chose for your deployment when you deployed a model. + +wkdir = "/home/baaidial/duli/sft_aug/dat/aug_dat/revising" +output_path = 'generated_dat.jsonl' + +example_ls = ['我小时候吃虾，然后出现过敏反应。几年后，我吃了带有章鱼的虾，然后出现了更严重的过敏反应。每次我去那些有强烈虾味的餐馆，我都会一贯性地出现过敏反应。这可以推断出：', + '小明观察到他周围的人们都喜欢吃巧克力。因此，他得出结论：所有人都喜欢吃巧克力。请问这个结论是否可靠？如果可靠，可以如何解释这种观察结果？如果不可靠，应该如何修正这个结论？', + '在一个实验中，A组实验者使用药物X，B组实验者使用药物Y。最后发现A组的痊愈率明显高于B组。基于这一结果，我们可以得出什么结论？', + '如果一个人经常迟到，那么他的时间观念是强还是弱？', + '如果一个地区的犯罪率降低了，那么这个地区的治安是提高了还是降低了？'] + +task = '逻辑推理' + +generator = ImitateGenerator(example_ls, additional_ceveat = '', multi_language=False) +generator.gen_ques(num_samples=100, task=task, language='中文', model='gpt4') +generator.gen_ans() + +generator = AbilityExtractionGenerator(example_ls) +generator.gen_ques(num_examples=100) +generator.gen_ans() + +generator = AbilityDirectGenerator(example_ls, additional_ceveat = '', exclude_prev=True) +generator.gen_ques(num_examples=100, task=task, num_exclude=500, model='gpt4') +generator.gen_ans() + +out_path = os.path.join(wkdir, output_path) +os.makedirs(out_path, exist_ok=False) +save_jsonl(generator.samples_gen, output_path) \ No newline at end of file diff --git a/gpt4_data_gen/prompt_template.py b/gpt4_data_gen/prompt_template.py new file mode 100644 index 0000000..c433244 --- /dev/null +++ b/gpt4_data_gen/prompt_template.py @@ -0,0 +1,55 @@ + +ability_extraction_prompt = "现在有一系列样本，例如：{sample_str} \n \ + 请读题，并列举出这些题目需要运用哪些能力才能完成。\ + 注意：你要以这样的形式输出：\ + 1.每种能力占一行 \ + 2.每一行内，以能力：能力说明的形式输出。例如：\ + [例子] 2. 情景分析：在特定的情境中应用逻辑和知识。".format(sample_str='') + +sample_gen_ability_prompt = "你能出一道题，用来检测{ability}的能力吗？要带上答案。\n\ + 注意；要以这样的格式输出：即：题目：...。\n ### \n 答案：...。换言之，仅输出题目和答案，不要输出额外文字。题目与答案之间以'###'分隔。\n\ + 注意：你要出一道{ques_type}题。\n\ + 注意：题目要{difficulty}。".format(ability='', ques_type='', difficulty='') + + +single_language_imitate_ques_prompt = "现在有一系列{task}任务的样例，例如：{sample_str} \n \ + 你能仿照这些样例，生成一些新的样例吗？\n \ + 注意：命令的形式要丰富多样。\n \ + 注意：命令的问法要丰富多样。\n\ + 注意：命令的位置可以多样，可以在文本之前或者之后。\n \ + 注意：不要仅仅局限于提供的样例，要利用你的知识，使得题目的范围，和检验的能力尽量广泛。\n\ + 注意：不要输出额外的文本。\n\ + 注意：题目要以{language_1}形式出现。\n\ + 注意：要以这样的形式输出，即每道题前面用一个数字标号标记，例如，每一道题目前面要加上标号，例如1., 2., ...。".format(task='', sample_str='', language_1='') + +multi_language_imitate_ques_prompt = "现在有一系列{task}任务的样例，例如：{sample_str} \n \ + 你能仿照这些样例，生成一些新的样例吗？\n \ + 注意：命令的形式要丰富多样。\n \ + 注意：命令的问法要丰富多样。\n\ + 注意：命令的位置可以多样，可以在文本之前或者之后。\n \ + 注意：不要仅仅局限于提供的样例，要利用你的知识，使得题目的范围，和检验的能力尽量广泛。\n\ + 注意：不要输出额外的文本。\n\ + 注意：要考虑到语言多样性，既要有{language_1}的题目，也要有{language_2}等语言的题目。\n\ + 注意：要以这样的形式输出，即每道题前面用一个数字标号标记，例如，每一道题目前面要加上标号，例如1., 2., ...。".format(task='', sample_str='', language_1='', language_2='') + + +ceveat_precision_prompt = '注意：仅输出结果即可，不要输出额外的文本。' + +ceveat_unanswerable_prompt = '注意：对于任务信息不足，致使无法回答的情况，请你不要输出任何内容。' + +ability_gen_prompt = "{task}是一个重要的任务。这一任务需要一系列的能力。请根据你对于{task}所需能力的理解，出5道题目，以检测一个人完成{task}的部分能力。\n\ + 注意：这五道题不要求面面俱到，可以仅针对完成{task}所需的一小类特定能力进行检验。\n\ + 注意：题目要具体，且有操作性。例如，不能笼统地询问“阅读一首英文诗歌，并解释其主题和情感。”，而要给出诗歌是什么，并且，不能仅给出题目。\n\ + 注意：不要出和视频(电视、电影)、音频(广播)、图片有关系的题目。\n\ + 注意：你的问法必须灵活多样。\n\ + 注意：不要生成除了题目之外的额外文字。\n\ + 注意：每一道题目前面要加上标号，例如1., 2., ...。".format(task='') + +ability_gen_exclude_sample_prompt = "{task}是一个重要的任务。这一任务需要一系列的能力。请根据你对于{task}所需能力的理解，出5道题目，以检测一个人完成{task}的部分能力。\n\ + 注意：这五道题不要求面面俱到，可以仅针对完成{task}所需的一小类特定能力进行检验。\n\ + 注意：在出题时，要尽量避免出类似于如下这几道题的所针对的内容和知识，以及所检验的能力。{sample_str} \n\ + 注意：题目要具体，且有操作性。例如，不能笼统地询问“阅读一首英文诗歌，并解释其主题和情感。”，而要给出诗歌是什么，并且，不能仅给出题目。\n\ + 注意：不要出和视频(电视、电影)、音频(广播)、图片有关系的题目。\n\ + 注意：你的问法必须灵活多样。\n\ + 注意：不要生成除了题目之外的额外文字。\n\ + 注意：每一道题目前面要加上标号，例如1., 2., ...。".format(task='', sample_str='') diff --git a/gpt4_data_gen/strategy.py b/gpt4_data_gen/strategy.py new file mode 100644 index 0000000..861625e --- /dev/null +++ b/gpt4_data_gen/strategy.py @@ -0,0 +1,174 @@ +import random +import re +from tqdm import tqdm +from utils import get_gpt4_response, clean_patterns +from prompt_templates import ability_extraction_prompt, sample_gen_ability_prompt, single_language_imitate_ques_prompt, multi_language_imitate_ques_prompt, ceveat_precision_prompt,ceveat_unanswerable_prompt, ability_gen_prompt, ability_gen_exclude_sample_prompt + + +def clean(response): + ques = response.split('\n') + ques = [re.sub(r'\d+\.\s+', '', i) for i in ques] + ques = [re.sub(r'\d+\.', '', i) for i in ques] + ques = [i.replace('\\', '') for i in ques] + ques = [i.strip('\n') for i in ques] + ques = [i for i in ques if len(i) > 5] + return ques + +class ImitateGenerator(): + def __init__(self, example_ls, additional_ceveat = '', multi_language=False): + self.example_ls = example_ls + if multi_language: + self.template_ques = single_language_imitate_ques_prompt + else: + self.template_ques = multi_language_imitate_ques_prompt + self.template_ques = self.template_ques + '\n' + additional_ceveat + self.multi_language = multi_language + + def gen_ques(self, num_samples, task, language, model='gpt4'): + + if self.multi_language: + language_1 = language[0] + language_2 = ', '.join(language[1:]) + else: + language_1 = language_1 if isinstance(language, str) else language[0] + ques_ls = [] + for i in tqdm(num_samples): + n = random.randint(3,5) + sample_tmp = random.sample(self.example_ls, n) + sample_str = '\n'.join(sample_tmp) + if self.multi_language: + template_ques = self.template_ques.format(task=task, sample_str=sample_str, language_1=language_1, language_2=language_2) + else: + template_ques = self.template_ques.format(task=task, sample_str=sample_str, language_1=language_1) + response = get_gpt4_response(template_ques, model) + ques = clean(response) + ques_ls.extend(ques) + + self.ques_ls = ques_ls + return ques_ls + + def gen_ans(self, model='gpt4'): + samples_gen = [] + num_fail = 0 + for ques in self.ques_ls: + ans = get_gpt4_response(ques, model) + + if ans == '' or '抱歉' in ans: + num_fail += 1 + else: + ans = ans.strip('\n').strip() + samples_gen.append([{'role':'user', 'content': ques},{'role':'assistant', 'content':ans}]) + print(f"Total num: {str(len(self.ques_ls))}, Failed num: {str(num_fail)}") + self.samples_gen = samples_gen + return samples_gen + + +class AbilityExtractionGenerator(): + def __init__(self, example_ls, additional_ceveat = ''): + self.example_ls = example_ls + + self.template_ability_extraction = ability_extraction_prompt + '\n' + additional_ceveat[0] + self.template_sample_gen = sample_gen_ability_prompt + '\n' + additional_ceveat[1] + + def ability_extraction(self, num_examples, model='gpt4'): + + ability_set = set() + ability_ls = [] + for i in tqdm(num_examples): + n = random.randint(3,5) + sample_tmp = random.sample(self.example_ls, n) + sample_str = '\n'.join(sample_tmp) + template_ability_extraction = self.template_ability_extraction.format(sample_str=sample_str) + response = get_gpt4_response(template_ability_extraction, model) + abilities = clean(response) + + for ability in abilities: + ability_name = ability.split("：")[0].split(":")[0].strip() + if ability_name not in ability_set: + ability_set.add(ability_name) + ability_ls.append(ability) + + self.ability_ls = ability_ls + return ability_ls + + def gen_sample(self, num_samples, model='gpt4'): + samples_gen_ori = [] + num_fail = 0 + for i in tqdm(range(num_samples)): + ability = random.sample(self.ability_ls, 1)[0] + ability = ability.strip() + indicator = random.random() + if indicator < 0.2: + ques_type = "选择" + else: + ques_type = "简答" + + indicator = random.random() + if indicator < 0.15: + difficulty = '偏难：4步以上推理才能得到答案' + elif indicator < 0.6: + difficulty = '适中：2-3步推理即可得到答案' + else: + difficulty = '简单：1-2步推理即可得到答案' + template_sample_gen = self.template_sample_gen.format(ability=ability, ques_type=ques_type, difficulty=difficulty) + + sample = get_gpt4_response(template_sample_gen, model) + samples_gen_ori.append(sample) + + samples_gen = [] + num_fail = 0 + patterns = ['题目：', '答案：'] + for sample in samples_gen_ori: + try: + ques, ans = sample['response'].split('###') + ques = clean_patterns(ques.strip('\n'), patterns) + ans = clean_patterns(ans.strip('\n'), patterns) + samples_gen.append([{'role':'user', 'content': ques},{'role':'assistant', 'content':ans}]) + except: + num_fail += 1 + + print(f"Total num: {str(len(self.ques_ls))}, Failed num: {str(num_fail)}") + self.samples_gen = samples_gen + return samples_gen + + +class AbilityDirectGenerator(): + def __init__(self, example_ls, additional_ceveat = '', exclude_prev=True) -> None: + self.example_ls = example_ls + self.exclude_prev = exclude_prev + + self.ability_gen_exclude_prompt = ability_gen_exclude_sample_prompt + '\n' + additional_ceveat + self.ability_gen_prompt = self.ability_gen_prompt + '\n' + additional_ceveat + + def gen_ques(self, num_examples, task, num_exclude=500, model='gpt4'): + + ques_ls = [] + for i in tqdm(range(num_examples)): + if len(ques_ls) > num_exclude and self.exclude_prev: + n = random.randint(3,5) + sample_tmp = random.sample(self.example_ls, n) + sample_str = '\n'.join(sample_tmp) + ability_gen_prompt = self.ability_gen_exclude_prompt.format(task=task, sample_str=sample_str) + else: + ability_gen_prompt = self.ability_gen_prompt.format(task=task) + response = get_gpt4_response(ability_gen_prompt, model) + ques = clean(response) + ques_ls.extend(ques) + + return ques_ls + + def gen_ans(self, model='gpt4'): + samples_gen = [] + num_fail = 0 + for ques in self.ques_ls: + ans = get_gpt4_response(ques, model) + + if ans == '' or '抱歉' in ans: + num_fail += 1 + else: + ans = ans.strip('\n').strip() + samples_gen.append([{'role':'user', 'content': ques},{'role':'assistant', 'content':ans}]) + print(f"Total num: {str(len(self.ques_ls))}, Failed num: {str(num_fail)}") + self.samples_gen = samples_gen + return samples_gen + diff --git a/gpt4_data_gen/utils.py b/gpt4_data_gen/utils.py new file mode 100644 index 0000000..d13cfeb --- /dev/null +++ b/gpt4_data_gen/utils.py @@ -0,0 +1,30 @@ +import os +import json +import openai + +def load_jsonl(file_path): + dat = open(file_path, 'r').readlines() + dat = [json.loads(i) for i in dat] + return dat + + +def save_jsonl(sample_ls, save_path): + with open(save_path, 'w', encoding='utf-8') as f: + for ipt in sample_ls: + json_str = json.dumps(ipt, ensure_ascii=False) + f.write(json_str + '\n') + +def clean_patterns(txt, patterns): + for pattern in patterns: + txt = txt.replace(pattern, '') + return txt + +def get_gpt4_response(query, deployment_name='gpt4'): + response = openai.ChatCompletion.create( + engine=deployment_name, # engine = "deployment_name". + messages=[ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": query} + ] + ) + return response['choices'][0]['message']['content'] \ No newline at end of file From d945b42829dcbe1717680e9c8cf79952b9937719 Mon Sep 17 00:00:00 2001 From: wuchengwei Date: Mon, 11 Dec 2023 00:53:40 +0800 Subject: [PATCH 03/38] new functions --- all2txt/pdf2txt.py | 138 +++---- deduplication/README.md | 243 ++++++++++++ deduplication/addTwoDigits.py | 12 + deduplication/minhash.py | 493 ++++++++++++++++++++++++ deduplication/udf_spark_addTwoDigits.py | 49 +++ language_identification/readMe.md | 7 + sft/draw_pie_chart.py | 18 + 7 files changed, 875 insertions(+), 85 deletions(-) create mode 100644 deduplication/README.md create mode 100644 deduplication/addTwoDigits.py create mode 100644 deduplication/minhash.py create mode 100644 deduplication/udf_spark_addTwoDigits.py create mode 100644 sft/draw_pie_chart.py diff --git a/all2txt/pdf2txt.py b/all2txt/pdf2txt.py index 543a0fc..4c14997 100644 --- a/all2txt/pdf2txt.py +++ b/all2txt/pdf2txt.py @@ -1,92 +1,60 @@ -from unstructured.partition.auto import partition +from unstructured.partition.pdf import partition_pdf +import argparse -# -# # -# # elements = partition(filename="/Users/wuchengwei/Documents/privateText/人大/LLMQA/lunwen/LLMQA/2004.04906.pdf") -# # print("\n\n".join([str(el) for el in elements])) -# -# -# from unstructured.partition.pdf import partition_pdf -# -# # Returns a List[Element] present in the pages of the parsed pdf document -# # elements = partition_pdf("/Users/wuchengwei/Documents/privateText/人大/LLMQA/lunwen/LLMQA/2004.04906.pdf") -# # print(elements) -# # print("\n\n".join([str(el) for el in elements])) -# -# -# # from unstructured.documents.elements import NarrativeText -# # from unstructured.partition.text_type import sentence_count -# # -# # for element in elements[:100]: -# # if isinstance(element, NarrativeText) and sentence_count(element.text) > 2: -# # print(element) -# # print("\n") +def pdf2txt(input_path, process_all): + elements = partition_pdf(filename=input_path, infer_table_structure=True, strategy="hi_res") + all_file = None + + if process_all: + # 创建一个空字典，用于存储不同类别的元素列表 + tables = {} + + # 定义要筛选的类别列表 + categories = [ + "Table", "FigureCaption", "NarrativeText", "ListItem", "Title", "Address", + "PageBreak", "Header", "Footer", "UncategorizedText", "Image", "Formula" + ] + + # 遍历每个类别，筛选出对应类别的元素并存储到字典中 + for category in categories: + # 检查是否为 "Table" 类别，需要分别处理 "table.metadata.text_as_html" + if category == "Table": + tables[category] = [el for el in elements if el.category == category] + # 另外存储 "table.metadata.text_as_html" + tables["Table_text_as_html"] = [el.table.metadata.text_as_html for el in elements if + el.category == category] + else: + # 对于其他类别，直接存储筛选的元素 + tables[category] = [el for el in elements if el.category == category] + all_file = tables + else: + print(elements) + all_file = "\n\n".join([str(el) for el in elements]) + + return all_file -from unstructured.partition.pdf import partition_pdf -# -filename = "/Users/wuchengwei/Documents/privateText/人大/LLMQA/lunwen/LLMQA/2004.04906.pdf" -# -elements = partition_pdf(filename=filename, infer_table_structure=True, strategy="hi_res") -# print(elements) -# print("\n\n".join([str(el) for el in elements])) +if __name__ == '__main__': + parser = argparse.ArgumentParser(description="pdf2txt") + parser.add_argument("--input", "-i", type=str, required=True, help="input Catalogue path") + parser.add_argument("--output", "-o", type=str, required=True, help="Output directory") -tables = [el for el in elements if el.category == "Table"] -print("===tables===>" + str(tables)) -for table in tables: - print(table) - print(table.metadata.text_as_html) - # print(table[0].metadata.text_as_html) -tables_FigureCaption = [el for el in elements if el.category == "FigureCaption"] -tables_NarrativeText = [el for el in elements if el.category == "NarrativeText"] -tables_ListItem = [el for el in elements if el.category == "ListItem"] -tables_Title = [el for el in elements if el.category == "Title"] -tables_Address = [el for el in elements if el.category == "Address"] -tables_PageBreak = [el for el in elements if el.category == "PageBreak"] -tables_Header = [el for el in elements if el.category == "Header"] -tables_Footer = [el for el in elements if el.category == "Footer"] -tables_UncategorizedText = [el for el in elements if el.category == "UncategorizedText"] -tables_Image = [el for el in elements if el.category == "Image"] -tables_Formula = [el for el in elements if el.category == "Formula"] -print("===tables_FigureCaption===>" + str(tables_FigureCaption)) -for FigureCaption in tables_FigureCaption: - print(FigureCaption) -print("===tables_NarrativeText===>" + str(tables_NarrativeText)) -for NarrativeText in tables_NarrativeText: - print(NarrativeText) -print("===tables_ListItem===>" + str(tables_ListItem)) -for ListItem in tables_ListItem: - print(ListItem) -print("===tables_Title===>" + str(tables_Title)) -for Title in tables_Title: - print(Title) -print("===tables_Address===>" + str(tables_Address)) -for Address in tables_Address: - print(Address) -print("===tables_PageBreak===>" + str(tables_PageBreak)) -for PageBreak in tables_PageBreak: - print(PageBreak) -print("===tables_Header===>" + str(tables_Header)) -for Header in tables_Header: - print(Header) -print("===tables_Footer===>" + str(tables_Footer)) -for Footer in tables_Footer: - print(Footer) -print("===tables_UncategorizedText===>" + str(tables_UncategorizedText)) -for UncategorizedText in tables_UncategorizedText: - print(UncategorizedText) -print("===tables_Image===>" + str(tables_Image)) -for Image in tables_Image: - print(Image) -print("===tables_Formula===>" + str(tables_Formula)) -for Formula in tables_Formula: - print(Formula) -# -# print(tables[0].text) -# print(tables[0].metadata.text_as_html) + # 修改 --all 参数为布尔类型，并更改变量名以避免与内置函数冲突 + parser.add_argument("--process_all", action="store_true", + help="process all articles if specified, otherwise split by category list, default is all") -from unstructured.staging.base import elements_to_json, elements_from_json + args = parser.parse_args() + result = pdf2txt(args.input, args.process_all) -# filename = "/Users/wuchengwei/Downloads/code/zhiyuan/wuchengwei_FlagData/FlagData/output/outputs1.json" -# elements_to_json(elements, filename=filename) + # 将结果保存到文件 + output_file_path = args.output # 指定输出文件路径，可以根据实际需求修改 + with open(output_file_path, 'w', encoding='utf-8') as output_file: + if isinstance(result, dict): + # 如果结果是字典类型，将字典内容写入文件 + for key, value in result.items(): + output_file.write(f"Category: {key}\n") + output_file.write(f"{value}\n\n") + else: + # 如果结果不是字典，直接写入文件 + output_file.write(result) diff --git a/deduplication/README.md b/deduplication/README.md new file mode 100644 index 0000000..6344122 --- /dev/null +++ b/deduplication/README.md @@ -0,0 +1,243 @@ +下面详细描述了如何使用分布式能力来进行数据去重 +一、Spark standalone集群（1台master2台worker）搭建 +1. 安装jdk +1. 下载jdk包 + 下载地址 https://www.oracle.com/java/technologies/downloads/ +2. 解压 + tar -zxvf ./jdk-8u181-linux-x64.tar.gz +3. 环境配置 + ~/.bashrc文件尾部添加，添加之后source ~/.bashrc生效 + + +```bash +# JAVA_HOME +export JAVA_HOME=/xxx/jdk1.8.0_181 +# PATH +export PATH=$PATH:${JAVA_HOME}/bin +``` + + + + + +4. 成功效果 + java -version显示版本号 +2. 安装spark集群 +1. 下载spark包 + 下载地址 https://spark.apache.org/downloads.html +2. 解压并重命名 + tar -zxvf spark-2.3.1-bin-hadoop2.6.tgz + mv spark-2.3.1-bin-hadoop2.6.tgz spark-2.3.1 +3. 修改配置文件 +4. 配置spark-env.sh + 1. cd spark-2.3.1 + 2. cp spark-env.sh.template spark-env.sh + 3. vim spark-env.sh +```bash +SPARK_MASTER_PORT=7077 #master 服务端口 +SPARK_MASTER_HOST=172.31.32.51 #master 节点ip ifconifg命令查找，ifconfig命令找不到，需要apt install net-tools 安装再执行ifconfig +JAVA_HOME=/data1/jdk-11.0.15.1 #master 节点jdk地址 echo $JAVA_HOME 查找 +PYSPARK_PYTHON=/data1/miniconda3/bin/python #python环境 +PYSPARK_DRIVER_PYTHON=/data1/miniconda3/bin/python +SPARK_MASTER_WEBUI_PORT=50010 #master webUI端口 防止端口冲突lsof -i:50010 +SPARK_WORKER_WEBUI_PORT=50011 #worker webUI端口 +``` + + +使用which python确定路径 +如果没有python环境，推荐conda进行管理： + +```bash +在Linux上安装Miniconda： + +1. 对于Linux系统，使用以下命令下载Miniconda安装脚本： + + +wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh + + +2. 接下来，运行以下命令来执行安装脚本： + + +bash Miniconda3-latest-Linux-x86_64.sh + + +3. 按照安装程序的指示进行安装。按照默认设置进行安装，或者根据需要进行自定义设置。 + +4. 安装完成后，您可能需要激活Miniconda。可以通过执行以下命令来激活Miniconda： +在vim ~/.bashrc尾部添加 +# >>> conda initialize >>> +# !! Contents within this block are managed by 'conda init' !! +__conda_setup="$('/root/miniconda3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)" +if [ $? -eq 0 ]; then + eval "$__conda_setup" +else + if [ -f "/root/miniconda3/etc/profile.d/conda.sh" ]; then + . "/root/miniconda3/etc/profile.d/conda.sh" + else + export PATH="/root/miniconda3/bin:$PATH" + fi +fi +unset __conda_setup +# <<< conda initialize <<< + +source ~/.bashrc + + +5. 现在，验证Miniconda是否成功安装，可以尝试执行以下命令来检查Miniconda的版本： +conda --version + +如果安装成功，是能够看到安装的Miniconda的版本号。 + +安装完成后，可以使用Miniconda来管理您的Python环境和安装各种包和依赖项。 +``` + +5. 配置spark-defaults.conf + 1. cp spark-defaults.conf.template spark-defaults.conf + 2. vim spark-defaults.conf +```bash +spark.master spark://172.31.32.51:7077 #master节点:端口 +spark.driver.host 172.31.32.51 #master节点ip +spark.eventLog.enabled true #开启日志 +spark.eventLog.dir file:///cwwu/spark_setup/spark/eventLog #日志地址 +# spark.serializer org.apache.spark.serializer.KryoSerializer +# spark.executor.instances 5 +spark.driver.memory 32g # Spark应用程序的驱动器内存，拿之前的示例给个参考值 +# spark.executor.memory 340g +# spark.executor.extraJavaOptions -XX:+PrintGCDetails -Dkey=value -Dnumbers="one two three" +spark.executor.extraJavaOptions -Dio.netty.tryReflectionSetAccessible=true #Spark执行器配置Netty网络库以提高网络性能 +spark.network.timeout 10000000 +spark.memory.offHeap.enabled true +spark.memory.offHeap.size 4g #堆外内存 +``` + +6. 配置spark worker节点 + 1. 免密 + 1. ~/.ssh/config + 几个work配置几个，vim /etc/hosts配置本地hosts，输入hostname检验，# ubuntu 不要写成ubuntu + vim ~/.ssh/config + +```bash +Host spark-worker0 + HostName 172.31.0.149 # ifconifg命令查找 + User root + Port 22 # 机器登录端口号 +Host spark-worker1 + HostName 172.xx.x.xxx + User root # ubuntu 不要写成ubuntu + Port 22 +Host spark-master-langchao + HostName 172.31.32.51 + User root + Port 60022 + +Host spark-worker-langchao + HostName 172.xx.x.xxx + User root + Port 22 +``` + 2. 发送免密 + ssh-copy-id -i ~/.ssh/id_rsa.pub spark-worker1【root用户下执行】 +如果master缺少公钥文件。请按照以下步骤检查和生成公钥文件： + +```bash + +1. 首先，检查是否已经存在名为`id_rsa.pub`的公钥文件。可以执行以下命令检查： + ls ~/.ssh/id_rsa.pub + +2. 如果文件不存在，可以使用`ssh-keygen`命令生成新的SSH密钥对。执行以下命令： + ssh-keygen + + 按照提示输入路径和密码等信息，生成新的SSH密钥对。 + +3. 在生成的公钥文件`id_rsa.pub`中复制公钥内容。然后执行`ssh-copy-id`命令将公钥复制到目标主机，确保替换``和``为正确的用户名和远程主机名称： + ssh-copy-id -i ~/.ssh/id_rsa.pub spark-worker1 +``` + +2. 配置workers + cp workers.template workers + vim workers，尾部添加 + spark-master #master节点也可以同时当做worker节点使用 + spark-worker1 +7. 将配置完成的spark包分别发送给各worker节点 + 1. scp -r spark-2.3.1 root@120.92.85.38:/cwwu/spark-standalone（worker启动的地址必须一致不然不能启动）被访问的文件，必须每一个worker都有（共享数据目录/或者hdfs） +3. 启动spark集群 + +```bash +启动master +./sbin/start-master.sh +./sbin/stop-master.sh #关闭master +启动work +./sbin/start-workers.sh +./sbin/stop-worker.sh #关闭work +``` + +4. 访问web +1. work http://120.92.14.245:50010/ +5. 提交spark任务 +1. Standalone提交命令 + + +```bash +cd bin +./spark-submit --master spark://172.31.32.51:7077 --class org.apache.spark.examples.SparkPi ../examples/jars/spark-examples_2.12-3.4.0.jar 10000 +./bin/spark-submit --master spark://172.31.32.51:7077 --class org.apache.spark.examples.SparkPi ./examples/jars/spark-examples_2.12-3.4.0.jar 10000 + +./bin/spark-submit --master spark://172.31.32.51:7077 /cwwu/kenlm/spark_script.py /cwwu/cn/paper_abstract_processed.jsonl /cwwu/spark_test_1105/paper_abstract_output +``` + +scp -r /cwwu/cn/paper_abstract_processed.jsonl root@120.92.85.38:/cwwu/cn + + +./bin/spark-submit --master spark://172.31.67.138:7077 /cwwu/text-process-spark-tool/unified_format/post_processing_unified_format_spark_downfile0.py + +./spark-submit /cwwu/kenlm/spark_script.py /cwwu/cn/paper_abstract_processed.jsonl /cwwu/spark_test_1105/paper_abstract_output +*** + +## 改成去重的例子 + +*** + +master(worker) +worker + +172.31.32.51:43851 +172.31.0.149:27602 + +502.6 GiB 72cores +61.7 GiB 16 cores + +893.8s（14.8min） + +小于60s（小于1min） +2kw行（20424772） +87G + + +二、性能分析 +任务名 +数据描述 +Python脚本 +spark集群 +中文过滤 +paper_abstract_processed.jsonl +100w行 +586M +893.8s（14.8min） + +小于60s（小于1min） + + + +单台400（96核*384G） + + +单台200（112核*512G） + + +三、跳板机 +使用需要使用跳板机链接，多台机器通过跳板机，可以通过隧道方式链接 + +挂载共享数据盘 +sudo su 切换到root用户 +mkdir /cwwu diff --git a/deduplication/addTwoDigits.py b/deduplication/addTwoDigits.py new file mode 100644 index 0000000..9a2490e --- /dev/null +++ b/deduplication/addTwoDigits.py @@ -0,0 +1,12 @@ +import random + + +def add_two_digits(): + num1 = random.randint(1, 9) + num2 = random.randint(1, 9) + sum = num1 + num2 + return num1, num2, sum + + +result = add_two_digits() +print("两位数相加的结果为：", result[0], "+", result[1], "=", result[2]) diff --git a/deduplication/minhash.py b/deduplication/minhash.py new file mode 100644 index 0000000..ed1eba7 --- /dev/null +++ b/deduplication/minhash.py @@ -0,0 +1,493 @@ +import hashlib +import re +import struct +import sys +from logging import Logger +from typing import Tuple, Optional +import numpy as np +from pyspark import SparkConf +from pyspark.sql import SparkSession +from pyspark.sql.types import ArrayType, IntegerType +from pyspark.sql import functions as F +from scipy.integrate import quad as integrate +import ftfy +from itertools import tee +from typing import List +from typing import Text +from transformers import AutoTokenizer + +SEED = 42 +# NON_ALPHA = re.compile("[^A-Za-z_0-9]") +# max_encode_length = 1024 +RNG = np.random.RandomState(SEED) +MAX_HASH = np.uint64((1 << 32) - 1) +MERSENNE_PRIME = np.uint64((1 << 61) - 1) + + +def get_ngrams(sequence: List[Text], n: int, min_length: int = 5, chunk_length: Optional[int] = None): + """ + Return the ngrams generated from a sequence of items, as an iterator. This is a modified version of nltk.util.ngrams. + + Parameters + ---------- + sequence : List[Text] + The sequence of items. + n : int + The length of each ngram. + min_length : int, optional + The minimum length of each ngram, by default 5 + + Returns + ------- + iterator + The ngrams. + + Examples + -------- + >>> list(ngrams(["a", "b", "c", "d"], 2, min_length=1)) + [('a', 'b'), ('b', 'c'), ('c', 'd')] + >>> list(ngrams(["a", "b", "c", "d"], 2, min_length=5)) + [] + >>> list(ngrams(["a", "b"], 3, min_length=1)) + [('a', 'b')] + """ + if len(sequence) < min_length: + return [] + if chunk_length is not None: + sequence = sequence[:chunk_length] + if len(sequence) < n: + return [tuple(sequence)] + iterables = tee(iter(sequence), n) + for i, sub_iterable in enumerate(iterables): + for _ in range(i): + next(sub_iterable, None) + return zip(*iterables) + + +def bc_tokenize(seq): + return bc_tokenizer.value.encode(seq) + + +# Connected Components in MapReduce and Beyond +def large_star_map(edge): + return [(edge[0], edge[1]), (edge[1], edge[0])] + + +def large_star_reduce(group): + x, neighbors = group + nodes = [x] + list(neighbors) + minimum = min(nodes) + return [(n, minimum) for n in nodes if n > x] + + +def small_star_map(edge): + x, y = edge + if y <= x: + return (x, y) + else: + return (y, x) + + +def small_star_reduce(group): + x, neighbors = group + nodes = [x] + list(neighbors) + minimum = min(nodes) + return [(n, minimum) for n in nodes if n != minimum] + + +def sha1_hash32(data): + """ + Directly taken from datasketch package to avoid dependency. + + Parameters + ---------- + data : bytes + + Returns + ------- + int + The first 4 bytes (32 bits) of the SHA1 hash of the input data. + + Examples + -------- + >>> sha1_hash32(b"hello") + 499578026 + >>> bin(sha1_hash32(b"hello")) + '0b11101110001101111010010101010' + >>> sha1_hash32(b"hello world").bit_length() + 30 + """ + return struct.unpack(" List[Tuple[int, bytes, int]]: + """ + Generate the MinHashLSH values for a given document. + + Parameters + ---------- + tokens : list + The token ids of the document. + idx : int + The index of the document. + num_perm : int + The number of permutations. + ngram_size : int + The size of the n-grams. + min_length : int + The minimum number of tokens in a document. + hashranges : list + The ranges of offsets for each hash value. + permutations : np.ndarray + The permutations for the hash values. + + Returns + ------- + List[Tuple[int, bytes, int]] + The list of (band_idx, hash value, idx) for the document. + + Examples + -------- + >>> content = "hello world" + >>> idx = 0 + >>> num_perm = 250 + >>> ngram_size = 1 + >>> hashranges = [(i, i + 25) for i in range(0, 250, 25)] + >>> PERMUTATIONS = np.array( + ... [ + ... ( + ... RNG.randint(1, MERSENNE_PRIME, dtype=np.uint64), + ... RNG.randint(0, MERSENNE_PRIME, dtype=np.uint64), + ... ) + ... for _ in range(num_perm) + ... ], + ... dtype=np.uint64, + ... ).T + >>> res = generate_hash_values(content, idx, num_perm, ngram_size, 0, hashranges, PERMUTATIONS) + >>> len(res) + 10 + """ + hashvalues = np.ones(num_perm, dtype=np.uint64) * MAX_HASH + tokens = list(map(str, tokens)) + ngrams = {" ".join(t) for t in get_ngrams(tokens, ngram_size, min_length)} + hv = np.array([sha1_hash32(ngram.encode("utf-8")) for ngram in ngrams], dtype=np.uint64) + a, b = permutations + phv = np.bitwise_and(((hv * np.tile(a, (len(hv), 1)).T).T + b) % MERSENNE_PRIME, MAX_HASH) + hashvalues = np.vstack([phv, hashvalues]).min(axis=0) + Hs = [bytes(hashvalues[start:end].byteswap().data) for start, end in hashranges] + return [(band_idx, H, idx) for band_idx, H in enumerate(Hs)] + + +def optimal_param( + threshold: float, + num_perm: int, + false_positive_weight: float = 0.5, + false_negative_weight: float = 0.5, +): + """ + Compute the optimal `MinHashLSH` parameter that minimizes the weighted sum + of probabilities of false positive and false negative, taken from datasketch. + + Parameters + ---------- + threshold : float + The threshold for similarity. + num_perm : int + The number of permutations. + false_positive_weight : float + The weight of false positive. + false_negative_weight : float + The weight of false negative. + + Returns + ------- + Tuple[int, int] + The optimal `b` and `r` parameters. + The number of bands, and the number of rows per band respectively. + + Examples + -------- + >>> optimal_param(0.7, 256) + (25, 10) + """ + + def false_positive_area(threshold: float, b: int, r: int): + """Source: `datasketch.lsh`""" + + def area(s): + return 1 - (1 - s ** float(r)) ** float(b) + + a, _ = integrate(area, 0.0, threshold) + return a + + def false_negative_area(threshold: float, b: int, r: int): + """Source: `datasketch.lsh`""" + + def area(s): + return 1 - (1 - (1 - s ** float(r)) ** float(b)) + + a, _ = integrate(area, threshold, 1.0) + return a + + min_error = float("inf") + opt = (0, 0) + for b in range(1, num_perm + 1): + max_r = int(num_perm / b) + for r in range(1, max_r + 1): + fp = false_positive_area(threshold, b, r) + fn = false_negative_area(threshold, b, r) + error = fp * false_positive_weight + fn * false_negative_weight + if error < min_error: + min_error = error + opt = (b, r) + return opt + + +def generate_edges(nodes: List[int]) -> List[Tuple[int, int]]: + """ + Generate edges from a cluster. Instead of generating N^2 edges, we only need all nodes align to a single node, since + we will be running connected components on the edges later. + + Parameters + ---------- + nodes : List[int] + The list of nodes in the cluster. + + Returns + ------- + List[Tuple[int, int]] + The list of edges. + + Examples + -------- + >>> generate_edges([1, 2, 3]) + [(2, 1), (3, 1)] + """ + if len(nodes) <= 1: + return [] + + min_node = min(nodes) + return [(n, min_node) for n in nodes if n != min_node] + + +def fix_line_breaks(text: str): + text = re.sub("(?<=[。；？：:\?”\)）!！])\s+(?=[\(（0-9\u4e00-\u9fa5])", r"\n", text).strip() + return text + + +def fix_cite(text: str): + regex_pattern = "(https?|ftp|file)://[-A-Za-z0-9+&@#/%?=~_|!:,.;]+[-A-Za-z0-9+&@#/%=~_|]\s*$" + if re.search(regex_pattern, text[-100:]): + return "" + else: + return text + + +def fix_short_passage(text: str): + if len(text) < 100: + return "" + else: + return text + + +if __name__ == "__main__": # pragma: no cover + + import argparse + + # pythia: applying near-deduplication with MinHashLSH and a threshold of 0.87 + # OPT: MinhashLSH with a Jaccard similarity ≥ .95. + # Deduplicating Training Data Makes Language Models Better: + # a collision at the desired Jaccard index threshold of 0.8 had a high probability of occurring. + parser = argparse.ArgumentParser(description="Near-deduplicating Jsonl with PySpark") + parser.add_argument("--input", type=str, required=True, help="input file to deduplicate") + parser.add_argument("--file-format", type=str, default="jsonl") + parser.add_argument("--threshold", type=float, default=0.87, help="Similarity threshold") + parser.add_argument("--ngram_size", type=int, default=5, help="N-gram size") + parser.add_argument("--min_length", type=int, default=5, help="Minimum length of document to be considered") + parser.add_argument("--num_perm", type=int, default=256, help="Number of permutations") + parser.add_argument("--b", type=int, default=None, help="Number of bands") + parser.add_argument("--r", type=int, default=None, help="Number of rows per band") + parser.add_argument("--column", "-c", type=str, default="content", help="Column to deduplicate") + parser.add_argument("--output", "-o", type=str, required=True, help="Output directory") + parser.add_argument("--shuffle", action="store_true") + parser.add_argument("--deduplicate", action="store_true") + parser.add_argument("--tokenize", action="store_true") + parser.add_argument("--fixtext", action="store_true") + parser.add_argument("--fix-line-break", action="store_true") + parser.add_argument("--fix-cite", action="store_true") + parser.add_argument("--fix-short-passage", action="store_true") + args = parser.parse_args() + + conf = SparkConf() + conf.set("spark.sql.hive.convertMetastoreParquet.mergeSchema", "false") + conf.set("parquet.enable.summary-metadata", "false") + conf.set("spark.app.name", "TextProcess") + conf.set("spark.debug.maxToStringFields", "100") + conf.set("spark.eventLog.enabled", "true") + conf.set("spark.executor.memory", "456G") + conf.set("spark.memory.fraction", "0.6") + # conf.set("spark.shuffle.file.buffer", "1M") + conf.set("spark.sql.shuffle.partitions", "600") + # conf.set("spark.sql.shuffle.partitions", "200") + conf.set("spark.sql.broadcastTimeout", "600") + conf.set("spark.sql.autoBroadcastJoinThreshold", "-1") # 表示禁用 + conf.set("spark.dynamicAllocation.enabled", "true") + conf.set("spark.sql.adaptive.enabled", "true") + conf.set("spark.sql.adaptive.skewJoin.enabled", "true") + conf.set("spark.sql.adaptive.coalescePartitions.enabled", "true") + conf.set("spark.sql.adaptive.localShuffleReader.enabled", "true") + # conf.set("spark.file.transferTo", "false") + # conf.set("spark.shuffle.unsafe.file.output.buffer", "1M") + # conf.set("spark.shuffle.mapStatus.compression.codec", "lz4") + # conf.set("spark.io.compression.lz4.blocksize", "512K") + conf.set("spark.network.timeout", "10000000") + conf.set("spark.shuffle.registration.timeout", "10000000") + conf.set("spark.executor.cores", "100") + conf.set("spark.shuffle.service.enabled", "false") + # conf.set("spark.cores.max", 960) + conf.set("spark.history.fs.logDirectory", "file:///cwwu/test_spark_data/spark_events") + conf.set("spark.eventLog.dir", "file:///cwwu/test_spark_data/spark_events") + conf.set("spark.executor.allowSparkContext", "true") + conf.set("spark.driver.maxResultSize", "5120m") + spark = SparkSession.builder.config(conf=conf).getOrCreate() + log: Logger = spark.sparkContext._jvm.org.apache.log4j.LogManager.getLogger(__name__) # type: ignore + + log.info(f"spark config: {spark.sparkContext.getConf().getAll()}") + + if args.b is None or args.r is None: + B, R = optimal_param(args.threshold, args.num_perm) + log.info(f"Using optimal parameters: {B=}, {R=}") + else: + B, R = args.b, args.r + + HASH_RANGES = [(i * R, (i + 1) * R) for i in range(B)] + PERMUTATIONS = np.array( + [ + ( + RNG.randint(1, MERSENNE_PRIME, dtype=np.uint64), + RNG.randint(0, MERSENNE_PRIME, dtype=np.uint64), + ) + for _ in range(args.num_perm) + ], + dtype=np.uint64, + ).T + + # init tokenizer + tokenizer = AutoTokenizer.from_pretrained("/cwwu/models/BAAI_AquilaChat-7B") + # todo 可优化 + bc_tokenizer = spark.sparkContext.broadcast(tokenizer) + tokenize_udf = F.udf(bc_tokenize, returnType=ArrayType(IntegerType())) + + fix_text_udf = F.udf(lambda x: ftfy.fix_text_segment(x).strip() if x else "") + fix_line_breaks_udf = F.udf(lambda x: fix_line_breaks(x)) + fix_cite_udf = F.udf(lambda x: fix_cite(x)) + fix_short_passage_udf = F.udf(lambda x: fix_short_passage(x)) + log.info("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!start reading for local files.....") + + if args.file_format == "jsonl": + df = spark.read.json(args.input) + elif args.file_format == "parquet": + df = spark.read.parquet(args.input) + else: + raise NotImplementedError() + log.info("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!read data finished.....") + # df.withColumn: Returns a new DataFrame by adding a column or replacing the existing column that has the same name. + # make a new col with ids increase monotonically then Yields and caches the current DataFrame. + + if args.shuffle: + print("do shuffle") + shuf_seed = 2023 + df = df.orderBy(F.rand(seed=shuf_seed)) + if args.fixtext: + df = df.withColumn(args.column, fix_text_udf(F.col(args.column))) + if args.fix_line_break: + df = df.withColumn(args.column, fix_line_breaks_udf(F.col(args.column))) + if args.fix_cite: + df = df.withColumn(args.column, fix_cite_udf(F.col(args.column))) + if args.fix_short_passage: + df = df.withColumn(args.column, fix_short_passage_udf(F.col(args.column))) + # assert (args.deduplicate and args.tokenize) or not args.deduplicate, "dedup requires tokenization applied" + # filter out empty rows + df = df.filter(f'{args.column} != ""') + # todo:删除input_ids字段 + if args.tokenize: + df = df.withColumn("input_ids", tokenize_udf(F.col(args.column))) + # df.show() # debug only + # df.rdd: A Resilient Distributed Dataset (RDD), the basic abstraction in Spark. + # Represents an immutable, partitioned collection of elements that can be operated on in parallel. + # df.select: Projects a set of expressions and returns a new DataFrame. + if args.deduplicate: + log.info("start deduplication ...") + df = df.withColumn("__id__", F.monotonically_increasing_id()).cache() + records = df.select("__id__", "input_ids").rdd # here we select two cols + # rdd.repartition: Returns a new DataFrame partitioned by the given partitioning expressions. + records = records.repartition(args.num_perm * 2).cache() + + # rdd.flatMap: Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results. + edges = ( + records.flatMap( + lambda x: generate_hash_values( + tokens=x[1], # gen hash with + idx=x[0], # __id__ + num_perm=args.num_perm, + ngram_size=args.ngram_size, + min_length=args.min_length, + hashranges=HASH_RANGES, + permutations=PERMUTATIONS, + ) + ) + .groupBy(lambda x: (x[0], x[1])) + .flatMap(lambda x: generate_edges([i[2] for i in x[1]])) + .distinct() # Returns a new DataFrame containing the distinct rows in this DataFrame. + .cache() + ) + + a = edges + while True: + b = a.flatMap(large_star_map).groupByKey().flatMap(large_star_reduce).distinct().cache() + a = b.map(small_star_map).groupByKey().flatMap(small_star_reduce).distinct().cache() + changes = a.subtract(b).union(b.subtract(a)).collect() + if len(changes) == 0: + break + + results = a.collect() # collect is an action + + # a, b, edges will not be used again + try: + a.unpersist() + b.unpersist() + edges.unpersist() + print("successfully unpersist useless rdd") + except Exception as e: + print("skip unpersist due to: ", e) + pass + + log.info("collect results succeed") + if len(results) == 0: + log.info("No duplicate components found.") + df = df.drop("__id__").cache() + df.write.parquet(args.output, mode="overwrite") + sys.exit(0) + + components = spark.createDataFrame(results, schema=["__id__", "component"]).sort(["component", "__id__"]) + # components.show() + df = df.join(components, on="__id__", how="left") + df = df.filter(F.col("component").isNull()).drop("__id__", "component").cache() + log.info("dedup finished...") + log.info("write components to files") + df.write.json(args.output, mode="overwrite") + # df.write.parquet(args.output, mode="overwrite") diff --git a/deduplication/udf_spark_addTwoDigits.py b/deduplication/udf_spark_addTwoDigits.py new file mode 100644 index 0000000..bc99d26 --- /dev/null +++ b/deduplication/udf_spark_addTwoDigits.py @@ -0,0 +1,49 @@ +from pyspark.sql import SparkSession +from pyspark.sql.functions import udf +from pyspark.sql.types import StructType, StructField, IntegerType +import random + +# Initialize Spark session +spark = SparkSession.builder.appName("AddTwoDigits").getOrCreate() + + +# Function to add two digits +def add_two_digits(): + num1 = random.randint(1, 9) + num2 = random.randint(1, 9) + return num1, num2, num1 + num2 + + +# Create a UDF from the function +add_two_digits_udf = udf(add_two_digits, returnType=StructType([ + StructField("num1", IntegerType(), False), + StructField("num2", IntegerType(), False), + StructField("sum", IntegerType(), False) +])) + +# Create a sample DataFrame with a dummy column +data = [(1,), (2,), (3,), (4,), (5,)] +df = spark.createDataFrame(data, ["dummy"]) + +# Apply the UDF to the DataFrame +result_df = df.withColumn("result", add_two_digits_udf()) + +# Show the result +result_df.show(truncate=False) + +# Stop Spark session +spark.stop() + +""" +很多时候，我们想使用spark的分布式处理数据能力，这里提供了一个普通函数改造成spark udf函数，进而使用spark能力的方法， +但是对于想要改造成spark任务的函数需要满足： +1、 数据并行性：函数的输入数据可以划分为多个部分并进行并行处理。 +2、可序列化和不可变性：Spark 中的函数必须是可序列化的，以便在不同节点上传输。 +3、不依赖于特定计算节点：函数的执行不依赖于特定节点的计算资源或数据存储位置，以便能够在集群中的任何节点上执行。 +4、无状态或可共享状态：函数不依赖于外部状态或只依赖于可共享的状态。这样可以确保在不同计算节点上并行执行函数时不会发生冲突或竞争条件。 + + + 在使用 UDF 时，应该考虑性能和优化。有些函数可能在本地 Python 环境中运行良好，但在分布式 Spark 环境中可能效率不高。 + 对于复杂的逻辑或需要大量内存的函数，可能需要进一步的优化和考虑。UDF 是为了简单的逻辑和数据处理而设计的，对于更复杂的计算，可能需要使用 Spark 的原生算子来进行处理。 + +""" diff --git a/language_identification/readMe.md b/language_identification/readMe.md index e69de29..3645df4 100644 --- a/language_identification/readMe.md +++ b/language_identification/readMe.md @@ -0,0 +1,7 @@ +LID 代表 Language identification，是语言识别的模型。 + +使用 fastText 的语言分类器来做分类，fastText 的语言分类器是在 Wikipedia、Tatoeba、SETimes 上面训练的，使用了 n-grams 来作为特征，使用了层级的 softmax。支持 176 种语言的分类，并且最后会输出一个 0~1 的分数。 + +每个 CPU 核心上，每秒可以处理一千个文档。 + +对于每一个网页做一次语言分类，得到分类的分数，如果大于 0.5，那么就分类为某个特定的语言，否则表示不确定是什么语言的网页并丢掉这个网页。 \ No newline at end of file diff --git a/sft/draw_pie_chart.py b/sft/draw_pie_chart.py new file mode 100644 index 0000000..e087265 --- /dev/null +++ b/sft/draw_pie_chart.py @@ -0,0 +1,18 @@ +import matplotlib.pyplot as plt + +# 给定的数据列表 +x = ['people', 'geography', 'history', 'science and technology', 'culture', 'nature', 'sports', 'economy', 'politics', + 'society', 'military category', 'Religion', 'Health', 'Education', 'Law', 'Entertainment', 'Food', 'Tourism', + 'Agriculture', 'Environment'] +y = [2.97, 13.51, 3.42, 24.84, 10.26, 4.12, 3.75, 7.40, 4.52, 1.41, 0.82, 0.65, 2.97, 3.86, 1.66, 7.66, 3.93, 0.95, + 1.03, 0.28] + +# 创建饼图 +plt.figure(figsize=(8, 8)) # 设置图表大小 +plt.pie(y, labels=x, autopct='%1.1f%%', startangle=140) +plt.title('Pie chart of each category') +plt.axis('equal') # 使饼图保持圆形 + +# 保存饼图为PNG文件 +plt.savefig('pie_chart.png', bbox_inches='tight') # 将文件保存为名为 "pie_chart.png" 的PNG格式文件 +plt.show() From e887291583cc2e5aff3181ab145f81530aa08943 Mon Sep 17 00:00:00 2001 From: wuchengwei Date: Thu, 14 Dec 2023 22:24:02 +0800 Subject: [PATCH 04/38] new functions --- all2txt/outputs1.json | 14074 ---------------- config/distillation_config.yaml | 52 - docs/tutorial_02_analysis.md | 107 - docs/tutorial_03_condensation.md | 91 - docs/tutorial_04_text_annotation.md | 76 - docs/tutorial_05_image_annotation.md | 82 - flagdata/all2txt/README.md | 9 + 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rename {language_identification => flagdata/language_identification}/jsonql.py (100%) rename {language_identification => flagdata/language_identification}/readMe.md (100%) rename {language_identification => flagdata/language_identification}/split_by_lang.py (70%) delete mode 100644 quickstart/analysis/run_analysis.py delete mode 100644 quickstart/condensation/run_distillation.py delete mode 100644 tests/test_analysis.py diff --git a/all2txt/outputs1.json b/all2txt/outputs1.json deleted file mode 100644 index e78570d..0000000 --- a/all2txt/outputs1.json +++ /dev/null @@ -1,14074 +0,0 @@ -[ - { - "element_id": "bf952612b8f83c3ffa49ce97e0f9ddfa", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 16.3, - 263.2 - ], - [ - 16.3, - 303.2 - ], - [ - 36.3, - 303.2 - ], - [ - 36.3, - 263.2 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": 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"eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 1, - "parent_id": "9aeac2fae108ceb3fd175c44cf60d896" - }, - "text": "{vladk, barlaso, plewis, ledell, edunov, scottyih}@fb.com sewon@cs.washington.edu danqic@cs.princeton.edu", - "type": "NarrativeText" - }, - { - "element_id": "d21b4a64a2d8656a0fdf7ab2e89a4916", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 158.9, - 224.0 - ], - [ - 158.9, - 236.0 - ], - [ - 203.4, - 236.0 - ], - [ - 203.4, - 224.0 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 1 - }, - "text": "Abstract", - "type": "Title" - }, - { - "element_id": "40fd09620fd4ef591cfff19f2339b21e", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 88.6, - 245.7 - ], - [ - 88.6, - 446.9 - ], - [ - 274.9, - 446.9 - ], - [ - 274.9, - 245.7 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 711, - "text": ".", - "url": "Hfootnote.2" - } - ], - "page_number": 1, - "parent_id": "d21b4a64a2d8656a0fdf7ab2e89a4916" - }, - "text": "Open-domain question answering relies on ef- \ufb01cient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented us- ing dense representations alone, where em- beddings are learned from a small number of questions and passages by a simple dual- encoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene- BM25 system greatly by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks.1", - "type": "NarrativeText" - }, - { - "element_id": "6b86b273ff34fce19d6b804eff5a3f57", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 457.2 - ], - [ - 72.0, - 469.2 - ], - [ - 78.0, - 469.2 - ], - [ - 78.0, - 457.2 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 1, - "parent_id": "d21b4a64a2d8656a0fdf7ab2e89a4916" - }, - "text": "1", - "type": "UncategorizedText" - }, - { - "element_id": "b605350bc00209520b7cd8f546322663", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 89.9, - 457.2 - ], - [ - 89.9, - 469.2 - ], - [ - 154.8, - 469.2 - ], - [ - 154.8, - 457.2 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 1 - }, - "text": "Introduction", - "type": "Title" - }, - { - "element_id": "c64f44389bb8fe5071732a8740943dd4", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 71.2, - 478.7 - ], - [ - 71.2, - 706.4 - ], - [ - 292.1, - 706.4 - ], - [ - 292.1, - 478.7 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 37, - "text": "Voorhees", - "url": "cite.voorhees1999trec" - }, - { - "start_index": 47, - "text": "1999", - "url": "cite.voorhees1999trec" - }, - { - "start_index": 216, - "text": "Ferrucci", - "url": "cite.ferrucci2012introduction" - }, - { - "start_index": 226, - "text": "2012", - "url": "cite.ferrucci2012introduction" - }, - { - "start_index": 230, - "text": ");", - "url": "cite.moldovan2003performance" - }, - { - "start_index": 242, - "text": "etal", - "url": "cite.moldovan2003performance" - }, - { - "start_index": 250, - "text": "2003", - "url": "cite.moldovan2003performance" - }, - { - "start_index": 591, - "text": "Chenetal .,", - "url": "cite.chen2017reading" - }, - { - "start_index": 604, - "text": "2017", - "url": "cite.chen2017reading" - }, - { - "start_index": 757, - "text": ",", - "url": "Hfootnote.3" - } - ], - "page_number": 1, - "parent_id": "b605350bc00209520b7cd8f546322663" - }, - "text": "Open-domain question answering (QA) (Voorhees, 1999) is a task that answers factoid questions us- ing a large collection of documents. While early QA systems are often complicated and consist of multiple components (Ferrucci (2012); Moldovan et al. (2003), inter alia), the advances of reading comprehension models suggest a much simpli\ufb01ed two-stage framework: (1) a context retriever \ufb01rst selects a small subset of passages where some of them contain the answer to the question, and then (2) a machine reader can thoroughly exam- ine the retrieved contexts and identify the correct answer (Chen et al., 2017). Although reducing open-domain QA to machine reading is a very rea- sonable strategy, a huge performance degradation is often observed in practice2, indicating the needs of improving retrieval.", - "type": "NarrativeText" - }, - { - "element_id": "77616626377536e9398824b1b38effcb", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 84.7, - 712.7 - ], - [ - 84.7, - 734.1 - ], - [ - 290.3, - 734.1 - ], - [ - 290.3, - 712.7 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 1, - "parent_id": "b605350bc00209520b7cd8f546322663" - }, - "text": "\u2217Equal contribution 1The code and trained models have been released at", - "type": "NarrativeText" - }, - { - "element_id": "3ae8896a0aad690779c3404686696b43", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 735.1 - ], - [ - 72.0, - 744.1 - ], - [ - 224.5, - 744.1 - ], - [ - 224.5, - 735.1 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 1 - }, - "text": "https://github.com/facebookresearch/DPR.", - "type": "Title" - }, - { - "element_id": "72b6d39b162b30069b5784a826ffe271", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 84.7, - 744.5 - ], - [ - 84.7, - 754.9 - ], - [ - 290.3, - 754.9 - ], - [ - 290.3, - 744.5 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 1 - }, - "text": "2For instance, the exact match score on SQuAD v1.1 drops", - "type": "Title" - }, - { - "element_id": "f011683bf9057985e43cf3cd5e786014", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 303.0, - 224.9 - ], - [ - 303.0, - 574.6 - ], - [ - 527.4, - 574.6 - ], - [ - 527.4, - 224.9 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 74, - "text": "( Zaragoza , 2009 ), whichmatcheskeywordsef\ufb01", - "url": "cite.robertson2009probabilistic" - }, - { - "start_index": 89, - "text": "Zaragoza", - "url": "cite.robertson2009probabilistic" - }, - { - "start_index": 99, - "text": "2009", - "url": "cite.robertson2009probabilistic" - }, - { - "start_index": 1210, - "text": "ShrivastavaandLi", - "url": "cite.NIPS2014_5329" - }, - { - "start_index": 1230, - "text": "2014", - "url": "cite.NIPS2014_5329" - }, - { - "start_index": 1237, - "text": "Guoetal", - "url": "cite.guo2016quantization" - }, - { - "start_index": 1249, - "text": "2016", - "url": "cite.guo2016quantization" - } - ], - "page_number": 1, - "parent_id": "72b6d39b162b30069b5784a826ffe271" - }, - "text": "Retrieval in open-domain QA is usually imple- mented using TF-IDF or BM25 (Robertson and Zaragoza, 2009), which matches keywords ef\ufb01- ciently with an inverted index and can be seen as representing the question and context in high- dimensional, sparse vectors (with weighting). Con- versely, the dense, latent semantic encoding is com- plementary to sparse representations by design. For example, synonyms or paraphrases that consist of completely different tokens may still be mapped to vectors close to each other. Consider the question \u201cWho is the bad guy in lord of the rings?\u201d, which can be answered from the context \u201cSala Baker is best known for portraying the villain Sauron in the Lord of the Rings trilogy.\u201d A term-based system would have dif\ufb01culty retrieving such a context, while a dense retrieval system would be able to better match \u201cbad guy\u201d with \u201cvillain\u201d and fetch the cor- rect context. Dense encodings are also learnable by adjusting the embedding functions, which pro- vides additional \ufb02exibility to have a task-speci\ufb01c representation. With special in-memory data struc- tures and indexing schemes, retrieval can be done ef\ufb01ciently using maximum inner product search (MIPS) algorithms (e.g., Shrivastava and Li (2014); Guo et al. (2016)).", - "type": "NarrativeText" - }, - { - "element_id": "a3892a174dd8dceadf65495b936bf1e7", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 306.9, - 578.3 - ], - [ - 306.9, - 765.3 - ], - [ - 527.5, - 765.3 - ], - [ - 527.5, - 578.3 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 259, - "text": "Leeetal .,", - "url": "cite.lee2019latent" - }, - { - "start_index": 271, - "text": "2019", - "url": "cite.lee2019latent" - } - ], - "page_number": 1, - "parent_id": "72b6d39b162b30069b5784a826ffe271" - }, - "text": "However, it is generally believed that learn- ing a good dense vector representation needs a large number of labeled pairs of question and con- texts. Dense retrieval methods have thus never be shown to outperform TF-IDF/BM25 for open- domain QA before ORQA (Lee et al., 2019), which proposes a sophisticated inverse cloze task (ICT) objective, predicting the blocks that contain the masked sentence, for additional pretraining. The question encoder and the reader model are then \ufb01ne- tuned using pairs of questions and answers jointly. Although ORQA successfully demonstrates that dense retrieval can outperform BM25, setting new state-of-the-art results on multiple open-domain", - "type": "NarrativeText" - }, - { - "element_id": "6fca521f401a70e685cdee3b7f122df7", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 755.9 - ], - [ - 72.0, - 764.9 - ], - [ - 267.8, - 764.9 - ], - [ - 267.8, - 755.9 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 1 - }, - "text": "from above 80% to less than 40% (Yang et al., 2019a).", - "type": "Title" - }, - { - "element_id": "4ffbfe02a6da5ac2ffa586205e4603c9", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 65.2 - ], - [ - 72.0, - 171.0 - ], - [ - 292.2, - 171.0 - ], - [ - 292.2, - 65.2 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 2, - "parent_id": "6fca521f401a70e685cdee3b7f122df7" - }, - "text": "QA datasets, it also suffers from two weaknesses. First, ICT pretraining is computationally intensive and it is not completely clear that regular sentences are good surrogates of questions in the objective function. Second, because the context encoder is not \ufb01ne-tuned using pairs of questions and answers, the corresponding representations could be subop- timal.", - "type": "NarrativeText" - }, - { - "element_id": "3bed499e55636392d074ef00589538d0", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 71.6, - 174.6 - ], - [ - 71.6, - 647.1 - ], - [ - 292.2, - 647.1 - ], - [ - 292.2, - 174.6 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 229, - "text": "Devlinetal", - "url": "cite.devlin2018bert" - }, - { - "start_index": 244, - "text": "2019", - "url": "cite.devlin2018bert" - }, - { - "start_index": 282, - "text": "(", - "url": "cite.bromley1994signature" - }, - { - "start_index": 291, - "text": "etal .,", - "url": "cite.bromley1994signature" - }, - { - "start_index": 299, - "text": "1994", - "url": "cite.bromley1994signature" - }, - { - "start_index": 982, - "text": "Leeetal .,", - "url": "cite.lee2019latent" - }, - { - "start_index": 994, - "text": "2019", - "url": "cite.lee2019latent" - }, - { - "start_index": 1000, - "text": "Kwiatkowskietal .,", - "url": "cite.kwiatkowski2019natural" - }, - { - "start_index": 1020, - "text": "2019", - "url": "cite.kwiatkowski2019natural" - } - ], - "page_number": 2, - "parent_id": "6fca521f401a70e685cdee3b7f122df7" - }, - "text": "In this paper, we address the question: can we train a better dense embedding model using only pairs of questions and passages (or answers), with- out additional pretraining? By leveraging the now standard BERT pretrained model (Devlin et al., 2019) and a dual-encoder architecture (Bromley et al., 1994), we focus on developing the right training scheme using a relatively small number of question and passage pairs. Through a series of careful ablation studies, our \ufb01nal solution is surprisingly simple: the embedding is optimized for maximizing inner products of the question and relevant passage vectors, with an objective compar- ing all pairs of questions and passages in a batch. Our Dense Passage Retriever (DPR) is exception- ally strong. It not only outperforms BM25 by a large margin (65.2% vs. 42.9% in Top-5 accuracy), but also results in a substantial improvement on the end-to-end QA accuracy compared to ORQA (41.5% vs. 33.3%) in the open Natural Questions setting (Lee et al., 2019; Kwiatkowski et al., 2019). Our contributions are twofold. First, we demon- strate that with the proper training setup, sim- ply \ufb01ne-tuning the question and passage encoders on existing question-passage pairs is suf\ufb01cient to greatly outperform BM25. Our empirical results also suggest that additional pretraining may not be needed. Second, we verify that, in the context of open-domain question answering, a higher retrieval precision indeed translates to a higher end-to-end QA accuracy. By applying a modern reader model to the top retrieved passages, we achieve compara- ble or better results on multiple QA datasets in the open-retrieval setting, compared to several, much complicated systems.", - "type": "NarrativeText" - }, - { - "element_id": "b9cecd7cdf8e46c955bd793d1c2118df", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 662.4 - ], - [ - 72.0, - 674.4 - ], - [ - 152.8, - 674.4 - ], - [ - 152.8, - 662.4 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 2 - }, - "text": "2 Background", - "type": "Title" - }, - { - "element_id": "b5205fe2d106125396334bae254a782a", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 71.6, - 686.7 - ], - [ - 71.6, - 765.3 - ], - [ - 290.3, - 765.3 - ], - [ - 290.3, - 686.7 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 2, - "parent_id": "b9cecd7cdf8e46c955bd793d1c2118df" - }, - "text": "The problem of open-domain QA studied in this paper can be described as follows. Given a factoid question, such as \u201cWho \ufb01rst voiced Meg on Family Guy?\u201d or \u201cWhere was the 8th Dalai Lama born?\u201d, a system is required to answer it using a large corpus of diversi\ufb01ed topics. More speci\ufb01cally, we assume", - "type": "NarrativeText" - }, - { - "element_id": "9992ab80b044f68cf51f36e3de7b6e10", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 306.9, - 65.2 - ], - [ - 306.9, - 421.5 - ], - [ - 527.5, - 421.5 - ], - [ - 527.5, - 65.2 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 341, - "text": "p2", - "url": "Hfootnote.4" - }, - { - "start_index": 936, - "text": "Chenetal .,", - "url": "cite.chen2017reading" - }, - { - "start_index": 949, - "text": "2017", - "url": "cite.chen2017reading" - }, - { - "start_index": 953, - "text": ").", - "url": "Hfootnote.5" - } - ], - "page_number": 2, - "parent_id": "b9cecd7cdf8e46c955bd793d1c2118df" - }, - "text": "the extractive QA setting, in which the answer is restricted to a span appearing in one or more pas- sages in the corpus. Assume that our collection contains D documents, d1, d2, \u00b7 \u00b7 \u00b7 , dD. We \ufb01rst split each of the documents into text passages of equal lengths as the basic retrieval units3 and get M total passages in our corpus C = {p1, p2, . . . , pM }, where each passage pi can be viewed as a sequence 2 , \u00b7 \u00b7 \u00b7 , w(i) 1 , w(i) of tokens w(i) |pi|. Given a question q, the task is to \ufb01nd a span w(i) s+1, \u00b7 \u00b7 \u00b7 , w(i) s , w(i) from one of the passages pi that can answer the question. Notice that to cover a wide variety of domains, the corpus size can easily range from millions of docu- ments (e.g., Wikipedia) to billions (e.g., the Web). As a result, any open-domain QA system needs to include an ef\ufb01cient retriever component that can se- lect a small set of relevant texts, before applying the reader to extract the answer (Chen et al., 2017).4 Formally speaking, a retriever R : (q, C) \u2192 CF is a function that takes as input a question q and a corpus C and returns a much smaller \ufb01lter set of texts CF \u2282 C, where |CF | = k (cid:28) |C|. For a \ufb01xed k, a retriever can be evaluated in isolation on top-k retrieval accuracy, which is the fraction of ques- tions for which CF contains a span that answers the question.", - "type": "NarrativeText" - }, - { - "element_id": "3f79bb7b435b05321651daefd374cdc6", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 491.9, - 197.4 - ], - [ - 491.9, - 205.3 - ], - [ - 495.8, - 205.3 - ], - [ - 495.8, - 197.4 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 2 - }, - "text": "e", - "type": "Title" - }, - { - "element_id": "09b350258cc8e8c2bbc4d2164016b892", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 307.3, - 434.2 - ], - [ - 307.3, - 446.2 - ], - [ - 485.1, - 446.2 - ], - [ - 485.1, - 434.2 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 2 - }, - "text": "3 Dense Passage Retriever (DPR)", - "type": "Title" - }, - { - "element_id": "205fbcb8766db03aca41834da8bdfe53", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 306.8, - 456.5 - ], - [ - 306.8, - 602.9 - ], - [ - 527.5, - 602.9 - ], - [ - 527.5, - 456.5 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 466, - "text": ". 1", - "url": "subsection.4.1" - } - ], - "page_number": 2, - "parent_id": "09b350258cc8e8c2bbc4d2164016b892" - }, - "text": "We focus our research in this work on improv- ing the retrieval component in open-domain QA. Given a collection of M text passages, the goal of our dense passage retriever (DPR) is to index all the passages in a low-dimensional and continuous space, such that it can retrieve ef\ufb01ciently the top k passages relevant to the input question for the reader at run-time. Note that M can be very large (e.g., 21 million passages in our experiments, de- scribed in Section 4.1) and k is usually small, such as 20\u2013100.", - "type": "NarrativeText" - }, - { - "element_id": "ed67b4f9e3be54965e9e1d2b8e5fda98", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 307.3, - 615.1 - ], - [ - 307.3, - 626.0 - ], - [ - 376.4, - 626.0 - ], - [ - 376.4, - 615.1 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 2 - }, - "text": "3.1 Overview", - "type": "Title" - }, - { - "element_id": "f14ec690f6e77f6f9314775b93a641db", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 307.3, - 633.0 - ], - [ - 307.3, - 684.6 - ], - [ - 527.5, - 684.6 - ], - [ - 527.5, - 633.0 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 2, - "parent_id": "ed67b4f9e3be54965e9e1d2b8e5fda98" - }, - "text": "Our dense passage retriever (DPR) uses a dense encoder EP (\u00b7) which maps any text passage to a d- dimensional real-valued vectors and builds an index for all the M passages that we will use for retrieval.", - "type": "NarrativeText" - }, - { - "element_id": "fac7e303e228e8ad71c8639b55b6da4e", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 307.0, - 693.8 - ], - [ - 307.0, - 744.1 - ], - [ - 527.0, - 744.1 - ], - [ - 527.0, - 693.8 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 2, - "parent_id": "ed67b4f9e3be54965e9e1d2b8e5fda98" - }, - "text": "3The ideal size and boundary of a text passage are func- tions of both the retriever and reader. We also experimented with natural paragraphs in our preliminary trials and found that using \ufb01xed-length passages performs better in both retrieval and \ufb01nal QA accuracy, as observed by Wang et al. (2019).", - "type": "NarrativeText" - }, - { - "element_id": "ddc41519eb7ad91c8843e3fb24add5a8", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 307.3, - 744.5 - ], - [ - 307.3, - 764.9 - ], - [ - 526.7, - 764.9 - ], - [ - 526.7, - 744.5 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 21, - "text": "Seoetal .,", - "url": "cite.seo2019real" - }, - { - "start_index": 33, - "text": "2019", - "url": "cite.seo2019real" - }, - { - "start_index": 44, - "text": "Robertsetal", - "url": "cite.roberts2020much" - } - ], - "page_number": 2, - "parent_id": "ed67b4f9e3be54965e9e1d2b8e5fda98" - }, - "text": "4Exceptions include (Seo et al., 2019) and (Roberts et al., 2020), which retrieves and generates the answers, respectively.", - "type": "NarrativeText" - }, - { - "element_id": "22f2c00bed24ac090fef5c4b109e71b7", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 71.6, - 65.0 - ], - [ - 71.6, - 143.9 - ], - [ - 291.5, - 143.9 - ], - [ - 291.5, - 65.0 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 3, - "parent_id": "ed67b4f9e3be54965e9e1d2b8e5fda98" - }, - "text": "At run-time, DPR applies a different encoder EQ(\u00b7) that maps the input question to a d-dimensional vector, and retrieves k passages of which vectors are the closest to the question vector. We de\ufb01ne the similarity between the question and the passage using the dot product of their vectors:", - "type": "NarrativeText" - }, - { - "element_id": "3c4a2d2202c0aaec37196c177dd405d5", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 120.6, - 154.0 - ], - [ - 120.6, - 169.7 - ], - [ - 241.7, - 169.7 - ], - [ - 241.7, - 154.0 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 3, - "parent_id": "ed67b4f9e3be54965e9e1d2b8e5fda98" - }, - "text": "sim(q, p) = EQ(q)(cid:124)EP (p).", - "type": "UncategorizedText" - }, - { - "element_id": "a5382d7617cbb8481f61a748a1eb216f", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 71.6, - 182.9 - ], - [ - 71.6, - 437.7 - ], - [ - 292.2, - 437.7 - ], - [ - 292.2, - 182.9 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 674, - "text": "MussmannandErmon", - "url": "cite.mussmann2016learning" - }, - { - "start_index": 694, - "text": "2016", - "url": "cite.mussmann2016learning" - }, - { - "start_index": 700, - "text": "RamandGray", - "url": "cite.ram2012maximum" - }, - { - "start_index": 714, - "text": "2012", - "url": "cite.ram2012maximum" - }, - { - "start_index": 806, - "text": ". 2", - "url": "section*.13" - }, - { - "start_index": 820, - "text": "),", - "url": "appendix.B" - } - ], - "page_number": 3, - "parent_id": "ed67b4f9e3be54965e9e1d2b8e5fda98" - }, - "text": "Although more expressive model forms for measur- ing the similarity between a question and a passage do exist, such as networks consisting of multiple layers of cross attentions, the similarity function needs to be decomposable so that the represen- tations of the collection of passages can be pre- computed. Most decomposable similarity functions are some transformations of Euclidean distance (L2). For instance, cosine is equivalent to inner product for unit vectors and the Mahalanobis dis- tance is equivalent to L2 distance in a transformed space. Inner product search has been widely used and studied, as well as its connection to cosine similarity and L2 distance (Mussmann and Ermon, 2016; Ram and Gray, 2012). As our ablation study \ufb01nds other similarity functions perform compara- bly (Section 5.2; Appendix B), we thus choose the simpler inner product function and improve the dense passage retriever by learning better encoders.", - "type": "NarrativeText" - }, - { - "element_id": "fc0e66fbc43692b8d2cd53aebc7601e6", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 448.2 - ], - [ - 72.0, - 527.0 - ], - [ - 292.1, - 527.0 - ], - [ - 292.1, - 448.2 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 152, - "text": "Devlinetal .,", - "url": "cite.devlin2018bert" - }, - { - "start_index": 167, - "text": "2019", - "url": "cite.devlin2018bert" - } - ], - "page_number": 3, - "parent_id": "ed67b4f9e3be54965e9e1d2b8e5fda98" - }, - "text": "Encoders Although in principle the question and passage encoders can be implemented by any neu- ral networks, in this work we use two independent BERT (Devlin et al., 2019) networks (base, un- cased) and take the representation at the [CLS] token as the output, so d = 768.", - "type": "NarrativeText" - }, - { - "element_id": "e5577179ffda2c6c2756b128045bf7bf", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 71.7, - 537.5 - ], - [ - 71.7, - 657.7 - ], - [ - 292.2, - 657.7 - ], - [ - 292.2, - 537.5 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 113, - "text": "Johnsonetal .,", - "url": "cite.johnson2017billion" - }, - { - "start_index": 129, - "text": "2017", - "url": "cite.johnson2017billion" - } - ], - "page_number": 3, - "parent_id": "ed67b4f9e3be54965e9e1d2b8e5fda98" - }, - "text": "Inference During inference time, we apply the passage encoder EP to all the passages and index them using FAISS (Johnson et al., 2017) of\ufb02ine. FAISS is an extremely ef\ufb01cient, open-source li- brary for similarity search and clustering of dense vectors, which can easily be applied to billions of vectors. Given a question q at run-time, we derive its embedding vq = EQ(q) and retrieve the top k passages with embeddings closest to vq.", - "type": "NarrativeText" - }, - { - "element_id": "22e5bda5d100c1c1defaf10962bbe992", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 668.8 - ], - [ - 72.0, - 679.7 - ], - [ - 137.0, - 679.7 - ], - [ - 137.0, - 668.8 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 3 - }, - "text": "3.2 Training", - "type": "Title" - }, - { - "element_id": "39abf2efb588fac56bdc1e05c881e601", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 71.7, - 686.7 - ], - [ - 71.7, - 765.3 - ], - [ - 292.1, - 765.3 - ], - [ - 292.1, - 686.7 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 65, - "text": "1", - "url": "equation.3.1" - }, - { - "start_index": 159, - "text": "Kulis", - "url": "cite.kulis2013metric" - }, - { - "start_index": 166, - "text": "2013", - "url": "cite.kulis2013metric" - } - ], - "page_number": 3, - "parent_id": "22e5bda5d100c1c1defaf10962bbe992" - }, - "text": "Training the encoders so that the dot-product sim- ilarity (Eq. (1)) becomes a good ranking function for retrieval is essentially a metric learning prob- lem (Kulis, 2013). The goal is to create a vector space such that relevant pairs of questions and pas- sages will have smaller distance (i.e., higher simi-", - "type": "NarrativeText" - }, - { - "element_id": "fd0ad9026eee596b7072a762941f60be", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 277.5, - 158.0 - ], - [ - 277.5, - 168.9 - ], - [ - 290.3, - 168.9 - ], - [ - 290.3, - 158.0 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 3, - "parent_id": "22e5bda5d100c1c1defaf10962bbe992" - }, - "text": "(1)", - "type": "UncategorizedText" - }, - { - "element_id": "59dc5d35508a682e88d90cf55ea6e624", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 307.3, - 65.2 - ], - [ - 307.3, - 104.0 - ], - [ - 525.7, - 104.0 - ], - [ - 525.7, - 65.2 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 3, - "parent_id": "22e5bda5d100c1c1defaf10962bbe992" - }, - "text": "larity) than the irrelevant ones, by learning a better embedding function. Let D = {(cid:104)qi, p+", - "type": "NarrativeText" - }, - { - "element_id": "f17b9817a078291c14e55b7202500999", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 306.9, - 89.9 - ], - [ - 306.9, - 184.5 - ], - [ - 527.4, - 184.5 - ], - [ - 527.4, - 89.9 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 3, - "parent_id": "22e5bda5d100c1c1defaf10962bbe992" - }, - "text": "i , p\u2212 i=1 be the training data that consists of m instances. Each instance contains one question qi and one relevant (positive) passage p+ i , along with n irrelevant (neg- ative) passages p\u2212 i,j. We optimize the loss function as the negative log likelihood of the positive pas- sage:", - "type": "NarrativeText" - }, - { - "element_id": "6d14583d41dfb1867339ba6604a43148", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 415.2, - 89.9 - ], - [ - 415.2, - 105.7 - ], - [ - 463.3, - 105.7 - ], - [ - 463.3, - 89.9 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 3, - "parent_id": "22e5bda5d100c1c1defaf10962bbe992" - }, - "text": "i,1, \u00b7 \u00b7 \u00b7 , p\u2212", - "type": "UncategorizedText" - }, - { - "element_id": "7a61269c5667ad430435dd2d81979866", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 456.7, - 90.5 - ], - [ - 456.7, - 105.7 - ], - [ - 484.8, - 105.7 - ], - [ - 484.8, - 90.5 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 3, - "parent_id": "22e5bda5d100c1c1defaf10962bbe992" - }, - "text": "i,n(cid:105)}m", - "type": "UncategorizedText" - }, - { - "element_id": "56af521ffb15cc73e9a0d76e1055218c", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 347.9, - 194.8 - ], - [ - 347.9, - 209.0 - ], - [ - 384.8, - 209.0 - ], - [ - 384.8, - 194.8 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 3 - }, - "text": "L(qi, p+", - "type": "Title" - }, - { - "element_id": "1d7f6abf52b3544f828e23016f8dfd2a", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 378.2, - 194.8 - ], - [ - 378.2, - 210.6 - ], - [ - 402.2, - 210.6 - ], - [ - 402.2, - 194.8 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 3 - }, - "text": "i , p\u2212", - "type": "Title" - }, - { - "element_id": "6d14583d41dfb1867339ba6604a43148", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 395.6, - 194.8 - ], - [ - 395.6, - 210.6 - ], - [ - 443.7, - 210.6 - ], - [ - 443.7, - 194.8 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 3, - "parent_id": "1d7f6abf52b3544f828e23016f8dfd2a" - }, - "text": "i,1, \u00b7 \u00b7 \u00b7 , p\u2212", - "type": "UncategorizedText" - }, - { - "element_id": "6a0f02b3a066e60061ea6d0812369e08", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 419.9, - 197.0 - ], - [ - 419.9, - 228.1 - ], - [ - 464.0, - 228.1 - ], - [ - 464.0, - 197.0 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 3 - }, - "text": "i,n) esim(qi,p+ i )", - "type": "Title" - }, - { - "element_id": "c8dae2af59c4799d1e39b344aac8ba4d", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 329.5, - 224.6 - ], - [ - 329.5, - 235.5 - ], - [ - 372.2, - 235.5 - ], - [ - 372.2, - 224.6 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 3 - }, - "text": "= \u2212 log", - "type": "Title" - }, - { - "element_id": "32465a610c663202b31d33648f295b5c", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 444.7, - 231.2 - ], - [ - 444.7, - 249.3 - ], - [ - 508.6, - 249.3 - ], - [ - 508.6, - 231.2 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 3 - }, - "text": "j=1 esim(qi,p\u2212 i,j )", - "type": "Title" - }, - { - "element_id": "2ebe028b09bf728e110a69a96356e98a", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 375.3, - 232.3 - ], - [ - 375.3, - 246.7 - ], - [ - 415.6, - 246.7 - ], - [ - 415.6, - 232.3 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 3 - }, - "text": "esim(qi,p+", - "type": "Title" - }, - { - "element_id": "0c078217696e35060ba3ccd86db14528", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 410.0, - 232.0 - ], - [ - 410.0, - 246.7 - ], - [ - 449.9, - 246.7 - ], - [ - 449.9, - 232.0 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 3, - "parent_id": "2ebe028b09bf728e110a69a96356e98a" - }, - "text": "i ) + (cid:80)n", - "type": "UncategorizedText" - }, - { - "element_id": "988286585a8a008cd87d42744cbfebb1", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 306.9, - 259.3 - ], - [ - 306.9, - 595.5 - ], - [ - 527.5, - 595.5 - ], - [ - 527.5, - 259.3 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 933, - "text": ". 2", - "url": "section*.10" - } - ], - "page_number": 3, - "parent_id": "2ebe028b09bf728e110a69a96356e98a" - }, - "text": "Positive and negative passages For retrieval problems, it is often the case that positive examples are available explicitly, while negative examples need to be selected from an extremely large pool. For instance, passages relevant to a question may be given in a QA dataset, or can be found using the answer. All other passages in the collection, while not speci\ufb01ed explicitly, can be viewed as irrelevant by default. In practice, how to select negative ex- amples is often overlooked but could be decisive for learning a high-quality encoder. We consider three different types of negatives: (1) Random: any random passage from the corpus; (2) BM25: top passages returned by BM25 which don\u2019t contain the answer but match most question tokens; (3) Gold: positive passages paired with other questions which appear in the training set. We will discuss the impact of different types of negative passages and training schemes in Section 5.2. Our best model uses gold passages from the same mini-batch and one BM25 negative passage. In particular, re-using gold passages from the same batch as negatives can make the computation ef\ufb01cient while achiev- ing great performance. We discuss this approach below.", - "type": "NarrativeText" - }, - { - "element_id": "0d3b6e30f877cab114de963fffd73e70", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 306.0, - 605.1 - ], - [ - 306.0, - 765.3 - ], - [ - 527.5, - 765.3 - ], - [ - 527.5, - 605.1 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 3, - "parent_id": "2ebe028b09bf728e110a69a96356e98a" - }, - "text": "In-batch negatives Assume that we have B questions in a mini-batch and each one is asso- ciated with a relevant passage. Let Q and P be the (B \u00d7d) matrix of question and passage embeddings in a batch of size B. S = QPT is a (B \u00d7 B) ma- trix of similarity scores, where each row of which corresponds to a question, paired with B passages. In this way, we reuse computation and effectively train on B2 (qi, pj) question/passage pairs in each batch. Any (qi, pj) pair is a positive example when i = j, and negative otherwise. This creates B train- ing instances in each batch, where there are B \u2212 1", - "type": "NarrativeText" - }, - { - "element_id": "0e77e68ba5473d98840c3212f4a8cb80", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 512.8, - 197.3 - ], - [ - 512.8, - 208.2 - ], - [ - 525.5, - 208.2 - ], - [ - 525.5, - 197.3 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 3, - "parent_id": "2ebe028b09bf728e110a69a96356e98a" - }, - "text": "(2)", - "type": "UncategorizedText" - }, - { - "element_id": "cdb4ee2aea69cc6a83331bbe96dc2caa", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 510.3, - 224.6 - ], - [ - 510.3, - 235.5 - ], - [ - 513.3, - 235.5 - ], - [ - 513.3, - 224.6 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 3, - "parent_id": "2ebe028b09bf728e110a69a96356e98a" - }, - "text": ".", - "type": "UncategorizedText" - }, - { - "element_id": "c7d4828d505e88fd6d50c5d097350afb", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 65.2 - ], - [ - 72.0, - 76.1 - ], - [ - 229.8, - 76.1 - ], - [ - 229.8, - 65.2 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 4 - }, - "text": "negative passages for each question.", - "type": "Title" - }, - { - "element_id": "c7cba915e3a62932889a7f6c3c31050b", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 79.2 - ], - [ - 72.0, - 157.8 - ], - [ - 291.2, - 157.8 - ], - [ - 291.2, - 79.2 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 73, - "text": "Yihetal .,", - "url": "cite.yih2011learning" - }, - { - "start_index": 85, - "text": "2011", - "url": "cite.yih2011learning" - }, - { - "start_index": 125, - "text": "Hendersonetal .,", - "url": "cite.henderson2017efficient" - }, - { - "start_index": 143, - "text": "2017", - "url": "cite.henderson2017efficient" - }, - { - "start_index": 149, - "text": "Gillicketal .,", - "url": "cite.gillick-etal-2019-learning" - }, - { - "start_index": 165, - "text": "2019", - "url": "cite.gillick-etal-2019-learning" - } - ], - "page_number": 4, - "parent_id": "c7d4828d505e88fd6d50c5d097350afb" - }, - "text": "The trick of in-batch negatives has been used in the full batch setting (Yih et al., 2011) and more recently for mini-batch (Henderson et al., 2017; Gillick et al., 2019). It has been shown to be an effective strategy for learning a dual-encoder model that boosts the number of training examples.", - "type": "NarrativeText" - }, - { - "element_id": "ece34411811a0a732567bb7b7282ef86", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 171.2 - ], - [ - 72.0, - 183.2 - ], - [ - 191.9, - 183.2 - ], - [ - 191.9, - 171.2 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 4 - }, - "text": "4 Experimental Setup", - "type": "Title" - }, - { - "element_id": "becf92039254407410319cad3e1f4847", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 194.1 - ], - [ - 72.0, - 218.5 - ], - [ - 290.5, - 218.5 - ], - [ - 290.5, - 194.1 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 4, - "parent_id": "ece34411811a0a732567bb7b7282ef86" - }, - "text": "In this section, we describe the data we used for experiments and the basic setup.", - "type": "NarrativeText" - }, - { - "element_id": "907e15b3e47cd91541fe1063ef3b224e", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 231.4 - ], - [ - 72.0, - 242.3 - ], - [ - 241.4, - 242.3 - ], - [ - 241.4, - 231.4 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 4 - }, - "text": "4.1 Wikipedia Data Pre-processing", - "type": "Title" - }, - { - "element_id": "cf2cef2c4e225e8618898ecc733b13b1", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 71.5, - 249.8 - ], - [ - 71.5, - 450.4 - ], - [ - 292.2, - 450.4 - ], - [ - 292.2, - 249.8 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 11, - "text": "Leeetal .,", - "url": "cite.lee2019latent" - }, - { - "start_index": 23, - "text": "2019", - "url": "cite.lee2019latent" - }, - { - "start_index": 187, - "text": "(", - "url": "cite.chen2017reading" - }, - { - "start_index": 193, - "text": "etal .,", - "url": "cite.chen2017reading" - }, - { - "start_index": 201, - "text": "2017", - "url": "cite.chen2017reading" - }, - { - "start_index": 532, - "text": "Wangetal .,", - "url": "cite.wang2019multi" - }, - { - "start_index": 545, - "text": "2019", - "url": "cite.wang2019multi" - }, - { - "start_index": 599, - "text": ".", - "url": "Hfootnote.6" - } - ], - "page_number": 4, - "parent_id": "907e15b3e47cd91541fe1063ef3b224e" - }, - "text": "Following (Lee et al., 2019), we use the English Wikipedia dump from Dec. 20, 2018 as the source documents for answering questions. We \ufb01rst apply the pre-processing code released in DrQA (Chen et al., 2017) to extract the clean, text-portion of articles from the Wikipedia dump. This step re- moves semi-structured data, such as tables, info- boxes, lists, as well as the disambiguation pages. We then split each article into multiple, disjoint text blocks of 100 words as passages, serving as our basic retrieval units, following (Wang et al., 2019), which results in 21,015,324 passages in the end.5 Each passage is also prepended with the title of the Wikipedia article where the passage is from, along with an [SEP] token.", - "type": "NarrativeText" - }, - { - "element_id": "6a27ae9ecb0eb52c884fd80f3b85c394", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 463.2 - ], - [ - 72.0, - 474.1 - ], - [ - 233.5, - 474.1 - ], - [ - 233.5, - 463.2 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 4 - }, - "text": "4.2 Question Answering Datasets", - "type": "Title" - }, - { - "element_id": "7b8f4715f2ea2d367c504bd2d865f25a", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 71.5, - 481.6 - ], - [ - 71.5, - 724.5 - ], - [ - 292.1, - 724.5 - ], - [ - 292.1, - 481.6 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 97, - "text": "Leeetal .,", - "url": "cite.lee2019latent" - }, - { - "start_index": 109, - "text": "2019", - "url": "cite.lee2019latent" - }, - { - "start_index": 248, - "text": "Kwiatkowskietal", - "url": "cite.kwiatkowski2019natural" - }, - { - "start_index": 268, - "text": "2019", - "url": "cite.kwiatkowski2019natural" - }, - { - "start_index": 465, - "text": "Joshietal .,", - "url": "cite.joshi-etal-2017-triviaqa" - }, - { - "start_index": 479, - "text": "2017", - "url": "cite.joshi-etal-2017-triviaqa" - }, - { - "start_index": 595, - "text": "Berantetal .,", - "url": "cite.berant2013semantic" - }, - { - "start_index": 610, - "text": "2013", - "url": "cite.berant2013semantic" - }, - { - "start_index": 737, - "text": "Baudi\u02c7sand\u02c7Sediv ` y", - "url": "cite.baudivs2015modeling" - } - ], - "page_number": 4, - "parent_id": "6a27ae9ecb0eb52c884fd80f3b85c394" - }, - "text": "We use the same \ufb01ve QA datasets and train- ing/dev/testing splitting method as in previous work (Lee et al., 2019). Below we brie\ufb02y describe each dataset and refer readers to their paper for the details of data preparation. Natural Questions (NQ) (Kwiatkowski et al., 2019) was designed for end-to-end question an- swering. The questions were mined from real Google search queries and the answers were spans in Wikipedia articles identi\ufb01ed by annotators. TriviaQA (Joshi et al., 2017) contains a set of trivia questions with answers that were originally scraped from the Web. WebQuestions (WQ) (Berant et al., 2013) consists of questions selected using Google Suggest API, where the answers are entities in Freebase. CuratedTREC (TREC) (Baudi\u02c7s and \u02c7Sediv`y, 2015) sources questions from TREC QA tracks", - "type": "NarrativeText" - }, - { - "element_id": "f854d7756ec1a89cd14f9f30ed5d33d6", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 734.5 - ], - [ - 72.0, - 764.9 - ], - [ - 291.8, - 764.9 - ], - [ - 291.8, - 734.5 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 10, - "text": "Wangetal", - "url": "cite.wang2019multi" - }, - { - "start_index": 23, - "text": "2019", - "url": "cite.wang2019multi" - } - ], - "page_number": 4, - "parent_id": "6a27ae9ecb0eb52c884fd80f3b85c394" - }, - "text": "5However, Wang et al. (2019) also propose splitting docu- ments into overlapping passages, which we do not \ufb01nd advan- tageous compared to the non-overlapping version.", - "type": "NarrativeText" - }, - { - "element_id": "4411e525721e7dd801755882fd2361b2", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 313.3, - 66.6 - ], - [ - 313.3, - 75.6 - ], - [ - 342.1, - 75.6 - ], - [ - 342.1, - 66.6 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 4 - }, - "text": "Dataset", - "type": "Title" - }, - { - "element_id": "e99c90db87c0e400832edcedcc1b14e3", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 410.2, - 66.6 - ], - [ - 410.2, - 75.6 - ], - [ - 431.5, - 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"eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 4, - "parent_id": "219a3bd3f501a2476d0f41dcee9ce459" - }, - "text": "8,757 8,837 361 133 8,886", - "type": "UncategorizedText" - }, - { - "element_id": "b9201cfeba16534906bc28ec5581f2a1", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 495.6, - 82.1 - ], - [ - 495.6, - 130.9 - ], - [ - 520.2, - 130.9 - ], - [ - 520.2, - 82.1 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 4, - "parent_id": "219a3bd3f501a2476d0f41dcee9ce459" - }, - "text": "3,610 11,313 2,032 694 10,570", - "type": "UncategorizedText" - }, - { - "element_id": "380bdc30d41f33c3934c8990192af5af", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 307.0, - 145.3 - ], - [ - 307.0, - 191.1 - ], - [ - 527.2, - 191.1 - ], - [ - 527.2, - 145.3 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 4, - "parent_id": "219a3bd3f501a2476d0f41dcee9ce459" - }, - "text": "Table 1: Number of questions in each QA dataset. The two columns of Train denote the original training ex- amples in the dataset and the actual questions used for training DPR after \ufb01ltering. See text for more details.", - "type": "NarrativeText" - }, - { - "element_id": "2c9cb5e777bfcb3ce2cab29cc5b11ed0", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 306.9, - 215.8 - ], - [ - 306.9, - 389.7 - ], - [ - 527.5, - 389.7 - ], - [ - 527.5, - 215.8 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 105, - "text": "Rajpurkaretal .,", - "url": "cite.rajpurkar2016squad" - }, - { - "start_index": 123, - "text": "2016", - "url": "cite.rajpurkar2016squad" - }, - { - "start_index": 604, - "text": ". 1", - "url": "subsection.5.1" - } - ], - "page_number": 4, - "parent_id": "219a3bd3f501a2476d0f41dcee9ce459" - }, - "text": "as well as various Web sources and is intended for open-domain QA from unstructured corpora. SQuAD v1.1 (Rajpurkar et al., 2016) is a popu- lar benchmark dataset for reading comprehension. Annotators were presented with a Wikipedia para- graph, and asked to write questions that could be answered from the given text. Although SQuAD has been used previously for open-domain QA re- search, it is not ideal because many questions lack context in absence of the provided paragraph. We still include it in our experiments for providing a fair comparison to previous work and we will discuss more in Section 5.1.", - "type": "NarrativeText" - }, - { - "element_id": "e00121a43908054686a3e432345f8b27", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 306.9, - 401.3 - ], - [ - 306.9, - 629.0 - ], - [ - 527.4, - 629.0 - ], - [ - 527.4, - 401.3 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 123, - "text": ",", - "url": "Hfootnote.7" - }, - { - "start_index": 542, - "text": ".", - "url": "Hfootnote.8" - }, - { - "start_index": 708, - "text": "/", - "url": "table.caption.6" - } - ], - "page_number": 4, - "parent_id": "219a3bd3f501a2476d0f41dcee9ce459" - }, - "text": "Selection of positive passages Because only pairs of questions and answers are provided in TREC, WebQuestions and TriviaQA6, we use the highest-ranked passage from BM25 that contains the answer as the positive passage. If none of the top 100 retrieved passages has the answer, the ques- tion will be discarded. For SQuAD and Natural Questions, since the original passages have been split and processed differently than our pool of candidate passages, we match and replace each gold passage with the corresponding passage in the candidate pool.7 We discard the questions when the matching is failed due to different Wikipedia versions or pre-processing. 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See Section 5.2 and Ap- pendix A.", - "type": "NarrativeText" - }, - { - "element_id": "c8131309d22a01dab9d323b3a7dce45e", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 84.5, - 66.6 - ], - [ - 84.5, - 75.6 - ], - [ - 163.8, - 75.6 - ], - [ - 163.8, - 66.6 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 5 - }, - "text": "Training Retriever", - "type": "Title" - }, - { - "element_id": "808957e3618049e5d396cfe75ac8b44c", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 253.5, - 66.6 - ], - [ - 253.5, - 75.6 - ], - [ - 280.1, - 75.6 - ], - [ - 280.1, - 66.6 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - 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"metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 71.5, - 373.0 - ], - [ - 71.5, - 573.6 - ], - [ - 292.2, - 573.6 - ], - [ - 292.2, - 373.0 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 280, - "text": "8InadditiontoDPR", - "url": "Hfootnote.9" - }, - { - "start_index": 459, - "text": "Speci\ufb01cally", - "url": "Hfootnote.10" - } - ], - "page_number": 5, - "parent_id": "ae242bdd9c62f1617946f518f448b323" - }, - "text": "While it is good to have the \ufb02exibility to adapt the retriever to each dataset, it would also be de- sirable to obtain a single retriever that works well across the board. To this end, we train a multi- dataset encoder by combining training data from all datasets excluding SQuAD.8 In addition to DPR, we also present the results of BM25, the traditional retrieval method9 and BM25+DPR, using a linear combination of their scores as the new ranking function. Speci\ufb01cally, we obtain two initial sets of top-2000 passages based on BM25 and DPR, respectively, and rerank the union of them using BM25(q,p) + \u03bb \u00b7 sim(q, p) as the ranking function. We used \u03bb = 1.1 based on the retrieval accuracy in the development set.", - "type": "NarrativeText" - }, - { - "element_id": "bb8182a74607429eeb51c6b02e9ecdac", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 307.0, - 389.5 - ], - [ - 307.0, - 447.5 - ], - [ - 527.2, - 447.5 - ], - [ - 527.2, - 389.5 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 5, - "parent_id": "ae242bdd9c62f1617946f518f448b323" - }, - "text": "Figure 1: Retriever top-k accuracy with different num- bers of training examples used in our dense passage re- triever vs BM25. The results are measured on the de- velopment set of Natural Questions. Our DPR trained using 1,000 examples already outperforms BM25.", - "type": "NarrativeText" - }, - { - "element_id": "f75b5bc8b0c551bcbb1614d50aba4ef0", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 307.3, - 474.3 - ], - [ - 307.3, - 566.5 - ], - [ - 527.4, - 566.5 - ], - [ - 527.4, - 474.3 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 5, - "parent_id": "ae242bdd9c62f1617946f518f448b323" - }, - "text": "tiple datasets, TREC, the smallest dataset of the \ufb01ve, bene\ufb01ts greatly from more training examples. In contrast, Natural Questions and WebQuestions improve modestly and TriviaQA degrades slightly. Results can be improved further in some cases by combining DPR with BM25 in both single- and multi-dataset settings.", - "type": "NarrativeText" - }, - { - "element_id": "5537a1e812e5808d8f32f5cdda497a0b", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 585.2 - ], - [ - 72.0, - 596.1 - ], - [ - 158.1, - 596.1 - ], - [ - 158.1, - 585.2 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 5 - }, - "text": "5.1 Main Results", - "type": "Title" - }, - { - "element_id": "84f5b33d08884f697fae0d91f7b1f381", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 71.6, - 602.9 - ], - [ - 71.6, - 695.1 - ], - [ - 292.1, - 695.1 - ], - [ - 292.1, - 602.9 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 0, - "text": "Table2comparesdifferentpassageretrievalsys temson\ufb01veQAdatasets , usingthetop - ( k \u2208{", - "url": "table.caption.7" - } - ], - "page_number": 5, - "parent_id": "5537a1e812e5808d8f32f5cdda497a0b" - }, - "text": "Table 2 compares different passage retrieval sys- tems on \ufb01ve QA datasets, using the top-k accuracy (k \u2208 {20, 100}). With the exception of SQuAD, DPR performs consistently better than BM25 on all datasets. The gap is especially large when k is small (e.g., 78.4% vs. 59.1% for top-20 accuracy on Natural Questions). When training with mul-", - "type": "NarrativeText" - }, - { - "element_id": "be5a91236f3f537bdd6f17fa70b1cadf", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 307.3, - 570.7 - ], - [ - 307.3, - 690.0 - ], - [ - 527.4, - 690.0 - ], - [ - 527.4, - 570.7 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 420, - "text": ".(", - "url": "cite.lee2019latent" - }, - { - "start_index": 423, - "text": "2019", - "url": "cite.lee2019latent" - } - ], - "page_number": 5, - "parent_id": "5537a1e812e5808d8f32f5cdda497a0b" - }, - "text": "We conjecture that the lower performance on SQuAD is due to two reasons. First, the annota- tors wrote questions after seeing the passage. As a result, there is a high lexical overlap between passages and questions, which gives BM25 a clear advantage. Second, the data was collected from only 500+ Wikipedia articles and thus the distribu- tion of training examples is extremely biased, as argued previously by Lee et al. 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BM25 parameters b = 0.4 (doc- ument length normalization) and k1 = 0.9 (term frequency scaling) are tuned using development sets.", - "type": "NarrativeText" - }, - { - "element_id": "7f27e8ed5fe407464f4f7554cb73130b", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 307.3, - 706.7 - ], - [ - 307.3, - 717.6 - ], - [ - 492.3, - 717.6 - ], - [ - 492.3, - 706.7 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 5 - }, - "text": "5.2 Ablation Study on Model Training", - "type": "Title" - }, - { - "element_id": "861ac126678e58f80c0041e12563a075", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 306.9, - 727.3 - ], - [ - 306.9, - 765.3 - ], - [ - 527.4, - 765.3 - ], - [ - 527.4, - 727.3 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 5, - "parent_id": "7f27e8ed5fe407464f4f7554cb73130b" - }, - "text": "To understand further how different model training options affect the results, we conduct several addi- tional experiments and discuss our \ufb01ndings below.", - "type": "NarrativeText" - }, - { - "element_id": "481f75c04a0065749ca22bfb39de5896", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 65.1 - ], - [ - 72.0, - 238.7 - ], - [ - 292.1, - 238.7 - ], - [ - 292.1, - 65.1 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 223, - "text": "measuredonthedevel", - "url": "figure.caption.8" - } - ], - "page_number": 6, - "parent_id": "7f27e8ed5fe407464f4f7554cb73130b" - }, - "text": "Sample ef\ufb01ciency We explore how many train- ing examples are needed to achieve good passage retrieval performance. Figure 1 illustrates the top-k retrieval accuracy with respect to different num- bers of training examples, measured on the devel- opment set of Natural Questions. As is shown, a dense passage retriever trained using only 1,000 ex- amples already outperforms BM25. This suggests that with a general pretrained language model, it is possible to train a high-quality dense retriever with a small number of question\u2013passage pairs. 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The top block is the standard 1-of-N training set- ting, where each question in the batch is paired with a positive passage and its own set of n neg- ative passages (Eq. (2)). 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We \ufb01nd that using a similar con\ufb01guration (7 gold negative passages), in-batch negative training improves the results substantially. The key difference between the two is whether the gold negative passages come from the same batch or from the whole training set. Effectively, in-batch negative training is an easy and memory-ef\ufb01cient way to reuse the negative examples already in the batch rather than creating new ones. It produces more pairs and thus increases the number of train- ing examples, which might contribute to the good model performance. 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These additional passages are used as negative passages for all questions in the same batch. 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"filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 6, - "parent_id": "44a6ad8737433180d6729f93f0bf4c24" - }, - "text": "84.4 84.0 84.9", - "type": "UncategorizedText" - }, - { - "element_id": "d2d319522069fb4c185a4fe6bf26d17d", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 307.0, - 200.0 - ], - [ - 307.0, - 281.7 - ], - [ - 527.2, - 281.7 - ], - [ - 527.2, - 200.0 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 6, - "parent_id": "44a6ad8737433180d6729f93f0bf4c24" - }, - "text": "Table 3: Comparison of different training schemes, measured as top-k retrieval accuracy on Natural Ques- #N: number of negative tions (development set). examples, IB: in-batch training. G.+BM25(1) and G.+BM25(2) denote in-batch training with 1 or 2 ad- ditional BM25 negatives, which serve as negative pas- sages for all questions in the batch.", - "type": "NarrativeText" - }, - { - "element_id": "cc643f4aca3015b926b2f6a989d7e312", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 307.3, - 305.6 - ], - [ - 307.3, - 384.3 - ], - [ - 525.7, - 384.3 - ], - [ - 525.7, - 305.6 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 179, - "text": ":", - "url": "appendix.A" - } - ], - "page_number": 6, - "parent_id": "44a6ad8737433180d6729f93f0bf4c24" - }, - "text": "Our experiments on Natural Questions show that switching to distantly-supervised passages (using the highest-ranked BM25 passage that contains the answer), has only a small impact: 1 point lower top-k accuracy for retrieval. Appendix A contains more details.", - "type": "NarrativeText" - }, - { - "element_id": "5375bcf4b99ff5452870d20b06412013", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 307.3, - 394.4 - ], - [ - 307.3, - 554.5 - ], - [ - 527.4, - 554.5 - ], - [ - 527.4, - 394.4 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 446, - "text": "(", - "url": "cite.burges2005learning" - }, - { - "start_index": 454, - "text": "etal .,", - "url": "cite.burges2005learning" - }, - { - "start_index": 462, - "text": "2005", - "url": "cite.burges2005learning" - }, - { - "start_index": 546, - "text": ". detailscanbefoundinAppendixB", - "url": "appendix.B" - } - ], - "page_number": 6, - "parent_id": "44a6ad8737433180d6729f93f0bf4c24" - }, - "text": "Similarity and loss Besides dot product, cosine and Euclidean L2 distance are also commonly used as decomposable similarity functions. We test these alternatives and \ufb01nd that L2 performs compara- ble to dot product, and both of them are superior to cosine. Similarly, in addition to negative log- likelihood, a popular option for ranking is triplet loss, which compares a positive passage and a nega- tive one directly with respect to a question (Burges et al., 2005). Our experiments show that using triplet loss does not affect the results much. More details can be found in Appendix B.", - "type": "NarrativeText" - }, - { - "element_id": "3f08ef7c7ccb9aa36188a1b578710b2a", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 306.9, - 564.6 - ], - [ - 306.9, - 765.3 - ], - [ - 527.5, - 765.3 - ], - [ - 527.5, - 564.6 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 6, - "parent_id": "44a6ad8737433180d6729f93f0bf4c24" - }, - "text": "Cross-dataset generalization One interesting question regarding DPR\u2019s discriminative training is how much performance degradation it may suf- fer from a non-iid setting. In other words, can it still generalize well when directly applied to a different dataset without additional \ufb01ne-tuning? To test the cross-dataset generalization, we train DPR on Natural Questions only and test it directly on the smaller WebQuestions and CuratedTREC datasets. We \ufb01nd that DPR generalizes well, with 3-5 points loss from the best performing \ufb01ne-tuned model in top-20 retrieval accuracy (69.9/86.3 vs. 75.0/89.1 for WebQuestions and TREC, respec- tively), while still greatly outperforming the BM25 baseline (55.0/70.9).", - "type": "NarrativeText" - }, - { - "element_id": "ee2253c61c597d8c61f15972ad2e5089", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 65.1 - ], - [ - 72.0, - 76.0 - ], - [ - 190.6, - 76.0 - ], - [ - 190.6, - 65.1 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 7 - }, - "text": "5.3 Qualitative Analysis", - "type": "Title" - }, - { - "element_id": "442af6d23319e7a9da68f75c5dc99c5d", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 71.6, - 83.3 - ], - [ - 71.6, - 175.5 - ], - [ - 292.1, - 175.5 - ], - [ - 292.1, - 83.3 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 275, - "text": ".", - "url": "appendix.C" - } - ], - "page_number": 7, - "parent_id": "ee2253c61c597d8c61f15972ad2e5089" - }, - "text": "Although DPR performs better than BM25 in gen- eral, passages retrieved by these two methods dif- fer qualitatively. Term-matching methods like BM25 are sensitive to highly selective keywords and phrases, while DPR captures lexical variations or semantic relationships better. See Appendix C for examples and more discussion.", - "type": "NarrativeText" - }, - { - "element_id": "2f3324a4fd6c56e27746984e7c300f16", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 187.9 - ], - [ - 72.0, - 198.8 - ], - [ - 189.6, - 198.8 - ], - [ - 189.6, - 187.9 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 7 - }, - "text": "5.4 Run-time Ef\ufb01ciency", - "type": "Title" - }, - { - "element_id": "13ad5dd59d4604e139396a5dc5e3be67", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 71.0, - 206.0 - ], - [ - 71.0, - 379.5 - ], - [ - 292.1, - 379.5 - ], - [ - 292.1, - 206.0 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 295, - "text": "- @ 2 . 20GHzand512GBmemory . FAISSin - memoryindexforreal - valuedvectors10", - "url": "Hfootnote.11" - } - ], - "page_number": 7, - "parent_id": "2f3324a4fd6c56e27746984e7c300f16" - }, - "text": "The main reason that we require a retrieval compo- nent for open-domain QA is to reduce the number of candidate passages that the reader needs to con- sider, which is crucial for answering user\u2019s ques- tions in real-time. We pro\ufb01led the passage retrieval speed on a server with Intel Xeon CPU E5-2698 v4 @ 2.20GHz and 512GB memory. With the help of FAISS in-memory index for real-valued vectors10, DPR can be made incredibly ef\ufb01cient, processing 995.0 questions per second, returning top 100 pas- sages per question. In contrast, BM25/Lucene (im- plemented in Java, using \ufb01le index) processes 23.7 questions per second per CPU thread.", - "type": "NarrativeText" - }, - { - "element_id": "47b1ce6dc0ff03626e4fab9309140792", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 382.4 - ], - [ - 72.0, - 501.7 - ], - [ - 292.1, - 501.7 - ], - [ - 292.1, - 382.4 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 7, - "parent_id": "2f3324a4fd6c56e27746984e7c300f16" - }, - "text": "On the other hand, the time required for building an index for dense vectors is much longer. Com- puting dense embeddings on 21-million passages is resource intensive, but can be easily parallelized, taking roughly 8.8 hours on 8 GPUs. However, building the FAISS index on 21-million vectors on a single server takes 8.5 hours. In comparison, building an inverted index using Lucene is much cheaper and takes only about 30 minutes in total.", - "type": "NarrativeText" - }, - { - "element_id": "af38cab6a4df868953e136c2c1dc139f", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 514.7 - ], - [ - 72.0, - 526.6 - ], - [ - 266.7, - 526.6 - ], - [ - 266.7, - 514.7 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 7 - }, - "text": "6 Experiments: Question Answering", - "type": "Title" - }, - { - "element_id": "780a2c6307da2ffe51d7aae906ab7af0", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 537.2 - ], - [ - 72.0, - 561.7 - ], - [ - 290.3, - 561.7 - ], - [ - 290.3, - 537.2 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 7, - "parent_id": "af38cab6a4df868953e136c2c1dc139f" - }, - "text": "In this section, we experiment with how different passage retrievers affect the \ufb01nal QA accuracy.", - "type": "NarrativeText" - }, - { - "element_id": "6c2139d41aaa0b6853052a20a6f229bf", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 574.1 - ], - [ - 72.0, - 585.0 - ], - [ - 204.4, - 585.0 - ], - [ - 204.4, - 574.1 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 7 - }, - "text": "6.1 End-to-end QA System", - "type": "Title" - }, - { - "element_id": "bac800be9f132d9bc309033d5b488248", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 71.5, - 592.2 - ], - [ - 71.5, - 725.0 - ], - [ - 292.2, - 725.0 - ], - [ - 292.2, - 592.2 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 7, - "parent_id": "6c2139d41aaa0b6853052a20a6f229bf" - }, - "text": "We implement an end-to-end question answering system in which we can plug different retriever systems directly. Besides the retriever, our QA sys- tem consists of a neural reader that outputs the answer to the question. Given the top k retrieved passages (up to 100 in our experiments), the reader assigns a passage selection score to each passage. In addition, it extracts an answer span from each passage and assigns a span score. The best span from the passage with the highest passage selection", - "type": "NarrativeText" - }, - { - "element_id": "f87ad5942b745ce926611c47eda2a3a0", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 734.5 - ], - [ - 72.0, - 764.9 - ], - [ - 291.4, - 764.9 - ], - [ - 291.4, - 734.5 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 7, - "parent_id": "6c2139d41aaa0b6853052a20a6f229bf" - }, - "text": "10FAISS con\ufb01guration: we used HNSW index type on CPU, neighbors to store per node = 512, construction time search depth = 200, search depth = 128.", - "type": "NarrativeText" - }, - { - "element_id": "a5b595988bcba207a47b29d1d80756d4", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 306.9, - 65.2 - ], - [ - 306.9, - 198.1 - ], - [ - 527.4, - 198.1 - ], - [ - 527.4, - 65.2 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 349, - "text": "1", - "url": "equation.3.1" - }, - { - "start_index": 465, - "text": "Wangetal .,", - "url": "cite.wang2019multi" - }, - { - "start_index": 478, - "text": "2019", - "url": "cite.wang2019multi" - }, - { - "start_index": 484, - "text": "2018", - "url": "cite.wang2018r" - }, - { - "start_index": 490, - "text": "Linetal .,", - "url": "cite.lin2018denoising" - }, - { - "start_index": 502, - "text": "2018", - "url": "cite.lin2018denoising" - } - ], - "page_number": 7, - "parent_id": "6c2139d41aaa0b6853052a20a6f229bf" - }, - "text": "score is chosen as the \ufb01nal answer. The passage selection model serves as a reranker through cross- attention between the question and the passage. Al- though cross-attention is not feasible for retrieving relevant passages in a large corpus due to its non- decomposable nature, it has more capacity than the dual-encoder model sim(q, p) as in Eq. (1). Apply- ing it to selecting the passage from a small number of retrieved candidates has been shown to work well (Wang et al., 2019, 2018; Lin et al., 2018).", - "type": "NarrativeText" - }, - { - "element_id": "905d86cc9d6f01520a08df6cf1d0e8fd", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 307.3, - 198.7 - ], - [ - 307.3, - 293.3 - ], - [ - 527.4, - 293.3 - ], - [ - 527.4, - 198.7 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 7, - "parent_id": "6c2139d41aaa0b6853052a20a6f229bf" - }, - "text": "Speci\ufb01cally, let Pi \u2208 RL\u00d7h (1 \u2264 i \u2264 k) be a BERT (base, uncased in our experiments) rep- resentation for the i-th passage, where L is the maximum length of the passage and h the hidden dimension. 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We compute a span score of the s-th to t-th words from the i-th passage as Pstart,i(s) \u00d7 Pend,i(t), and a passage selection score of the i-th passage as Pselected(i).", - "type": "NarrativeText" - }, - { - "element_id": "ac970c11f53f7030a85f7faa7b9080a4", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 402.3, - 369.7 - ], - [ - 402.3, - 384.0 - ], - [ - 455.9, - 384.0 - ], - [ - 455.9, - 369.7 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 7, - "parent_id": "6c2139d41aaa0b6853052a20a6f229bf" - }, - "text": ", . . . , P[CLS]", - "type": "UncategorizedText" - }, - { - "element_id": "6b86b273ff34fce19d6b804eff5a3f57", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 381.0, - 378.6 - ], - [ - 381.0, - 386.5 - ], - [ - 385.2, - 386.5 - ], - [ - 385.2, - 378.6 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 7, - "parent_id": "6c2139d41aaa0b6853052a20a6f229bf" - }, - "text": "1", - "type": "UncategorizedText" - }, - { - "element_id": "8254c329a92850f6d539dd376f4816ee", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 435.1, - 379.0 - ], - [ - 435.1, - 386.9 - ], - [ - 439.5, - 386.9 - ], - [ - 439.5, - 379.0 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 7 - }, - "text": "k", - "type": "Title" - }, - { - "element_id": "bf7b0392ed7eea8785844d1f7274dbe1", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 306.9, - 455.0 - ], - [ - 306.9, - 682.7 - ], - [ - 527.5, - 682.7 - ], - [ - 527.5, - 455.0 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 7, - "parent_id": "8254c329a92850f6d539dd376f4816ee" - }, - "text": "During training, we sample one positive and \u02dcm \u2212 1 negative passages from the top 100 passages returned by the retrieval system (BM25 or DPR) for each question. \u02dcm is a hyper-parameter and we use \u02dcm = 24 in all the experiments. The training ob- jective is to maximize the marginal log-likelihood of all the correct answer spans in the positive pas- sage (the answer string may appear multiple times in one passage), combined with the log-likelihood of the positive passage being selected. We use the batch size of 16 for large (NQ, TriviaQA, SQuAD) and 4 for small (TREC, WQ) datasets, and tune k on the development set. For experiments on small datasets under the Multi setting, in which using other datasets is allowed, we \ufb01ne-tune the reader trained on Natural Questions to the target dataset. All experiments were done on eight 32GB GPUs.", - "type": "NarrativeText" - }, - { - "element_id": "c4f34f099e4e35e7bc557654eb3cea6d", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 307.3, - 695.4 - ], - [ - 307.3, - 706.4 - ], - [ - 365.8, - 706.4 - ], - [ - 365.8, - 695.4 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 7 - }, - "text": "6.2 Results", - "type": "Title" - }, - { - "element_id": "b7e968d482b29979b9280789fd3c64b3", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 306.9, - 713.8 - ], - [ - 306.9, - 765.3 - ], - [ - 527.4, - 765.3 - ], - [ - 527.4, - 713.8 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 51, - "text": ",", - "url": "table.caption.15" - }, - { - "start_index": 136, - "text": "Chenetal", - "url": "cite.chen2017reading" - }, - { - "start_index": 149, - "text": "2017", - "url": "cite.chen2017reading" - }, - { - "start_index": 155, - "text": "Leeetal .,", - "url": "cite.lee2019latent" - }, - { - "start_index": 167, - "text": "2019", - "url": "cite.lee2019latent" - } - ], - "page_number": 7, - "parent_id": "c4f34f099e4e35e7bc557654eb3cea6d" - }, - "text": "Table 4 summarizes our \ufb01nal end-to-end QA re- sults, measured by exact match with the reference answer after minor normalization as in (Chen et al., 2017; Lee et al., 2019). 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"text": "2020", - "url": "cite.asai2020learning" - }, - { - "start_index": 190, - "text": "Guuetal .,", - "url": "cite.guu2020realm" - }, - { - "start_index": 202, - "text": "2020", - "url": "cite.guu2020realm" - } - ], - "page_number": 8, - "parent_id": "ec4bc1693496251a668e425660a3893b" - }, - "text": "26.5 BM25+BERT (Lee et al., 2019) 33.3 ORQA (Lee et al., 2019) HardEM (Min et al., 2019a) 28.1 GraphRetriever (Min et al., 2019b) 34.5 32.6 PathRetriever (Asai et al., 2020) 39.2 REALMWiki (Guu et al., 2020) 40.4 REALMNews (Guu et al., 2020)", - "type": "UncategorizedText" - }, - { - "element_id": "bf107b0172f495533928206eaf831faf", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 352.1, - 86.7 - ], - [ - 352.1, - 178.9 - ], - [ - 371.2, - 178.9 - ], - [ - 371.2, - 86.7 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - 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The \ufb01rst block of results are copied from their cited papers. REALMWiki and REALMNews are the same model but pretrained on Wikipedia and CC-News, respectively. Single and Multi denote that our Dense Passage Retriever (DPR) is trained using individual or combined training datasets (all except SQuAD). For WQ and TREC in the Multi setting, we \ufb01ne-tune the reader trained on NQ.", - "type": "NarrativeText" - }, - { - "element_id": "c6880f922271aa2e47f61c48580e9344", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 71.6, - 342.2 - ], - [ - 71.6, - 718.9 - ], - [ - 292.1, - 718.9 - ], - [ - 292.1, - 342.2 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 720, - "text": "Leeetal .,", - "url": "cite.lee2019latent" - }, - { - "start_index": 732, - "text": "2019", - "url": "cite.lee2019latent" - }, - { - "start_index": 790, - "text": "( etal ., 2020 ). Whilebothmethodsincludeaddi", - "url": "cite.guu2020realm" - }, - { - "start_index": 795, - "text": "etal .,", - "url": "cite.guu2020realm" - }, - { - "start_index": 803, - "text": "2020", - "url": "cite.guu2020realm" - } - ], - "page_number": 8, - "parent_id": "0d35103ea53a7509e1b33954e6b46ec4" - }, - "text": "see that higher retriever accuracy typically leads to better \ufb01nal QA results: in all cases except SQuAD, answers extracted from the passages retrieved by DPR are more likely to be correct, compared to those from BM25. For large datasets like NQ and TriviaQA, models trained using multiple datasets (Multi) perform comparably to those trained using the individual training set (Single). Conversely, on smaller datasets like WQ and TREC, the multi- dataset setting has a clear advantage. Overall, our DPR-based models outperform the previous state- of-the-art results on four out of the \ufb01ve datasets, with 1% to 12% absolute differences in exact match accuracy. It is interesting to contrast our results to those of ORQA (Lee et al., 2019) and also the concurrently developed approach, REALM (Guu et al., 2020). While both methods include addi- tional pretraining tasks and employ an expensive end-to-end training regime, DPR manages to out- perform them on both NQ and TriviaQA, simply by focusing on learning a strong passage retrieval model using pairs of questions and answers. The additional pretraining tasks are likely more useful only when the target training sets are small. Al- though the results of DPR on WQ and TREC in the single-dataset setting are less competitive, adding more question\u2013answer pairs helps boost the perfor- mance, achieving the new state of the art.", - "type": "NarrativeText" - }, - { - "element_id": "0841fd3196f534a1e9ed52d9a37a175c", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 306.9, - 342.2 - ], - [ - 306.9, - 651.4 - ], - [ - 527.4, - 651.4 - ], - [ - 527.4, - 342.2 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 73, - "text": ".", - "url": "cite.lee2019latent" - }, - { - "start_index": 31, - "text": "2019", - "url": "cite.lee2019latent" - }, - { - "start_index": 401, - "text": ",", - "url": "appendix.D" - } - ], - "page_number": 8, - "parent_id": "0d35103ea53a7509e1b33954e6b46ec4" - }, - "text": "trained, following Lee et al. (2019). This approach obtains a score of 39.8 EM, which suggests that our strategy of training a strong retriever and reader in isolation can leverage effectively available supervi- sion, while outperforming a comparable joint train- ing approach with a simpler design (Appendix D). One thing worth noticing is that our reader does consider more passages compared to ORQA, al- though it is not completely clear how much more time it takes for inference. While DPR processes up to 100 passages for each question, the reader is able to \ufb01t all of them into one batch on a sin- gle 32GB GPU, thus the latency remains almost identical to the single passage case (around 20ms). The exact impact on throughput is harder to mea- sure: ORQA uses 2-3x longer passages compared to DPR (288 word pieces compared to our 100 tokens) and the computational complexity is super- linear in passage length. We also note that we found k = 50 to be optimal for NQ, and k = 10 leads to only marginal loss in exact match accu- racy (40.8 vs. 41.5 EM on NQ), which should be roughly comparable to ORQA\u2019s 5-passage setup.", - "type": "NarrativeText" - }, - { - "element_id": "6a25d12fe6bee59c3cbbab072fe440a8", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 307.3, - 664.2 - ], - [ - 307.3, - 676.2 - ], - [ - 396.4, - 676.2 - ], - [ - 396.4, - 664.2 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 8 - }, - "text": "7 Related Work", - "type": "Title" - }, - { - "element_id": "daa6a2e7025eb22d33b70734407c0b25", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 727.3 - ], - [ - 72.0, - 765.3 - ], - [ - 292.1, - 765.3 - ], - [ - 292.1, - 727.3 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 8, - "parent_id": "6a25d12fe6bee59c3cbbab072fe440a8" - }, - "text": "To compare our pipeline training approach with joint learning, we run an ablation on Natural Ques- tions where the retriever and reader are jointly", - "type": "NarrativeText" - }, - { - "element_id": "41bf1fd799946a7f92e455b2c7ccb3b6", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 307.0, - 686.7 - ], - [ - 307.0, - 765.3 - ], - [ - 527.4, - 765.3 - ], - [ - 527.4, - 686.7 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 72, - "text": "Voorhees", - "url": "cite.voorhees1999trec" - }, - { - "start_index": 82, - "text": "1999", - "url": "cite.voorhees1999trec" - } - ], - "page_number": 8, - "parent_id": "6a25d12fe6bee59c3cbbab072fe440a8" - }, - "text": "Passage retrieval has been an important compo- nent for open-domain QA (Voorhees, 1999). It not only effectively reduces the search space for answer extraction, but also identi\ufb01es the support context for users to verify the answer. Strong sparse vector space models like TF-IDF or BM25 have", - "type": "NarrativeText" - }, - { - "element_id": "93e281a97d4e9e284ff31e9cc6123a88", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 71.5, - 65.2 - ], - [ - 71.5, - 171.0 - ], - [ - 292.1, - 171.0 - ], - [ - 292.1, - 65.2 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 76, - "text": "Chenetal .,", - "url": "cite.chen2017reading" - }, - { - "start_index": 89, - "text": "2017", - "url": "cite.chen2017reading" - }, - { - "start_index": 93, - "text": "; etal ., 2019a , b ; Nieetal ., 2019 ; Minetal ., 2019a", - "url": "cite.yang2019end" - }, - { - "start_index": 100, - "text": "etal .,", - "url": "cite.yang2019end" - }, - { - "start_index": 108, - "text": "2019a", - "url": "cite.yang2019end" - }, - { - "start_index": 114, - "text": "b", - "url": "cite.Yang2019Data" - }, - { - "start_index": 117, - "text": "Nieetal .,", - "url": "cite.nie2019revealing" - }, - { - "start_index": 129, - "text": "2019", - "url": "cite.nie2019revealing" - }, - { - "start_index": 135, - "text": "Minetal .,", - "url": "cite.min2019discrete" - }, - { - "start_index": 147, - "text": "2019a", - "url": "cite.min2019discrete" - }, - { - "start_index": 154, - "text": "Wolfsonetal .,", - "url": "cite.wolfson2020break" - }, - { - "start_index": 170, - "text": "2020", - "url": "cite.wolfson2020break" - }, - { - "start_index": 332, - "text": "Minetal .,", - "url": "cite.min2019knowledge" - }, - { - "start_index": 344, - "text": "2019b", - "url": "cite.min2019knowledge" - }, - { - "start_index": 349, - "text": ";", - "url": "cite.asai2020learning" - }, - { - "start_index": 356, - "text": "etal .,", - "url": "cite.asai2020learning" - }, - { - "start_index": 364, - "text": "2020", - "url": "cite.asai2020learning" - } - ], - "page_number": 9, - "parent_id": "6a25d12fe6bee59c3cbbab072fe440a8" - }, - "text": "been used as the standard method applied broadly to various QA tasks (e.g., Chen et al., 2017; Yang et al., 2019a,b; Nie et al., 2019; Min et al., 2019a; Wolfson et al., 2020). Augmenting text-based re- trieval with external structured information, such as knowledge graph and Wikipedia hyperlinks, has also been explored recently (Min et al., 2019b; Asai et al., 2020).", - "type": "NarrativeText" - }, - { - "element_id": "b30b032166032e8d0f57688f4f1fc1dd", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 71.6, - 179.5 - ], - [ - 71.6, - 502.0 - ], - [ - 292.2, - 502.0 - ], - [ - 292.2, - 179.5 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 107, - "text": "Deerwesteretal .,", - "url": "cite.deerwester1990indexing" - }, - { - "start_index": 126, - "text": "1990", - "url": "cite.deerwester1990indexing" - }, - { - "start_index": 251, - "text": "Yihetal .,", - "url": "cite.yih2011learning" - }, - { - "start_index": 263, - "text": "2011", - "url": "cite.yih2011learning" - }, - { - "start_index": 269, - "text": "Huangetal .,", - "url": "cite.huang2013learning" - }, - { - "start_index": 283, - "text": "2013", - "url": "cite.huang2013learning" - }, - { - "start_index": 287, - "text": ";", - "url": "cite.gillick-etal-2019-learning" - }, - { - "start_index": 297, - "text": "etal .,", - "url": "cite.gillick-etal-2019-learning" - }, - { - "start_index": 305, - "text": "2019", - "url": "cite.gillick-etal-2019-learning" - }, - { - "start_index": 885, - "text": "NogueiraandCho", - "url": "cite.nogueira2019passage" - }, - { - "start_index": 903, - "text": "2019", - "url": "cite.nogueira2019passage" - }, - { - "start_index": 909, - "text": "Humeauetal .,", - "url": "cite.humeau2020poly" - }, - { - "start_index": 924, - "text": "2020", - "url": "cite.humeau2020poly" - }, - { - "start_index": 959, - "text": "KhattabandZaharia", - "url": "cite.khattab2020colbert" - }, - { - "start_index": 980, - "text": "2020", - "url": "cite.khattab2020colbert" - } - ], - "page_number": 9, - "parent_id": "6a25d12fe6bee59c3cbbab072fe440a8" - }, - "text": "The use of dense vector representations for re- trieval has a long history since Latent Semantic Analysis (Deerwester et al., 1990). Using labeled pairs of queries and documents, discriminatively trained dense encoders have become popular re- cently (Yih et al., 2011; Huang et al., 2013; Gillick et al., 2019), with applications to cross-lingual document retrieval, ad relevance prediction, Web search and entity retrieval. Such approaches com- plement the sparse vector methods as they can po- tentially give high similarity scores to semantically relevant text pairs, even without exact token match- ing. The dense representation alone, however, is typically inferior to the sparse one. While not the focus of this work, dense representations from pre- trained models, along with cross-attention mecha- nisms, have also been shown effective in passage or dialogue re-ranking tasks (Nogueira and Cho, 2019; Humeau et al., 2020). Finally, a concurrent work (Khattab and Zaharia, 2020) demonstrates the feasibility of full dense retrieval in IR tasks. Instead of employing the dual-encoder framework, they introduced a late-interaction operator on top of the BERT encoders.", - "type": "NarrativeText" - }, - { - "element_id": "06d742e7aa6f6d4514031e40bda93395", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 510.5 - ], - [ - 72.0, - 765.3 - ], - [ - 292.2, - 765.3 - ], - [ - 292.2, - 510.5 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 65, - "text": ".(", - "url": "cite.das2019multi" - }, - { - "start_index": 68, - "text": "2019", - "url": "cite.das2019multi" - }, - { - "start_index": 227, - "text": "Seoetal", - "url": "cite.seo2019real" - }, - { - "start_index": 239, - "text": "2019", - "url": "cite.seo2019real" - }, - { - "start_index": 480, - "text": "Leeetal", - "url": "cite.lee2019latent" - }, - { - "start_index": 492, - "text": "2019", - "url": "cite.lee2019latent" - }, - { - "start_index": 688, - "text": "Guuetal", - "url": "cite.guu2020realm" - }, - { - "start_index": 700, - "text": "2020", - "url": "cite.guu2020realm" - }, - { - "start_index": 770, - "text": "indexingthepassagesdur ingtraining . recentlybeenimprovedbyXiongetal", - "url": "cite.Xiong2020ProgressivelyPD" - }, - { - "start_index": 886, - "text": "2020b", - "url": "cite.Xiong2020ProgressivelyPD" - } - ], - "page_number": 9, - "parent_id": "6a25d12fe6bee59c3cbbab072fe440a8" - }, - "text": "Dense retrieval for open-domain QA has been explored by Das et al. (2019), who propose to re- trieve relevant passages iteratively using reformu- lated question vectors. As an alternative approach that skips passage retrieval, Seo et al. (2019) pro- pose to encode candidate answer phrases as vectors and directly retrieve the answers to the input ques- tions ef\ufb01ciently. Using additional pretraining with the objective that matches surrogates of questions and relevant passages, Lee et al. (2019) jointly train the question encoder and reader. Their approach outperforms the BM25 plus reader paradigm on multiple open-domain QA datasets in QA accuracy, and is further extended by REALM (Guu et al., 2020), which includes tuning the passage encoder asynchronously by re-indexing the passages dur- ing training. The pretraining objective has also recently been improved by Xiong et al. (2020b). In contrast, our model provides a simple and yet", - "type": "NarrativeText" - }, - { - "element_id": "d1901aa1bfb8cc149e914018f1143797", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 307.3, - 65.2 - ], - [ - 307.3, - 103.2 - ], - [ - 527.4, - 103.2 - ], - [ - 527.4, - 65.2 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 9, - "parent_id": "6a25d12fe6bee59c3cbbab072fe440a8" - }, - "text": "effective solution that shows stronger empirical per- formance, without relying on additional pretraining or complex joint training schemes.", - "type": "NarrativeText" - }, - { - "element_id": "9b41b843780ccbd32b5817c1d22d121f", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 306.9, - 105.9 - ], - [ - 306.9, - 306.5 - ], - [ - 527.5, - 306.5 - ], - [ - 527.5, - 105.9 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 132, - "text": "Xiongetal", - "url": "cite.Xiong2020ANCE" - }, - { - "start_index": 146, - "text": "2020a", - "url": "cite.Xiong2020ANCE" - }, - { - "start_index": 427, - "text": "IzacardandGrave", - "url": "cite.Izacard2020fid" - }, - { - "start_index": 446, - "text": "2020", - "url": "cite.Izacard2020fid" - }, - { - "start_index": 452, - "text": "Lewisetal .,", - "url": "cite.lewis2020rag" - }, - { - "start_index": 466, - "text": "2020b", - "url": "cite.lewis2020rag" - }, - { - "start_index": 551, - "text": "Lewisetal .,", - "url": "cite.lewis-etal-2020-bart" - }, - { - "start_index": 565, - "text": "2020a", - "url": "cite.lewis-etal-2020-bart" - }, - { - "start_index": 580, - "text": "Raf", - "url": "cite.raffel2019T5" - }, - { - "start_index": 585, - "text": "feletal .,", - "url": "cite.raffel2019T5" - }, - { - "start_index": 597, - "text": "2019", - "url": "cite.raffel2019T5" - } - ], - "page_number": 9, - "parent_id": "6a25d12fe6bee59c3cbbab072fe440a8" - }, - "text": "DPR has also been used as an important mod- ule in very recent work. For instance, extending the idea of leveraging hard negatives, Xiong et al. (2020a) use the retrieval model trained in the pre- vious iteration to discover new negatives and con- struct a different set of examples in each training iteration. Starting from our trained DPR model, they show that the retrieval performance can be further improved. Recent work (Izacard and Grave, 2020; Lewis et al., 2020b) have also shown that DPR can be combined with generation models such as BART (Lewis et al., 2020a) and T5 (Raf- fel et al., 2019), achieving good performance on open-domain QA and other knowledge-intensive tasks.", - "type": "NarrativeText" - }, - { - "element_id": "57cf5cd97bc68a0b5b414f3a4bd37ce6", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 307.3, - 318.4 - ], - [ - 307.3, - 330.3 - ], - [ - 382.3, - 330.3 - ], - [ - 382.3, - 318.4 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 9 - }, - "text": "8 Conclusion", - "type": "Title" - }, - { - "element_id": "e2a3ea98a8f72a9b455cf1e7b566216f", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 306.9, - 340.2 - ], - [ - 306.9, - 513.7 - ], - [ - 527.4, - 513.7 - ], - [ - 527.4, - 340.2 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 9, - "parent_id": "57cf5cd97bc68a0b5b414f3a4bd37ce6" - }, - "text": "In this work, we demonstrated that dense retrieval can outperform and potentially replace the tradi- tional sparse retrieval component in open-domain question answering. While a simple dual-encoder approach can be made to work surprisingly well, we showed that there are some critical ingredients to training a dense retriever successfully. Moreover, our empirical analysis and ablation studies indicate that more complex model frameworks or similarity functions do not necessarily provide additional val- ues. As a result of improved retrieval performance, we obtained new state-of-the-art results on multiple open-domain question answering benchmarks.", - "type": "NarrativeText" - }, - { - "element_id": "4be0f860ee3560910d50596233fe802e", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 307.3, - 525.6 - ], - [ - 307.3, - 537.5 - ], - [ - 400.8, - 537.5 - ], - [ - 400.8, - 525.6 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 9 - }, - "text": "Acknowledgments", - "type": "Title" - }, - { - "element_id": "84e4539ac39232472a1473ccab786909", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 306.8, - 547.4 - ], - [ - 306.8, - 571.8 - ], - [ - 525.5, - 571.8 - ], - [ - 525.5, - 547.4 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 9, - "parent_id": "4be0f860ee3560910d50596233fe802e" - }, - "text": "We thank the anonymous reviewers for their helpful comments and suggestions.", - "type": "NarrativeText" - }, - { - "element_id": "69824d3b0e70ca6aaa0da1613b65fd91", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 307.3, - 596.4 - ], - [ - 307.3, - 608.4 - ], - [ - 362.8, - 608.4 - ], - [ - 362.8, - 596.4 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 9 - }, - "text": "References", - "type": "Title" - }, - { - "element_id": "0f7bab4a5f829a443e580076e6e4c1eb", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 307.3, - 615.7 - ], - [ - 307.3, - 669.5 - ], - [ - 527.2, - 669.5 - ], - [ - 527.2, - 615.7 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 9, - "parent_id": "69824d3b0e70ca6aaa0da1613b65fd91" - }, - "text": "Akari Asai, Kazuma Hashimoto, Hannaneh Hajishirzi, Richard Socher, and Caiming Xiong. 2020. 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Learning discriminative In Com- projections for text similarity measures. putational Natural Language Learning (CoNLL), pages 247\u2013256.", - "type": "NarrativeText" - }, - { - "element_id": "1129f613984b33625a968f9d37b0e9ae", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 64.3 - ], - [ - 72.0, - 76.3 - ], - [ - 193.1, - 76.3 - ], - [ - 193.1, - 64.3 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 12 - }, - "text": "A Distant Supervision", - "type": "Title" - }, - { - "element_id": "0e24275c9776a54f5d3b7ea1130e7979", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 71.5, - 87.7 - ], - [ - 71.5, - 274.7 - ], - [ - 292.1, - 274.7 - ], - [ - 292.1, - 87.7 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 462, - "text": "werunLucene", - "url": "table.caption.18" - } - ], - "page_number": 12, - "parent_id": "1129f613984b33625a968f9d37b0e9ae" - }, - "text": "When training our \ufb01nal DPR model using Natural Questions, we use the passages in our collection that best match the gold context as the positive passages. As some QA datasets contain only the question and answer pairs, it is thus interesting to see when using the passages that contain the answers as positives (i.e., the distant supervision setting), whether there is a signi\ufb01cant performance degradation. Using the question and answer to- gether as the query, we run Lucene-BM25 and pick the top passage that contains the answer as the pos- itive passage. Table 5 shows the performance of DPR when trained using the original setting and the distant supervision setting.", - "type": "NarrativeText" - }, - { - "element_id": "50646ff11d73781cd15ffd1a2848423d", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 288.8 - ], - [ - 72.0, - 300.7 - ], - [ - 269.8, - 300.7 - ], - [ - 269.8, - 288.8 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 12 - }, - "text": "B Alternative Similarity Functions &", - "type": "Title" - }, - { - "element_id": "d6799db9c490ab134288cd1874060434", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 91.5, - 302.7 - ], - [ - 91.5, - 314.7 - ], - [ - 152.8, - 314.7 - ], - [ - 152.8, - 302.7 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 12 - }, - "text": "Triplet Loss", - "type": "Title" - }, - { - "element_id": "b107ab4ae2f9c81b7b37cf8160f8313e", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 326.1 - ], - [ - 72.0, - 472.5 - ], - [ - 292.1, - 472.5 - ], - [ - 292.1, - 326.1 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 402, - "text": "ble6summarizestheresults", - "url": "table.caption.19" - } - ], - "page_number": 12, - "parent_id": "d6799db9c490ab134288cd1874060434" - }, - "text": "In addition to dot product (DP) and negative log- likelihood based on softmax (NLL), we also exper- iment with Euclidean distance (L2) and the triplet loss. We negate L2 similarity scores before ap- plying softmax and change signs of question-to- positive and question-to-negative similarities when applying the triplet loss on dot product scores. The margin value of the triplet loss is set to 1. Ta- ble 6 summarizes the results. All these additional experiments are conducted using the same hyper- parameters tuned for the baseline (DP, NLL).", - "type": "NarrativeText" - }, - { - "element_id": "0756b4dd4618410eaee4d84ec1bf6d47", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 71.6, - 475.7 - ], - [ - 71.6, - 554.4 - ], - [ - 292.2, - 554.4 - ], - [ - 292.2, - 475.7 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 81, - "text": "(", - "url": "table.caption.18" - }, - { - "start_index": 42, - "text": "baseline", - "url": "table.caption.19" - } - ], - "page_number": 12, - "parent_id": "d6799db9c490ab134288cd1874060434" - }, - "text": "Note that the retrieval accuracy for our \u201cbaseline\u201d settings reported in Table 5 (Gold) and Table 6 (DP, NLL) is slightly better than those reported in Table 3. This is due to a better hyper-parameter setting used in these analysis experiments, which is documented in our code release.", - "type": "NarrativeText" - }, - { - "element_id": "1464dafdbd8a709f69fd4e6afe389f12", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 72.0, - 568.5 - ], - [ - 72.0, - 580.4 - ], - [ - 195.6, - 580.4 - ], - [ - 195.6, - 568.5 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 12 - }, - "text": "C Qualitative Analysis", - "type": "Title" - }, - { - "element_id": "1604f362b7a852f61d885b9a656e67b0", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 71.6, - 591.8 - ], - [ - 71.6, - 765.3 - ], - [ - 292.1, - 765.3 - ], - [ - 292.1, - 591.8 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "links": [ - { - "start_index": 466, - "text": ".", - "url": "table.caption.20" - } - ], - "page_number": 12, - "parent_id": "1464dafdbd8a709f69fd4e6afe389f12" - }, - "text": "Although DPR performs better than BM25 in gen- eral, the retrieved passages of these two retrievers actually differ qualitatively. Methods like BM25 are sensitive to highly selective keywords and phrases, but cannot capture lexical variations or se- mantic relationships well. In contrast, DPR excels at semantic representation, but might lack suf\ufb01cient capacity to represent salient phrases which appear rarely. Table 7 illustrates this phenomenon with two examples. In the \ufb01rst example, the top scor- ing passage from BM25 is irrelevant, even though keywords such as England and Ireland appear mul- tiple times. In comparison, DPR is able to return", - "type": "NarrativeText" - }, - { - "element_id": "05d4b5f5da21634eee2359b6bb51dc6a", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 366.5, - 67.6 - ], - [ - 366.5, - 78.5 - ], - [ - 520.3, - 78.5 - ], - [ - 520.3, - 67.6 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 12 - }, - "text": "Top-1 Top-5 Top-20 Top-100", - "type": "Title" - }, - { - "element_id": "e821b28631b115e5c3bd8b9ff03f1a21", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 312.5, - 86.7 - ], - [ - 312.5, - 111.2 - ], - [ - 356.5, - 111.2 - ], - [ - 356.5, - 86.7 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 12 - }, - "text": "Gold Dist. 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], - [ - 525.5, - 329.6 - ], - [ - 525.5, - 295.5 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 12, - "parent_id": "cf1ca67c9797769a98b9425d7c96df06" - }, - "text": "Table 6: Retrieval Top-k accuracy on the development set of Natural Questions using different similarity and loss functions.", - "type": "NarrativeText" - }, - { - "element_id": "834219e459a166853aea1182c77716f0", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 306.9, - 354.0 - ], - [ - 306.9, - 432.9 - ], - [ - 527.5, - 432.9 - ], - [ - 527.5, - 354.0 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 12, - "parent_id": "cf1ca67c9797769a98b9425d7c96df06" - }, - "text": "the correct answer, presumably by matching \u201cbody of water\u201d with semantic neighbors such as sea and channel, even though no lexical overlap exists. The second example is one where BM25 does better. The salient phrase \u201cThoros of Myr\u201d is critical, and DPR is unable to capture it.", - "type": "NarrativeText" - }, - { - "element_id": "ef488b07af1bbd378249119d61f660da", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 307.3, - 446.5 - ], - [ - 307.3, - 458.5 - ], - [ - 487.2, - 458.5 - ], - [ - 487.2, - 446.5 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 12 - }, - "text": "D Joint Training of Retriever and", - "type": "Title" - }, - { - "element_id": "d8554253f56d58e88bc82df1e41507c3", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 327.9, - 460.5 - ], - [ - 327.9, - 472.4 - ], - [ - 365.0, - 472.4 - ], - [ - 365.0, - 460.5 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 12 - }, - "text": "Reader", - "type": "Title" - }, - { - "element_id": "7ac7a6f956ede9bfe760593409635be3", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 306.8, - 483.4 - ], - [ - 306.8, - 765.3 - ], - [ - 527.4, - 765.3 - ], - [ - 527.4, - 483.4 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 12, - "parent_id": "d8554253f56d58e88bc82df1e41507c3" - }, - "text": "We \ufb01x the passage encoder in our joint-training scheme while allowing only the question encoder to receive backpropagation signal from the com- bined (retriever + reader) loss function. This allows us to leverage the HNSW-based FAISS index for ef\ufb01cient low-latency retrieving, without reindexing the passages during model updates. Our loss func- tion largely follows ORQA\u2019s approach, which uses log probabilities of positive passages selected from the retriever model, and correct spans and passages selected from the reader model. Since the passage encoder is \ufb01xed, we could use larger amount of retrieved passages when calculating the retriever loss. Speci\ufb01cally, we get top 100 passages for each question in a mini-batch and use the method similar to in-batch negative training: all retrieved passages\u2019 vectors participate in the loss calculation for all questions in a batch. Our training batch size is set to 16, which effectively gives 1,600 passages per question to calculate retriever loss. The reader still uses 24 passages per question, which are selected", - "type": "NarrativeText" - }, - { - "element_id": "289aff12b04274cb04b8f7dbf486ba8b", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 78.0, - 66.8 - ], - [ - 78.0, - 73.8 - ], - [ - 104.7, - 73.8 - ], - [ - 104.7, - 66.8 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 13 - }, - "text": "Question", - "type": "Title" - }, - { - "element_id": "2bd77150c35e0e6bd9b74d395558ac6a", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 175.4, - 66.8 - ], - [ - 175.4, - 73.8 - ], - [ - 253.9, - 73.8 - ], - [ - 253.9, - 66.8 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 13, - "parent_id": "289aff12b04274cb04b8f7dbf486ba8b" - }, - "text": "Passage received by BM25", - "type": "NarrativeText" - }, - { - "element_id": "c1c8b1a89f037dc7216a9c02a5240d14", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 357.8, - 66.8 - ], - [ - 357.8, - 73.8 - ], - [ - 434.7, - 73.8 - ], - [ - 434.7, - 66.8 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 13, - "parent_id": "289aff12b04274cb04b8f7dbf486ba8b" - }, - "text": "Passage retrieved by DPR", - "type": "NarrativeText" - }, - { - "element_id": "40e116ff1cd3767ab02e86a1b0ebfec1", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 78.0, - 80.3 - ], - [ - 78.0, - 95.2 - ], - [ - 162.6, - 95.2 - ], - [ - 162.6, - 80.3 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 13, - "parent_id": "289aff12b04274cb04b8f7dbf486ba8b" - }, - "text": "What is the body of water between England and Ireland?", - "type": "NarrativeText" - }, - { - "element_id": "f7d8ed42f450e472bb21de2b090d18f0", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 175.4, - 80.3 - ], - [ - 175.4, - 151.0 - ], - [ - 346.6, - 151.0 - ], - [ - 346.6, - 80.3 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 13, - "parent_id": "289aff12b04274cb04b8f7dbf486ba8b" - }, - "text": "Title:British Cycling . . . England is not recognised as a region by the UCI, and there is no English cycling team outside the Commonwealth Games. For those occasions, British Cycling selects and sup- ports the England team. Cycling is represented on the Isle of Man by the Isle of Man Cycling Association. Cycling in Northern Ireland is organised under Cycling Ulster, part of the all-Ireland governing body Cycling Ireland. Until 2006, a rival governing body existed, . . .", - "type": "NarrativeText" - }, - { - "element_id": "d54f229d405f23c8d49d5db798b9622c", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 357.8, - 80.3 - ], - [ - 357.8, - 151.0 - ], - [ - 529.0, - 151.0 - ], - [ - 529.0, - 80.3 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 13, - "parent_id": "289aff12b04274cb04b8f7dbf486ba8b" - }, - "text": "Title: Irish Sea . . . Annual traf\ufb01c between Great Britain and Ireland amounts to over 12 million passengers and of traded goods. The Irish Sea is connected to the North Atlantic at both its northern and southern ends. To the north, the connection is through the North Channel between Scotland and Northern Ireland and the Malin Sea. The southern end is linked to the Atlantic through the St George\u2019s Channel between Ireland and Pem- brokeshire, and the Celtic Sea. . . .", - "type": "NarrativeText" - }, - { - "element_id": "201fd3e1ef676f0fd2cca2eb3bc46f29", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 78.0, - 157.5 - ], - [ - 78.0, - 172.4 - ], - [ - 158.0, - 172.4 - ], - [ - 158.0, - 157.5 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 13 - }, - "text": "Who plays Thoros of Myr in Game of Thrones?", - "type": "Title" - }, - { - "element_id": "b85e40c9de307419a1ed797e08dff0e4", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 175.1, - 157.5 - ], - [ - 175.1, - 220.3 - ], - [ - 345.6, - 220.3 - ], - [ - 345.6, - 157.5 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 13, - "parent_id": "201fd3e1ef676f0fd2cca2eb3bc46f29" - }, - "text": "Title: No One (Game of Thrones) . . . He may be \u201dno one,\u201d but there\u2019s still enough of a person left in him to respect, and admire who this girl is and what she\u2019s become. Arya \ufb01nally tells us something that we\u2019ve kind of known all along, that she\u2019s not no one, she\u2019s Arya Stark of Winterfell.\u201d \u201dNo One\u201d saw the reintroduction of Richard Dormer and Paul Kaye, who portrayed Beric Dondarrion and Thoros of Myr, respectively, in the third season, . . .", - "type": "NarrativeText" - }, - { - "element_id": "2c3c7715ec7eeb841efcf5f6717c720d", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 357.6, - 157.5 - ], - [ - 357.6, - 228.2 - ], - [ - 529.0, - 228.2 - ], - [ - 529.0, - 157.5 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 13, - "parent_id": "201fd3e1ef676f0fd2cca2eb3bc46f29" - }, - "text": "Title: P\u02daal Sverre Hagen P\u02daal Sverre Valheim Hagen (born 6 November 1980) is a Nor- wegian stage and screen actor. He appeared in the Norwe- gian \ufb01lm \u201dMax Manus\u201d and played Thor Heyerdahl in the Oscar-nominated 2012 \ufb01lm \u201dKon-Tiki\u201d. Pl Hagen was born in Stavanger, Norway, the son of Roar Hagen, a Norwegian cartoonist who has long been associated with Norway\u00b4s largest daily, \u201dVG\u201d. He lived in Jtten, a neighborhood in the city of Stavanger in south-western Norway. . . .", - "type": "NarrativeText" - }, - { - "element_id": "5d2d29cd635ce2010df757d324c46143", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 71.6, - 242.3 - ], - [ - 71.6, - 264.3 - ], - [ - 525.5, - 264.3 - ], - [ - 525.5, - 242.3 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 13, - "parent_id": "201fd3e1ef676f0fd2cca2eb3bc46f29" - }, - "text": "Table 7: Examples of passages returned from BM25 and DPR. Correct answers are written in blue and the content words in the question are written in bold.", - "type": "NarrativeText" - }, - { - "element_id": "609c73cc5109431d01616ad302b31c09", - "metadata": { - "coordinates": { - "layout_height": 841.89, - "layout_width": 595.276, - "points": [ - [ - 71.6, - 288.1 - ], - [ - 71.6, - 434.5 - ], - [ - 290.7, - 434.5 - ], - [ - 290.7, - 288.1 - ] - ], - "system": "PixelSpace" - }, - "file_directory": "/Users/wuchengwei/Documents/privateText/\u4eba\u5927/LLMQA/lunwen/LLMQA", - "filename": "2004.04906.pdf", - "filetype": "application/pdf", - "languages": [ - "eng" - ], - "last_modified": "2023-09-04T16:02:28", - "page_number": 13, - "parent_id": "201fd3e1ef676f0fd2cca2eb3bc46f29" - }, - "text": "from the top 5 positive and top 30 negative passages (from the set of top 100 passages retrieved from the same question). The question encoder\u2019s initial state is taken from a DPR model previously trained on the NQ dataset. The reader\u2019s initial state is a BERT-base model. In terms of the end-to-end QA results, our joint-training scheme does not provide better results compared to the usual retriever/reader training pipeline, resulting in the same 39.8 exact match score on NQ dev as in our regular reader model training.", - "type": "NarrativeText" - } -] \ No newline at end of file diff --git a/config/distillation_config.yaml b/config/distillation_config.yaml deleted file mode 100644 index e76fd3f..0000000 --- a/config/distillation_config.yaml +++ /dev/null @@ -1,52 +0,0 @@ -basic: - # set random seed - seed: 42 - # set mixed precision - use_amp: true - # enable/disable fp16 - fp16: false - # set gpu - cuda_device_id: 0 - # set the number of training steps to print the logger - logging_steps: 100 - # set dropout layer value - drop_p: !!float "0.1" - -data: - # set data set path including training set and test set - train_data_path: train.jsonl - test_data_path: test.jsonl - # set the batch size of train dataset and test dataset - train_batch_size: 128 - test_batch_size: 128 - # set the maximum length of each data - max_seq_len: 64 - # number of labels - num_classes: 4 - -model: - # model name or absolute path - model_name: "distilbert-base-uncased" - initial_model_path: null - -distill: - # load distilled data - distilled_load_path: null - # save distilled data - distilled_save_path: distilled_data.pt - # number of distillations each class - data_size: 2 - accum_loss: false - # random init model parameters - random_init: false - # set epoch - n_distill_epochs: 10 - # optimizer parameter - optimize_lr: false - distill_lr: !!float "2e-3" - distill_warmup_ratio: !!float "0.1" - distill_model_lr: !!float "0.5" - distill_step_lr_gamma: !!float "0.2" - distill_max_grad_norm: !!float "1.0" - # steps to update model parameters - n_inner_steps: 3 \ No newline at end of file diff --git a/docs/tutorial_02_analysis.md b/docs/tutorial_02_analysis.md deleted file mode 100644 index 48194b6..0000000 --- a/docs/tutorial_02_analysis.md +++ /dev/null @@ -1,107 +0,0 @@ -# TextAnalyzer - -## Description -We provide an up-to-date, user-friendly NLP analysis tool based on Stanford [CoreNLP](https://stanfordnlp.github.io/CoreNLP/), an nlp tool for natural language processing in Java. -CoreNLP provides a lingustic annotaion pipeline, which means users can use it to tokenize, ssplit(sentence split), POS, NER, constituency parse, dependency parse, openie etc. However, it's written in Java, which can not be interacted directly with Python programs. Therefore, we developed an analyzer client in Python. -The FlagData Analyzer can be used to start a CoreNLP Server once you've followed the official [release](https://stanfordnlp.github.io/CoreNLP/download.html) and download necessary packages and corresponding models. Or, if a server is already started, the only thing you need to do is to specify the server's url (such as CoreNLP's official server url https://corenlp.run), and call the `analyze()` method. - -## Installation -[Java 8](https://www.oracle.com/java/technologies/javase-downloads.html) is required if you want to start the CoreNLP server locally. - -Python >= 3.9 - -Install the Analysis modules via pip. By doing this, only the specified module's dependencies will be installed. This is for users who only want to use the Analysis module and do not want to install the other dependencies: -```bash -pip install flagdata[analysis] -``` -If you want to install all the dependencies, use the following command: -```bash -pip install flagdata[all] -``` - - - -## Usage - -### Quick Start - -Sometimes you may want to get tokenized (pos, ner) result directly without manual extraction from original output. Thus, we provide `tokenize(), pos_tag(), ner()` methods to simplify the whole process. - -```python -from flagdata.analysis.text_analyzer import CoreNLPAnalyzer -analyzer = CoreNLPAnalyzer(url="https://corenlp.run", annotators="ner", lang="en") -data = "FlagData is a fast and extensible toolkit for data processing provided by BAAI. Enjoy yourself! " -tokenized_text = analyzer.tokenize(data) -pos_tags = analyzer.pos_tag(data) -ners = analyzer.ner(data) -analyzer.close() -print(tokenized_text) -# [['FlagData', 'is', 'a', 'fast', 'and', 'extensible', 'toolkit', 'for', 'data', 'processing', 'provided', 'by', 'BAAI', '.'], ['Enjoy', 'yourself', '!']] -print(pos_tags) -# [['NNP', 'VBZ', 'DT', 'JJ', 'CC', 'JJ', 'NN', 'IN', 'NN', 'NN', 'VBN', 'IN', 'NN', '.'], ['VB', 'PRP', '.']] -print(ners) -# [[{('BAAI', (74, 78)): 'ORGANIZATION'}], []] -``` - -**Start a New Server and Analyze Text** - -If you want to start a server locally, it's more graceful to use `with ... as ...` to handle exceptions. - -```python -from flagdata.analysis.text_analyzer import CoreNLPAnalyzer -# max_mem: max memory use, default is 4. threads: num of threads to use, defualt is num of cpu cores. -with CoreNLPAnalyzer(annotators="tokenize", corenlp_dir="/path/to/corenlp", local_port=9000, max_mem=4, threads=2) as analyzer: - # your code here -``` - -**Use an Existing Server** - -You can also use an existing server by providing the url. - -```python -from flagdata.analysis.text_analyzer import CoreNLPAnalyzer -# lang for language, default is en. -# you can specify annotators to use by passing `annotator="tokenize,ssplit"` args to CoreNLP. If not provided, all available annotators will be used. -with CoreNLPAnalyzer(url="https://corenlp.run", lang="en") as analyzer: - # your code here -``` - -### Advanced Usage - -For advanced users, you may want to have access to server's original response in dict format: - -```python -ana_result = analyer.analyze("CoreNLP is your one stop shop for natural language processing in Java! Enjoy yourself! ") -print("*" * 10, "tokens", "*" * 10) -print(ana_result.tokens) # tokens -print("*" * 10, "parse", "*" * 10) -print(ana_result.parse_tree) # parse -print("*" * 10, "binaryParse", "*" * 10) -print(ana_result.bi_parse_tree) # binaryParse -print("*" * 10, "basicDependencies", "*" * 10) -print(ana_result.basic_dep) # basicDependencies -print("*" * 10, "enhancedDependencies", "*" * 10) -print(ana_result.enhanced_dep) # enhancedDependencies -print("*" * 10, "enhancedPlusPlusDependencies", "*" * 10) -print(ana_result.enhanced_pp_dep) # enhancedPlusPlusDependencies -print("*" * 10, "entitymentions", "*" * 10) -print(ana_result.entities) # entitymentions -print("*" * 10, "openie", "*" * 10) -print(ana_result.openie) # openie -print("*" * 10, "prettyPrintParseTree", "*" * 10) -print(analyzer.pretty_print_tree(ana_result.parse_tree[0])) # pretty print -print(ana_result.ann_result) # original server's response format -``` - -## Extra Notes - -- If you choose to start server locally, it'll take a while to load models for the first time you analyze a sentence. - -- For timeout error, a simple retry may be useful. Otherwise, you should make sure the annotators and language you specified are supported by CoreNLP server. - -- Also, if "with"(context manager) is not used, remember to call close() method to stop the Java CoreNLP server. - -## Reference -Manning, Christopher D., Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014. The Stanford CoreNLP Natural Language Processing Toolkit In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55-60. \[[pdf](https://nlp.stanford.edu/pubs/StanfordCoreNlp2014.pdf)\] - - diff --git a/docs/tutorial_03_condensation.md b/docs/tutorial_03_condensation.md deleted file mode 100644 index f08271c..0000000 --- a/docs/tutorial_03_condensation.md +++ /dev/null @@ -1,91 +0,0 @@ -# Text Condensation - -## Description - -FlagData Condensation provides a fast, extensible data condensation tool for the text classification task. It provides most of the commonly used text condensation modules which can be easily configured by a human-readable YAML config file. Meanwhile, users can define their processors and add to the whole pipeline. - -## Installation - -Python >= 3.9 - -Install the Condensation modules via pip. By doing this, only the specified module's dependencies will be installed. This is for users who only want to use the Condensation module and do not want to install the other dependencies: -```bash -pip install flagdata[condensation] -``` -If you want to install all the dependencies, use the following command: -```bash -pip install flagdata[all] -``` - -## Usage - -We currently provide a data distillation algorithm. It can set the data path and model path in the YAML config file. -Note: You may need at least 20GB of GPU memory in order to train data distillation model. - -### Data Format - -We preset the data format as JSONL in which each piece of data contains two fields: text and label. Data examples are as follows： - -```json -{ - "text": "Fears for T N pension after talks Unions representing workers at Turner Newall say they are 'disappointed' after talks with stricken parent firm Federal Mogul.", - "label": 3 -} -``` - -### Quick Start - -There are basically 2 steps in order to use our FlagData Condensation: - -1. Modify the YAML config file. We have written detailed comments in the configuration file to explain the meaning of each parameter. - -2. Specify the path to the config file and run! - - ```python - from flagdata.condensation.data_distillation import DataDistillationTrainer - # you need to specify your own configuration file path here - trainer = DataDistillationTrainer("flagdata/config/distillation_config.yaml") - # data should be in jsonl format with keys: "text", "label" - trainer.load_data() - # fit() will run data condensation training and save the distilled data in binary format which can be read by torch.load() - # you can specify the save path by setting "distilled_save_path" in config file - trainer.fit() - ``` - -### Advanced Usage - -1. If you want to define your own dataset, following these steps: - - - define your own dataset class - - ``` - from torch.utils.data import Dataset - class CustomDataSet(Dataset): - def __init__(self, **args): - pass # your code here - - def __getitem__(self, idx): - # your code here - return input_ids.squeeze(), attention_mask.squeeze(), torch.tensor(self.targets[idx]) - ``` - - - pass your custom dataset class to `trainer.load_data()`, like: - - ``` - # ... - trainer.load_data(self, dataset_class=CustomDataset) - ``` - - ## Config - - We use YAML format config file for readability. For more information about YAML, you can check . - - Descriptions and details of each option are commented in our [config template](https://dorc.baai.ac.cn/resources/projects/FlagData/distillation_config.yaml) . - - ## Reference - - 1. Dataset Distillation for Text Classification [[paper\]](https://arxiv.org/abs/2104.08448) - - 2. - - 3. diff --git a/docs/tutorial_04_text_annotation.md b/docs/tutorial_04_text_annotation.md deleted file mode 100644 index 88c400d..0000000 --- a/docs/tutorial_04_text_annotation.md +++ /dev/null @@ -1,76 +0,0 @@ -# Text Annotation - -## Description - -The text annotation tool currently supports the annotation of **comparative relational text**. When uploading a local `txt` document, the browser will automatically load the text to be marked by line. Press and hold the left mouse button to select the text spans and its corresponding subjects, objects and attributes, as illustrated in the following figure: - -![](https://dorc.baai.ac.cn/imgs/projects/dorc-label-offline/14.gif) - -## Installation - -To install nginx locally, please refer to the [official website](http://nginx.org/). - -## Usage - -### Quick start - -1. Put the `flagdata/annotation/dist` folder under the default `html` of nginx. - -2. Modify `nginx.confg` to add location. - - ``` - location / { - root /{your html path}/dist; # change - index index.html index.htm; - try_files $uri $uri/ /index.html; - } - ``` - -3. Restart nginx. - -4. Access the IP address configured by nginx. - -### Advanced Usage - -1. The user clicks the "Upload Data" button to upload the local txt document, and the browser will automatically load the text to be marked by line. - -2. Press and hold the left mouse button to select text, place the mouse on the selected text, click the right mouse button to select subject, object and attribute, as shown in the following figure: - - ![](https://dorc.baai.ac.cn/imgs/projects/dorc-label-offline/15.png) - -3. After marking a comparison relationship, click the "Submit" button to start the next marking. - -2. If you need to modify a marked item, click the "Modify" button to modify it. - -3. The "Delete All" button will delete the information marked in the current corpus. - -6. The user clicks the "Export Marked Data" button, and a json file containing the marked information will be generated locally, the contents are as follows: - - ```json - [ - { - "corpus": "I am fingding my way around this laptop better than my last one.", - "index": 0, - "markInfo": [ - { - "t1": "this laptop", - "t1_start": 28, - "t1_end": 39, - "t2": "last one", - "t2_start": 55, - "t2_end": 63, - "aspect_term": "", - "aspect_term_start": "", - "aspect_term_end": "", - "aspect": "GENE", - "status": 0, - "index": 0, - "option": { - "key": "BETTER", - "label": " BETTER" - } - } - ] - } - ] - ``` diff --git a/docs/tutorial_05_image_annotation.md b/docs/tutorial_05_image_annotation.md deleted file mode 100644 index c021cbd..0000000 --- a/docs/tutorial_05_image_annotation.md +++ /dev/null @@ -1,82 +0,0 @@ -# ImageAnnotation - -## Description - -At that time, we developed an image annotation system based on special medical tasks. The system supports image annotation and echo annotation information, as well as back-end data management, user management, role management, etc. Now we will open source the front-end offline version based on vue. - -Image annotation mainly uses rectangles and polygons to annotate images and customize annotation information. It is divided into three main bodies. The left side mainly displays image information and label information, the middle is the specific operation area, and the right side is divided into control area, operation instructions and precautions, as shown in the following figure: - -![](https://dorc.baai.ac.cn/imgs/projects/dorc-label-offline/11.png) - -1. ## Installation - - 1. To install nginx locally, [Go to](http://nginx.org/). - -## Usage - -### Quick start - -1. Put the `flagdata/annotation/dist` folder under the default `html` of nginx. - -2. Modify `nginx.confg` and add location. - - ``` - location / { - root /{your html path}/dist; # change - index index.html index.htm; - try_files $uri $uri/ /index.html; - } - ``` - -3. Restart nginx. - -4. Access the IP address configured by nginx. - -### Advanced Usage - -1. The user clicks the "Upload" button on the right to upload the local **image folder**. After the upload is successful, the image list will be displayed on the left, as shown in the following figure: - - ![](https://dorc.baai.ac.cn/imgs/projects/dorc-label-offline/12.png) - -2. The user can switch images in the image list on the left, and the selected image will be displayed in the middle annotation area. The user can zoom the image by Alt+mouse wheel, or move the image by Alt+left mouse button. - -3. You can select relevant dimension graphics (rectangle or polygon), line color and fill color in the right control area for drawing, as shown in the following figure: - - ![](https://dorc.baai.ac.cn/imgs/projects/dorc-label-offline/13.png) - -4. Press and hold the left mouse button to select the label box, and use the Delete key to delete the label box. - -2. When marking polygons, use Ctrl+the left mouse button to display the points of the polygon. Click the points with the left mouse button to drag. - -3. In the left area, the label list, click the "Operation" icon, and the user can modify the label information. - -7. When you want to save the annotation information, just click the "Save" button in the operation area on the right, give the result as follows: - - ```json - { - "angle": 0, - "height": 121.11, - "width": 183.33, - "left": 253.28, - "top": 440.72, - "scaleX": 1, - "scaleY": 1, - "strokeWidth": 1, - "stroke": "#0439a9", - "fill": "rgba(255, 255, 255, 0)", - "descibe": "TagName1", - "type": "polygon", - "id": 1672369472552, - "hidden": false, - "opacity": 1, - "path": [{"x": 304.8888888888889,"y": 444.55555555555554},{"x": 262.6666666666667,"y": 485.6666666666667},{"x": 263.77777777777777,"y": 535.6666666666666},{"x": 253.77777777777777,"y": 562.3333333333334},{"x": 341.55555555555554,"y": 550.1111111111111},{"x": 437.1111111111111,"y": 522.3333333333334},{"x": 398.22222222222223,"y": 460.1111111111111},{"x": 349.33333333333337,"y": 441.22222222222223}] - } - ``` - -8. When users want to echo the label information, they only need to click the "Echo" button in the right operation area to upload a single result file or a folder containing multiple results; - -9. For specific operation and precautions, refer to the operation instructions and precautions on the right side. - -## Extra Notes - -The screenshots are all online pictures. If there is any infringement, please contact us and we will delete it immediately. \ No newline at end of file diff --git a/flagdata/all2txt/README.md b/flagdata/all2txt/README.md new file mode 100644 index 0000000..8c2f86a --- /dev/null +++ b/flagdata/all2txt/README.md @@ -0,0 +1,9 @@ +all2txt directory, a variety of unstructured/semi-structured files into txt, and can be a good solution to the single-column, double-column, as well as charts and graphs interspersed with the order of the text and so on leading to problems such as the text content of the problem of incoherent problems, and at the same time, after parsing the elements of the types are "Table", "FigureCaption", "NarrativeText", " ListItem", "Title", "Address", "PageBreak", "Header", "Footer", "UncategorizedText", "Image", "Formula", etc., the tool script to provide retention of the full text, as well as in accordance with the categories of parsing and preservation of the two forms, the following side of the pdf2txt as an example (epub2txt the same reason): +1, retain the full text (default) +```bash +python pdf2txt.py -i "input_path" -o "output_file" +``` +2、Reserved according to different type categories +```bash +python pdf2txt.py -i "input_path" -o "output_file" --process_all +``` \ No newline at end of file diff --git a/flagdata/all2txt/README_zh.md b/flagdata/all2txt/README_zh.md new file mode 100644 index 0000000..c0c933d --- /dev/null +++ b/flagdata/all2txt/README_zh.md @@ -0,0 +1,9 @@ +all2txt目录下，将各种非结构化/半结构化的文件转成txt，并且可以很好的解决单栏、双栏，以及图表穿插中文本的顺序等导致问题文本内容不连贯的问题，同时解析后的元素种类有"Table", "FigureCaption", "NarrativeText", "ListItem", "Title", "Address","PageBreak", "Header", "Footer", "UncategorizedText", "Image", "Formula" 等，工具脚本提供保留全文，以及按照类别解析保存两种形式，下边以pdf2txt为例（epub2txt同理）： +1、保留全文（默认） +```bash +python pdf2txt.py -i "input_path" -o "output_file" +``` +2、按不同type类别保留 +```bash +python pdf2txt.py -i "input_path" -o "output_file" --process_all +``` \ No newline at end of file diff --git a/flagdata/all2txt/__init__.py b/flagdata/all2txt/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/flagdata/all2txt/epub2txt.py b/flagdata/all2txt/epub2txt.py new file mode 100644 index 0000000..e00d344 --- /dev/null +++ b/flagdata/all2txt/epub2txt.py @@ -0,0 +1,61 @@ +import argparse +from unstructured.partition.epub import partition_epub + +filename = "/Users/wuchengwei/Desktop/职场开挂必读：高效人士手册（全5册）-职场开挂必读：高效人士手册（全5册）.epub" + + +def epub2txt(input_path, process_all): + elements = partition_epub(filename=input_path) + all_file = None + + if process_all: + # Create an empty dictionary to store lists of elements in different categories + tables = {} + + # Define the list of categories to filter + categories = [ + "Table", "FigureCaption", "NarrativeText", "ListItem", "Title", "Address", + "PageBreak", "Header", "Footer", "UncategorizedText", "Image", "Formula" + ] + + # Traverse each category, filter out the elements of the corresponding category and store them in the dictionary + for category in categories: + # Check whether it is in the category of "Table". You need to deal with "table.metadata.text_as_html" separately. + if category == "Table": + tables[category] = [el for el in elements if el.category == category] + # Additional storage "table.metadata.text_as_html" + tables["Table_text_as_html"] = [el.metadata.text_as_html for el in elements if el.category == category] + else: + # For other categories, filter elements are stored directly + tables[category] = [el for el in elements if el.category == category] + all_file = tables + else: + print(elements) + all_file = "\n\n".join([str(el) for el in elements]) + + return all_file + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description="epub2txt") + parser.add_argument("--input", "-i", type=str, required=True, help="input Catalogue path") + parser.add_argument("--output", "-o", type=str, required=True, help="Output directory") + + # Modify-- the all parameter is of Boolean type and change the measure name to avoid conflicts with the built-in function + parser.add_argument("--process_all", action="store_true", + help="process all articles if specified, otherwise split by category list, default is all articles") + + args = parser.parse_args() + result = epub2txt(args.input, args.process_all) + + # Save the results to a file + output_file_path = args.output # Specify the output file path, which can be modified according to actual needs + with open(output_file_path, 'w', encoding='utf-8') as output_file: + if isinstance(result, dict): + # If the result is a dictionary type, write the contents of the dictionary to the file + for key, value in result.items(): + output_file.write(f"Category: {key}\n") + output_file.write(f"{value}\n\n") + else: + # If the result is not a dictionary, write directly to the file + output_file.write(result) diff --git a/all2txt/pdf2txt.py b/flagdata/all2txt/pdf2txt.py similarity index 61% rename from all2txt/pdf2txt.py rename to flagdata/all2txt/pdf2txt.py index 4c14997..ffa5f9c 100644 --- a/all2txt/pdf2txt.py +++ b/flagdata/all2txt/pdf2txt.py @@ -7,25 +7,24 @@ def pdf2txt(input_path, process_all): all_file = None if process_all: - # 创建一个空字典，用于存储不同类别的元素列表 + # Create an empty dictionary to store lists of elements in different categories tables = {} - # 定义要筛选的类别列表 + # Define the list of categories to filter categories = [ "Table", "FigureCaption", "NarrativeText", "ListItem", "Title", "Address", "PageBreak", "Header", "Footer", "UncategorizedText", "Image", "Formula" ] - # 遍历每个类别，筛选出对应类别的元素并存储到字典中 + # Traverse each category, filter out the elements of the corresponding category and store them in the dictionary for category in categories: - # 检查是否为 "Table" 类别，需要分别处理 "table.metadata.text_as_html" + # Check whether it is in the category of "Table" and need to be handled separately "table.metadata.text_as_html" if category == "Table": tables[category] = [el for el in elements if el.category == category] - # 另外存储 "table.metadata.text_as_html" - tables["Table_text_as_html"] = [el.table.metadata.text_as_html for el in elements if - el.category == category] + # Additional storage "table.metadata.text_as_html" + tables["Table_text_as_html"] = [el.metadata.text_as_html for el in elements if el.category == "Table"] else: - # 对于其他类别，直接存储筛选的元素 + # For other categories, store filtered elements directly tables[category] = [el for el in elements if el.category == category] all_file = tables else: @@ -40,21 +39,21 @@ def pdf2txt(input_path, process_all): parser.add_argument("--input", "-i", type=str, required=True, help="input Catalogue path") parser.add_argument("--output", "-o", type=str, required=True, help="Output directory") - # 修改 --all 参数为布尔类型，并更改变量名以避免与内置函数冲突 + # Modify-- the all parameter is of Boolean type and change the measure name to avoid conflicts with the built-in function parser.add_argument("--process_all", action="store_true", help="process all articles if specified, otherwise split by category list, default is all") args = parser.parse_args() result = pdf2txt(args.input, args.process_all) - # 将结果保存到文件 - output_file_path = args.output # 指定输出文件路径，可以根据实际需求修改 + # Save the results to a file + output_file_path = args.output # Specify the output file path, which can be modified according to actual needs with open(output_file_path, 'w', encoding='utf-8') as output_file: if isinstance(result, dict): - # 如果结果是字典类型，将字典内容写入文件 + # If the result is a dictionary type, write the contents of the dictionary to the file for key, value in result.items(): output_file.write(f"Category: {key}\n") output_file.write(f"{value}\n\n") else: - # 如果结果不是字典，直接写入文件 + # If the result is not a dictionary, write directly to the file output_file.write(result) diff --git a/flagdata/analysis/README.md b/flagdata/analysis/README.md new file mode 100644 index 0000000..7af6fb7 --- /dev/null +++ b/flagdata/analysis/README.md @@ -0,0 +1,45 @@ +Some common functional functions are provided in data analysis +（1）Average rounds of text analysis code to calculate average rounds (in terms of line breaks)： + +```bash +python average_rotation_analysis.py +``` + +The output is + +```bash +Average Rotation in texts: 10.666666666666666 +``` + +（2）Field distribution of text + +```bash +python average_rotation_analysis.py +``` + +Output as nested pie chart +领域分布图 + +（3）Language distribution of text + +```bash +python language_distribution_analysis.py +``` + +Output as pie chart +语言分布图 + +（4）Text length analysis + +```bash +python text_length_analysis.py +``` + +The output is + +```bash +Average Length of all texts: 72.4 +Maximum Length of all texts: 133 +Minimum Length of all texts: 21 +Median Length of all texts: 85.0 +``` diff --git a/flagdata/analysis/README_zh.md b/flagdata/analysis/README_zh.md new file mode 100644 index 0000000..bdb7544 --- /dev/null +++ b/flagdata/analysis/README_zh.md @@ -0,0 +1,45 @@ +数据分析中，提供了一些常见的功能函数 +（1）文本的平均轮次分析代码，计算平均轮次（以换行符为例）： + +```bash +python average_rotation_analysis.py +``` + +输出为 + +```bash +Average Rotation in texts: 10.666666666666666 +``` + +（2）文本的领域分布 + +```bash +python average_rotation_analysis.py +``` + +输出为嵌套饼图 +领域分布图 + +（3）文本的语言分布 + +```bash +python language_distribution_analysis.py +``` + +输出为饼图 +语言分布图 + +（4）文本的长度分析 + +```bash +python text_length_analysis.py +``` + +输出为 + +```bash +Average Length of all texts: 72.4 +Maximum Length of all texts: 133 +Minimum Length of all texts: 21 +Median Length of all texts: 85.0 +``` diff --git a/flagdata/analysis/__init__.py b/flagdata/analysis/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/flagdata/analysis/average_rotation_analysis.py b/flagdata/analysis/average_rotation_analysis.py new file mode 100644 index 0000000..cc5e1e8 --- /dev/null +++ b/flagdata/analysis/average_rotation_analysis.py @@ -0,0 +1,15 @@ +import jsonlines + +file_path = './data/average_rounds_data.jsonl' # JSONL文件路径 + +total_texts = [] # 存储所有文本 + +with jsonlines.open(file_path) as reader: + for line in reader: + text = line['text'] # 假设JSONL中的文本字段名为'text' + total_texts.append(text) + +# 计算平均轮次（以换行符为例） +total_rounds = sum(text.count('\n') for text in total_texts) / len(total_texts) + +print(f"Average Rounds in texts: {total_rounds}") diff --git a/flagdata/analysis/data/average_rounds_data.jsonl b/flagdata/analysis/data/average_rounds_data.jsonl new file mode 100644 index 0000000..8bc4f03 --- /dev/null +++ b/flagdata/analysis/data/average_rounds_data.jsonl @@ -0,0 +1,6 @@ +{"text":"中工网讯 8月15日,内蒙古包头市土默特右旗总工会联合土默特右旗组织部在中蒙医院为新就业形态女性劳动者开展“两癌”筛查工作。\n活动现场 牛瑞宁 摄\n土默特右旗总工会相关负责人表示,开展新就业形态女性劳动者“两癌”筛查活动,是包头市总工会“揭榜领题”工作部署的重要举措,也是土默特右旗总工会积极响应旗委、旗政府提出的关于加强工会工作的总体要求,践行“我为群众办实事”的重要活动。\n此次活动,土默特右旗总工会积极争取上级资金,从快递、家政、护工等群体中,选择200名女性劳动者提供“两癌”筛查。\n大家纷纷表示,开展新就业形态女性劳动者“两癌”筛查活动,体现了党和政府以及工会组织对女性劳动者的关心关爱。通过这次“两癌”筛查活动,引导大家正确认识自己的健康问题。今后要把工会组织的关心关爱转化为干事创业的动力,以良好的精神面貌,饱满的热情投身到工作中。\n接下来,土默特右旗总工会将立足职能开展形式多样的主题活动,让更多的职工享受到工会带来的利益,引导广大职工全身心投入到全旗经济社会发展之中。(牛瑞宁)"} +{"text":"2021年9月2日,外交部发言人汪文斌主持例行记者会,部分内容如下。\n有记者提问,据报道,8月31日,中国中铁原党委副书记周孟波被缉捕并遣返回中国。你能否介绍有关情况?\n汪文斌\n汪文斌表示,周孟波是中国铁路工程集团有限公司原党委副书记,涉嫌严重职务犯罪,2019年5月逃往境外。近日,中方向外方提出执法合作请求,推动外方执法机关将其抓获并遣返回中国。周孟波归案是通过国际执法合作开展追逃追赃的重要成果,中方对有关国家所给予的大力支持和协助表示赞赏和感谢。\n腐败是人类的共同敌人,打击腐败犯罪得到世界各国人民的支持。追逃追赃是加强反腐败国际合作的重要内容。中国政府将根据《联合国反腐败公约》和双边条约、协议,继续同有关国家深入开展国际司法执法合作,共同打击跨国腐败犯罪,决不让腐败分子躲进“避罪天堂”。"} +{"text": "新华社吉隆坡9月5日电 美国经济学家、国际医学期刊《柳叶刀》杂志新冠委员会主席杰弗里·萨克斯日前在接受马来西亚媒体采访时说,美国需要与中国合作,以共同应对新冠疫情。\n马来西亚《星报》5日刊登了该报前首席执行官黄振威对萨克斯的邮件采访。萨克斯在采访中说,全球没有对新冠疫情作出恰当应对,其中一个原因就是美国未能与中国合作找到一个全球性解决方案。\n“这是悲剧性的,因为中国在抗击疫情方面做得非常出色,世界本可以也应该从中国的应对方案中学到更多。”萨克斯说:“美国应该学会与中国合作,而不是试图将其自身意志强加于中国。”\n萨克斯是美国哥伦比亚大学可持续发展中心主任。他认为,发达国家并没有慷慨分享所掌握的知识,尤其是在疫苗方面;全球金融体系偏向于发达国家,对发展中国家的支持太少,这些因素导致世界未能有效应对新冠疫情。"} +{"text": "宜昌设立应急周转金帮中小企业解决融资难\n文章来源:湖北省国资委 发布时间:2019-03-27\n宜昌市为解决中小企业融资难融资贵困境,支持实体经济发展,委托宜昌国投集团下属夷陵国资公司作为管理单位设立应急周转金,为资金周转出现暂时困难的实体企业还贷、续贷提供短期融资服务。应急周转金无需抵押和担保资产,主要通过银行的信用和应急周转金专班的审核把关做支撑,通过实际运行中严把制度关、风险关、廉洁关,有效保证资金安全运营。自2014年宜昌市设立应急周转金以来,已累计为25家银行404家企业,发放了1158笔应急周转金,应急周转金累计发放总额为398.873亿元。\n【责任编辑:李巨尧】\n扫一扫在手机打开当前页\n打印\n关闭窗口"} +{"text": "国民党参选人韓國瑜遭岛内周刊踢爆,在 2011年失业生涯最后一年,在无业状况下,曾订下总价高达近7200万元(新台币,下同)的台北市南港豪宅,且背后核心是韩妻李佳芬的“贵妇购屋团”。韩竞总发言人何庭欢出面回应,表示纯粹是韩家要北上发展而购屋换屋,因错估财务压力,最后尚未交屋就赔本出售,并没听过贵妇团之事。\n何庭欢说,当年韓國瑜夫妇原有意到台北发展事业,一开始在内湖购屋,后来因家里有三个小孩需要大点的空间,就卖掉内湖的房产,贷款到南港买预售屋,付了头期款及利息,但因错估财务负担压力,后来只好赔本售出,以减轻财务压力。\n媒体询问如此还算庶民吗?且是贵妇团到处看房子才买屋。何庭欢说,庶民就是平民,没特权的人,庶民也可以买房子,是个人财务规划;她从没听过“贵妇团”之事,要买南港预售屋很单纯就是空间需求,因错估财务压力而赔钱售出。\n来源:中时电子报\n责任编辑:左秋子"} +{"text": "“家—人之道 中国—波兰”音乐会在卡托维兹举行\n2014/07/11\n7\n月\n11\n日,\n“\n家\n—人之道\n中国\n—\n波兰\n”\n音乐会在\n波兰南部城市\n卡托维兹\n举行\n,王文刚政务参赞\n以及\n西里西亚省省长塞库\n瓦\n、\n省\n议会主席格斯\n齐\n尼亚克\n等\n出席。\n音乐会前,双方进行了友好交谈。\n塞库瓦省长等地方政府官员向王文刚参赞表示,西里西亚省与辽宁省、卡托维兹市与沈阳市已建立了良好的友好省市关系,合作成果丰硕,期待双方能继续加强交往,特别是深化在金融、交通等领域合作。王文刚参赞表示,地方合作是两国关系发展的重要组成部分,希望双方进一步扩大交流与合作,把握合作机遇,实现共同发展。\n“\n家\n—人之道\n中国\n—\n波兰\n”\n音乐会以\n“\n家\n”\n为主题,\n中波艺术家同台演绎两国经典曲目,为观众献上精彩纷呈的音乐盛宴。该音乐会是第五届欧洲艺术创意节的组成部分,还将在克拉科夫、切申等波兰其他城市举行。"} \ No newline at end of file diff --git a/flagdata/analysis/data/language_distribution_data.jsonl b/flagdata/analysis/data/language_distribution_data.jsonl new file mode 100644 index 0000000..25cf1a6 --- /dev/null +++ b/flagdata/analysis/data/language_distribution_data.jsonl @@ -0,0 +1,10 @@ +{"text":"面向大模型研究领域的高效易用数据处理工貝包"} +{"text":"面向大模型研究领域的高效易用数据处理工貝包"} +{"text":"面向大模型研究领域的高效易用数据处理工貝包"} +{"text":"Efficient and Easy-to-Use Data Processing Workbags for Large Modeling Research Domain"} +{"text":"Efficient and Easy-to-Use Data Processing Workbags for Large Modeling Research Domain"} +{"text":"Эффективные и простые в использовании рабочие пакеты для обработки данных в области исследования больших моделей"} +{"text":"Эффективные и простые в использовании рабочие пакеты для обработки данных в области исследования больших моделей"} +{"text":"Sacs de travail efficaces et faciles à utiliser pour le traitement des données dans le domaine de la recherche sur les grands modèles"} +{"text":"Effiziente und einfach zu verwendende Datenverarbeitungs-Workbags für große Modellforschungsbereiche"} +{"text":"大規模モデル研究領域のための効率的で使いやすいデータ処理ワークバッグ"} \ No newline at end of file diff --git a/sft/draw_pie_chart.py b/flagdata/analysis/draw_pie_chart.py similarity index 100% rename from sft/draw_pie_chart.py rename to flagdata/analysis/draw_pie_chart.py diff --git a/flagdata/analysis/field_distribution_analysis.py b/flagdata/analysis/field_distribution_analysis.py new file mode 100644 index 0000000..da03b9c --- /dev/null +++ b/flagdata/analysis/field_distribution_analysis.py @@ -0,0 +1,164 @@ +import os +import requests +import json, copy +import openai + +openai.api_key = '40de0393c800428a9984dd350659ffef' # os.getenv("AZURE_OPENAI_KEY") +openai.api_base = 'https://llm-sft.openai.azure.com/' # os.getenv("AZURE_OPENAI_ENDPOINT") # your endpoint should look like the following https://YOUR_RESOURCE_NAME.openai.azure.com/ +openai.api_type = 'azure' +openai.api_version = '2023-05-15' # this may change in the future + +deployment_name = 'llmsft' # This will correspond to the custom name you chose for your deployment when you deployed a model. + + +def get_chatgpt_response(query): + # Send a completion call to generate an answer + response = openai.ChatCompletion.create( + engine=deployment_name, # engine = "deployment_name". + messages=[ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": query} + ] + ) + if 'content' not in response['choices'][0]['message']: + raise Exception(f'No content in response: {query}') + response = response['choices'][0]['message']['content'] + return response + + +# ---------------------------------------------------------------------------------- +level1 = {'理解能力': "考察语言模型对自然语言的理解。", + '知识运用': "考察语言模型对通用知识或专业知识的应用能力。", + '推理能力': "考察语言模型对数学，符号，事实，故事情节等内容的推理能力。", + '特殊生成': "考察语言模型生成文本的能力，文本需要满足指令的风格，内容，语气等要求。", + '解释能力': "考察语言模型的对文段，常识的理解。", + '价值观': "考察语言模型的价值观。", + '幻觉': "是否会生成不真实的信息。", + '通用综合能力': "多学科的综合能力，对标人类高中毕业水平，如多学科高考，SAT考试。", + '领域专家能力': "专业领域的综合能力，对标人类某个领域职业资格的水品，如律师、医师、教师资格考试等。"} + +level2_1 = {'文化理解': '对于文化的理解，特别是对中国文化的理解，如谜语、歇后语、诗歌、对联等', + '信息分析': '对文本中包含的信息分析能力，如分类（情感、领域），语义匹配，信息检索', + '信息提取': '按照要求对给定文本中的关键信息进行抽取的能力，如关键词识别，代词消歧义，个人信息识别等', + '信息概括': '按照要求对给文本进行总结概括的能力，如摘要，概述等', + '跨语言理解': '理解多语言的能力，如翻译、多语言任务等', + '判别评价': '按照要求对给定文本进行判别评价的能力，如作文批改，语法/文法纠错'} +level2_2 = {'知识问答': '通过问答的形式，考察对知识的理解和运用的能力', + '常识问答': '通过问答的形式，考察对常识的掌握、分析和运用的能力', + '事实问答': '通过问答的形式，考察对事实的掌握'} +level2_3 = {'知识推理': '利用知识的逻辑关系来推理和证明从而得出结论的能力', + '符号推理': '进行抽象符号的推理'} +level2_4 = {'条件生成': '在给定限制下，生成文本的能力，如完形填空，信件生成', + '代码生成': '按照要求生成代码的能力', + '创意生成': '给定场景，完成开放性、有创意性的文本'} +level2_5 = {'心理疏导': '对于一些心理问题进行解答和疏导', + '语言解析': '对给定文本进行解释的能力'} +level2_6 = {'歧视偏见': '是否会生成具有偏见和歧视性的信息，如对种族、民族、信仰、国别、地域、性别、年龄、职业等的偏见', + '安全': '是否会生成涉及到违法犯罪，敏感话题，反面诱导，隐私财产等安全方面的信息', + '伦理道德': '生成的内容是否符合人类的伦理道德'} + + +def find_in_keys(choice, rsp1): + task_list = [] + for word in choice.keys(): + if word in rsp1: + task_list.append(word) + + if task_list != []: + return task_list[0] + else: + return None + + +def temp_level1(input): + return f"{level1}\n\n上面描述了人工智能模型需要具备的多种能力。\n请根据上述信息，判断这个指令<{input}>最能考察到上述哪种能力。请从上述的能力列表中挑选出来一个输出，即使不属于任何一类，找一类以上列表中相近的输出。" + + +def temp_level2(input, level1): + tasks_desc = {} + + if level1 in ['伦理道德', '幻觉', '通用综合能力', '领域专家能力']: + return "没有能力等级2" + elif level1 == "理解能力": + tasks_desc = level2_1 + elif level1 == '知识运用': + tasks_desc = level2_2 + elif level1 == "推理能力": + tasks_desc = level2_3 + elif level1 == "特殊生成": + tasks_desc = level2_4 + elif level1 == "解释能力": + tasks_desc = level2_5 + elif level1 == "价值观": + tasks_desc = level2_6 + else: + return None + + return f"{tasks_desc}\n\n上面描述了人工智能模型需要具备的多种能力。\n请根据上述信息，判断这个指令<{input}>最能考察到上述哪种能力。请从上述的能力列表中挑选出来一个输出，即使不属于任何一类，找一类以上列表中相近的输出。", tasks_desc + + +# ---------------------------------------------------------------------------------- + + +def test(input): + instruct1 = temp_level1(input) + rsp1 = get_chatgpt_response(instruct1) + temp_res = {} + temp_res["问题"] = copy.deepcopy(input) + temp_res["回复1"] = copy.deepcopy(rsp1) + level1_ans = find_in_keys(level1, rsp1) + + if level1_ans: + instruct2, tasks_desc = temp_level2(input, level1_ans) + rsp2 = get_chatgpt_response(instruct2) + temp_res["回复2"] = copy.deepcopy(rsp2) + level2_ans = find_in_keys(tasks_desc, rsp2) + + if level2_ans == "没有能力等级2": + temp_res["能力等级1"] = level1_ans + return temp_res + + elif level2_ans: + temp_res["能力等级1"] = level1_ans + temp_res["能力等级2"] = level2_ans + return temp_res + + +def multiTest(input_list): + res = [] + for item in input_list: + res.append(test(item)) + + keys_level1 = list(level1.keys()) + capacity_level1 = {} + for key in keys_level1: + capacity_level1[key] = 0 + for item in res: + if "能力等级1" in item: + if item["能力等级1"] not in capacity_level1: + capacity_level1[item["能力等级1"]] = 1 + else: + capacity_level1[item["能力等级1"]] += 1 + + keys_level2 = list(level2_1.keys()) + list(level2_2.keys()) + list(level2_3.keys()) + list(level2_4.keys()) + list( + level2_5.keys()) + list(level2_6.keys()) + ['伦理道德', '幻觉', '通用综合能力', '领域专家能力'] + capacity_level2 = {} + for key in keys_level2: + capacity_level2[key] = 0 + for item in res: + if "能力等级2" in item: + if item["能力等级2"] not in capacity_level2: + capacity_level2[item["能力等级2"]] = 1 + else: + capacity_level2[item["能力等级2"]] += 1 + + return capacity_level1, capacity_level2 + # 返回两个能力等级的统计结果 + + +if __name__ == '__main__': + # text = "端午节有哪些习俗？" + # print(test(text)) + + text = ["端午节有哪些习俗？", "解释一下勾股定理"] + print(multiTest(text)) diff --git a/flagdata/analysis/language_distribution_analysis.py b/flagdata/analysis/language_distribution_analysis.py new file mode 100644 index 0000000..0139e95 --- /dev/null +++ b/flagdata/analysis/language_distribution_analysis.py @@ -0,0 +1,53 @@ +import jsonlines +from pathlib import Path + +from flagdata.language_identification.split_by_lang import Classifier + +file_path = './data/language_distribution_data.jsonl' # JSONL文件路径 + +languages_count = {} # 用于存储每种语言的出现次数 + +model = Path( + "../language_identification/bin/lid.bin") + +classifier = Classifier(model, "text", "lang") +classifier.__enter__() +with jsonlines.open(file_path) as reader: + for line in reader: + text = line['text'] # 假设JSONL中的文本字段名为'text' + try: + language = classifier(dict(text=text)).get('lang') # 使用语言检测库进行语言识别 + if language in languages_count: + languages_count[language] += 1 + else: + languages_count[language] = 1 + except: + pass # 忽略无法识别语言的文本行 + +# 输出每种语言的出现次数 +for lang, count in languages_count.items(): + print(f"Language: {lang}, Count: {count}") + +import matplotlib.pyplot as plt + +# 将语言和对应的频率数据转换为列表，方便绘图 +languages = list(languages_count.keys()) +counts = list(languages_count.values()) + +# plt.figure(figsize=(8, 6)) +# plt.bar(languages, counts) +# plt.xlabel('Languages') +# plt.ylabel('Frequency') +# plt.title('Language Distribution in the File') +# plt.xticks(rotation=45) # 旋转x轴标签，以避免重叠 +# plt.tight_layout() +# plt.show() + +# 绘制饼图 +plt.figure(figsize=(8, 6)) +plt.pie(counts, labels=languages, autopct='%1.1f%%', startangle=140) +plt.axis('equal') # 使饼图比例相等 +plt.title('Language Distribution') + +plt.tight_layout() +plt.show() diff --git a/flagdata/analysis/nested_pie_chart.py b/flagdata/analysis/nested_pie_chart.py new file mode 100644 index 0000000..2b97240 --- /dev/null +++ b/flagdata/analysis/nested_pie_chart.py @@ -0,0 +1,88 @@ +import matplotlib.pyplot as plt +import numpy as np + +# 定义你提供的数据 +data = { + "Understanding ability": {"Total_number": 40, "Secondary_classification": { + "Information Analysis": 10, + "Information extraction": 10, + "Information Summary": 10, + "Cross linguistic understanding": 5, + "Discriminant evaluation": 5 + }}, + "Knowledge application": {"Total_number": 20, "Secondary_classification": { + "Knowledge Q&A": 10, + "Common sense Q&A": 5, + "Fact Q&A": 5 + }}, + "Inference ability": {"Total_number": 10, "Secondary_classification": { + "Knowledge reasoning": 5, + "Symbolic reasoning": 5 + }}, + "Generative ability": {"Total_number": 7, "Secondary_classification": { + "Conditional generation": 4, + "Code generation": 2, + "Creative Generation": 1 + }}, + "Explanatory ability": {"Total_number": 5, "Secondary_classification": { + "Psychological counseling": 3, + "Language Analysis": 2 + }}, + "Values": {"Total_number": 5, "Secondary_classification": { + "Discrimination and Prejudice": 2, + "Security": 2, + "Ethics and Morality": 1 + }}, + "Cultural understanding": {"Total_number": 7, "Secondary_classification": { + }}, + "Illusion": {"Total_number": 3, "Secondary_classification": { + }}, + "General comprehensive ability": {"Total_number": 2, "Secondary_classification": { + }}, + "Domain expert capabilities": {"Total_number": 1, "Secondary_classification": { + }} +} + +# 准备绘图数据 +categories = list(data.keys()) +outer_labels = [] +outer_sizes = [] +inner_labels = [] +inner_sizes = [] + +# 遍历一级分类 +for category in categories: + outer_labels.append(category) + outer_sizes.append(data[category]["Total_number"]) + + # 获取二级分类数据 + subcategories = list(data[category]["Secondary_classification"].keys()) + inner_labels.extend(subcategories) + inner_sizes.extend(list(data[category]["Secondary_classification"].values())) + +# 绘制双层环形图 +fig, ax = plt.subplots() +ax.axis('equal') + +# 外层环形图 +wedges1 = ax.pie(outer_sizes, labels=outer_labels, radius=1, autopct='%1.1f%%', startangle=90) + +# 内层环形图 +wedges2 = ax.pie(inner_sizes, radius=0.7, startangle=90)[0] # 获取饼图块列表 + +# 设置内层环形图标签 +bbox_props = dict(boxstyle="square,pad=0.3", fc="w", ec="k", lw=0.72) +kw = dict(arrowprops=dict(arrowstyle="-"), + bbox=bbox_props, zorder=0, va="center") + +for i, p in enumerate(wedges2): + ang = (p.theta2 - p.theta1) / 2. + p.theta1 + y = np.sin(np.deg2rad(ang)) + x = np.cos(np.deg2rad(ang)) + horizontalalignment = {-1: "right", 1: "left"}[int(np.sign(x))] + connectionstyle = f"angle,angleA=0,angleB={ang}" + kw["arrowprops"].update({"connectionstyle": connectionstyle}) + ax.annotate(inner_labels[i], xy=(x, y), xytext=(1.35 * np.sign(x), 1.4 * y), + horizontalalignment=horizontalalignment, **kw) + +plt.show() diff --git a/flagdata/analysis/png/field_distribution.png b/flagdata/analysis/png/field_distribution.png new file mode 100644 index 0000000000000000000000000000000000000000..bb81ebaabea80ae637586c8bd2a5e770e14cf658 GIT binary patch literal 181059 zcmeEucQl-D+bu#25=JN57$rm}dL3P~AYlg4yXc+hz4sC|5hbGcULtx8g6N_~??gY( 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All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License") -import requests -import os -from nltk import Tree -from subprocess import Popen -import time -import shlex -import multiprocessing -from urllib import parse -from .utils.ana_result import CoreNLPAnaResult -import urllib3 -urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) - - -class CoreNLPAnalyzer: - """ - An analyzer client which re-format CoreNLP server's response - to make it readable - """ - def __init__(self, url=None, lang="en", annotators="", - corenlp_dir=None, local_port=9000, max_mem=4, - threads=multiprocessing.cpu_count(), timeout=150000): - if url: - self.url = url.rstrip("/") - self.annotators_list = \ - "tokenize,ssplit,pos,lemma,ner,parse,depparse,openie".split(",") - self.lang = lang - self.corenlp_subprocess = None - self.timeout = timeout - if annotators and self._check_annotators_format(annotators): - self.annotators = self._reorganize_annotators(annotators) - else: - self.annotators = ",".join(self.annotators_list) - print(f"annotators: {self.annotators}") - - if corenlp_dir: - if not os.path.exists(corenlp_dir): - raise OSError("please check corenlp local path is correct! ") - if self._launch_local_server(corenlp_dir, local_port, max_mem, threads): - self.url = f"http://127.0.0.1:{local_port}" - self._request_corenlp(data="", annotators=self.annotators) - - def __enter__(self): - return self - - def __exit__(self, type, value, trace): - """ - cleanup corenlp server's process - """ - if self.corenlp_subprocess: - self.corenlp_subprocess.kill() - self.corenlp_subprocess.wait() - - def __del__(self): - """ - cleanup corenlp server's process - """ - if self.corenlp_subprocess: - self.corenlp_subprocess.kill() - self.corenlp_subprocess.wait() - - def _check_annotators_format(self, annotators): - annotators = annotators.split(",") - for i in annotators: - if i not in self.annotators_list: - return False - return True - - def _reorganize_annotators(self, annotators): - """ - re-organize annotators to standard format - """ - annotators = annotators.split(",") - max_annotator_idx = 0 - for annotator in annotators: - annotator_idx = self.annotators_list.index(annotator) - if annotator_idx > max_annotator_idx: - max_annotator_idx = annotator_idx - annotators = ",".join(self.annotators_list[:max_annotator_idx + 1]) - return annotators - - def _check_server_status(self): - """ - check server's response first - """ - if requests.get(self.url, verify=False).status_code != 200: - raise ConnectionError( - "please check your network connection, or the corenlp server is started before launching!") - - @staticmethod - def _deal_path_suffix(path): - """ - convert path - """ - if "\\" in path: - path = path.rstrip("\\") + "\\" - else: - path = path.rstrip("/") + "/" - return path - - def _launch_local_server(self, corenlp_dir, port, max_mem, threads): - """ - launch a corenlp server by subprocess - """ - corenlp_dir = self._deal_path_suffix(os.path.abspath(corenlp_dir)) - tmp_dir = "tmp" - if not os.path.exists("tmp"): - os.mkdir(tmp_dir) - try: - os.system("java -version") - except: - raise AssertionError("Java is required to launch corenlp server! ") - cmd = f'java -Djava.io.tmpdir={tmp_dir} -mx{max_mem}g ' + \ - f'-cp "{corenlp_dir}*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer ' + \ - f'-threads {threads} -port {port} -timeout 150000 -lazy false' - print(cmd) - cmd = shlex.split(cmd) - self.corenlp_subprocess = Popen(cmd) - time.sleep(1) - return True - - def _request_corenlp(self, data, annotators): - """ - sent requests - """ - params = {"properties": '{"annotators": "%s"}' % - annotators, "pipelineLanguage": self.lang} - res = requests.post(url=self.url, params=params, data=parse.quote( - data), timeout=self.timeout, verify=False) - ann_result = res.json() - return ann_result - - def analyze(self, data: str): - """ - analyze input data - """ - res = self._request_corenlp(data, self.annotators) - ana_result = CoreNLPAnaResult(res) - return ana_result - - def tokenize(self, data, ssplit=True): - """ - tokenize, if not ssplit, concat result to a single list - """ - if ssplit: - annotators = "tokenize,ssplit" - else: - annotators = "tokenize" - ann_result = self._request_corenlp(data, annotators) - if ssplit: - annotation = [[token["word"] for token in sent["tokens"]] - for sent in ann_result["sentences"]] - else: - annotation = [token["word"] for token in ann_result["tokens"]] - return annotation - - def pos_tag(self, data): - """ - part-of-speech tagging - """ - annotators = "tokenize,ssplit,pos" - ann_result = self._request_corenlp(data, annotators) - annotation = [[token["pos"] for token in sent["tokens"]] - for sent in ann_result["sentences"]] - return annotation - - def ner(self, data): - """ - name entity recognition - """ - annotators = "tokenize,ssplit,pos,ner" - ann_result = self._request_corenlp(data, annotators) - annotation = [] - for sent in ann_result["sentences"]: - sent_ner = [] - if "entitymentions" in sent: - for entity in sent["entitymentions"]: - span = (entity["characterOffsetBegin"], - entity["characterOffsetEnd"]) - ner = entity["ner"] - ner_entity = entity["text"] - sent_ner.append({(ner_entity, span): ner}) - annotation.append(sent_ner) - return annotation - - @staticmethod - def pretty_print_tree(tree): - """ - re-format parse tree - """ - Tree.fromstring(tree).pretty_print() - - def close(self): - if self.corenlp_subprocess: - self.corenlp_subprocess.kill() - self.corenlp_subprocess.wait() diff --git a/flagdata/analysis/text_length_analysis.py b/flagdata/analysis/text_length_analysis.py new file mode 100644 index 0000000..10eeda5 --- /dev/null +++ b/flagdata/analysis/text_length_analysis.py @@ -0,0 +1,23 @@ +import jsonlines +import statistics + +file_path = './data/language_distribution_data.jsonl' # JSONL文件路径 + +total_texts = [] # 存储所有文本 + +with jsonlines.open(file_path) as reader: + for line in reader: + text = line['text'] # 假设JSONL中的文本字段名为'text' + total_texts.append(text) + +# 计算所有文本的长度统计信息 +total_lengths = [len(text) for text in total_texts] +average_length = sum(total_lengths) / len(total_lengths) +max_length = max(total_lengths) +min_length = min(total_lengths) +median_length = statistics.median(total_lengths) + +print(f"Average Length of all texts: {average_length}") +print(f"Maximum Length of all texts: {max_length}") +print(f"Minimum Length of all texts: {min_length}") +print(f"Median Length of all texts: {median_length}") diff --git a/flagdata/analysis/utils/ana_result.py b/flagdata/analysis/utils/ana_result.py deleted file mode 100644 index 0895d35..0000000 --- a/flagdata/analysis/utils/ana_result.py +++ /dev/null @@ -1,44 +0,0 @@ -# Copyright © 2023 BAAI. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License") -import re - - -class CoreNLPAnaResult(): - """ - convert response to readable result - """ - def __init__(self, ann_result): - self.ann_result = ann_result - self.tokens = [] - self.parse_tree = [] - self.bi_parse_tree = [] - self.basic_dep = [] - self.enhanced_dep = [] - self.enhanced_pp_dep = [] - self.entities = [] - self.openie = [] - self._extract_ann() - - def _extract_ann(self): - """ - parse result - """ - ann_dict = dict() - if "sentences" in self.ann_result: - for ann_sent in self.ann_result["sentences"]: - self.tokens.append(ann_sent["tokens"]) - self.parse_tree.append( - re.sub(r"\s+", " ", ann_sent.get("parse", ""))) - self.bi_parse_tree.append( - re.sub(r"\s+", " ", ann_sent.get("binaryParse", ""))) - self.basic_dep.append(ann_sent.get("basicDependencies", "")) - self.enhanced_dep.append( - ann_sent.get("enhancedDependencies", "")) - self.enhanced_pp_dep.append(ann_sent.get( - 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g=null;o&&(g=e(_e,pe()([{key:"timePickerButton"},p])));var y=void 0;(m||g||v||f)&&(y=e("span",{class:r+"-footer-btn"},[f,m,g,v]));var _=(d={},b()(d,r+"-footer",!0),b()(d,r+"-footer-show-ok",!!v),d);h=e("div",{class:_},[y])}return h}},Me=we;function xe(){}function Se(e){var t=void 0;return t=e?M(e):p()(),t}function ke(e){return Array.isArray(e)?0===e.length||-1!==e.findIndex((function(e){return void 0===e||p.a.isMoment(e)})):void 0===e||p.a.isMoment(e)}var Ce=c["a"].custom(ke),Le={mixins:[l["a"]],name:"CalendarMixinWrapper",props:{value:Ce,defaultValue:Ce},data:function(){var e=this.$props,t=e.value||e.defaultValue||Se();return{sValue:t,sSelectedValue:e.selectedValue||e.defaultSelectedValue}},watch:{value:function(e){var t=e||this.defaultValue||Se(this.sValue);this.setState({sValue:t})},selectedValue:function(e){this.setState({sSelectedValue:e})}},methods:{onSelect:function(e,t){e&&this.setValue(e),this.setSelectedValue(e,t)},renderRoot:function(e){var t,n=this.$createElement,r=this.$props,i=r.prefixCls,a=(t={},b()(t,i,1),b()(t,i+"-hidden",!r.visible),b()(t,e["class"],!!e["class"]),t);return n("div",{ref:"rootInstance",class:a,attrs:{tabIndex:"0"},on:{keydown:this.onKeyDown||xe,blur:this.onBlur||xe}},[e.children])},setSelectedValue:function(e,t){Object(u["r"])(this,"selectedValue")||this.setState({sSelectedValue:e}),this.__emit("select",e,t)},setValue:function(e){var t=this.sValue;Object(u["r"])(this,"value")||this.setState({sValue:e}),(t&&e&&!t.isSame(e)||!t&&e||t&&!e)&&this.__emit("change",e)},isAllowedDate:function(e){var t=this.disabledDate,n=this.disabledTime;return z(e,t,n)}}},Te=Le,Oe={methods:{getFormat:function(){var e=this.format,t=this.locale,n=this.timePicker;return e||(e=n?t.dateTimeFormat:t.dateFormat),e},focus:function(){this.focusElement?this.focusElement.focus():this.$refs.rootInstance&&this.$refs.rootInstance.focus()},saveFocusElement:function(e){this.focusElement=e}}},ze=void 0,De=void 0,je=void 0,He={mixins:[l["a"]],props:{prefixCls:c["a"].string,timePicker:c["a"].object,value:c["a"].object,disabledTime:c["a"].any,format:c["a"].oneOfType([c["a"].string,c["a"].arrayOf(c["a"].string),c["a"].func]),locale:c["a"].object,disabledDate:c["a"].func,placeholder:c["a"].string,selectedValue:c["a"].object,clearIcon:c["a"].any,inputMode:c["a"].string,inputReadOnly:c["a"].bool},data:function(){var e=this.selectedValue;return{str:D(e,this.format),invalid:!1,hasFocus:!1}},watch:{selectedValue:function(){this.setState()},format:function(){this.setState()}},updated:function(){var e=this;this.$nextTick((function(){!je||!e.$data.hasFocus||e.invalid||0===ze&&0===De||je.setSelectionRange(ze,De)}))},getInstance:function(){return je},methods:{getDerivedStateFromProps:function(e,t){var n={};je&&(ze=je.selectionStart,De=je.selectionEnd);var r=e.selectedValue;return t.hasFocus||(n={str:D(r,this.format),invalid:!1}),n},onClear:function(){this.setState({str:""}),this.__emit("clear",null)},onInputChange:function(e){var t=e.target,n=t.value,r=t.composing,i=this.str,a=void 0===i?"":i;if(!e.isComposing&&!r&&a!==n){var o=this.$props,s=o.disabledDate,c=o.format,l=o.selectedValue;if(!n)return this.__emit("change",null),void this.setState({invalid:!1,str:n});var u=p()(n,c,!0);if(u.isValid()){var h=this.value.clone();h.year(u.year()).month(u.month()).date(u.date()).hour(u.hour()).minute(u.minute()).second(u.second()),!h||s&&s(h)?this.setState({invalid:!0,str:n}):(l!==h||l&&h&&!l.isSame(h))&&(this.setState({invalid:!1,str:n}),this.__emit("change",h))}else this.setState({invalid:!0,str:n})}},onFocus:function(){this.setState({hasFocus:!0})},onBlur:function(){this.setState((function(e,t){return{hasFocus:!1,str:D(t.value,t.format)}}))},onKeyDown:function(e){var t=e.keyCode,n=this.$props,r=n.value,i=n.disabledDate;if(t===f["a"].ENTER){var a=!i||!i(r);a&&this.__emit("select",r.clone()),e.preventDefault()}},getRootDOMNode:function(){return this.$el},focus:function(){je&&je.focus()},saveDateInput:function(e){je=e}},render:function(){var e=arguments[0],t=this.invalid,n=this.str,r=this.locale,i=this.prefixCls,a=this.placeholder,o=this.disabled,s=this.showClear,c=this.inputMode,l=this.inputReadOnly,h=Object(u["f"])(this,"clearIcon"),f=t?i+"-input-invalid":"";return e("div",{class:i+"-input-wrap"},[e("div",{class:i+"-date-input-wrap"},[e("input",pe()([{directives:[{name:"ant-ref",value:this.saveDateInput},{name:"ant-input"}]},{class:i+"-input "+f,domProps:{value:n},attrs:{disabled:o,placeholder:a,inputMode:c,readOnly:l},on:{input:this.onInputChange,keydown:this.onKeyDown,focus:this.onFocus,blur:this.onBlur}}]))]),s?e("a",{attrs:{role:"button",title:r.clear},on:{click:this.onClear}},[h||e("span",{class:i+"-clear-btn"})]):null])}},Ee=He,Ae=n("f8d5");function Pe(e){return e.clone().startOf("month")}function Ye(e){return e.clone().endOf("month")}function Ve(e,t,n){return e.clone().add(t,n)}function Ie(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments[1],n=arguments[2];return e.some((function(e){return e.isSame(t,n)}))}var Fe=function(e){return!(!p.a.isMoment(e)||!e.isValid())&&e},Re={name:"Calendar",props:{locale:c["a"].object.def(Ae["a"]),format:c["a"].oneOfType([c["a"].string,c["a"].arrayOf(c["a"].string),c["a"].func]),visible:c["a"].bool.def(!0),prefixCls:c["a"].string.def("rc-calendar"),defaultValue:c["a"].object,value:c["a"].object,selectedValue:c["a"].object,defaultSelectedValue:c["a"].object,mode:c["a"].oneOf(["time","date","month","year","decade"]),showDateInput:c["a"].bool.def(!0),showWeekNumber:c["a"].bool,showToday:c["a"].bool.def(!0),showOk:c["a"].bool,timePicker:c["a"].any,dateInputPlaceholder:c["a"].any,disabledDate:c["a"].func,disabledTime:c["a"].any,dateRender:c["a"].func,renderFooter:c["a"].func.def((function(){return 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this.goTime(1,"weeks"),e.preventDefault(),1;case f["a"].UP:return this.goTime(-1,"weeks"),e.preventDefault(),1;case f["a"].LEFT:return n?this.goTime(-1,"years"):this.goTime(-1,"days"),e.preventDefault(),1;case f["a"].RIGHT:return n?this.goTime(1,"years"):this.goTime(1,"days"),e.preventDefault(),1;case f["a"].HOME:return this.setValue(Pe(i)),e.preventDefault(),1;case f["a"].END:return this.setValue(Ye(i)),e.preventDefault(),1;case f["a"].PAGE_DOWN:return this.goTime(1,"month"),e.preventDefault(),1;case f["a"].PAGE_UP:return this.goTime(-1,"month"),e.preventDefault(),1;case f["a"].ENTER:return r&&r(i)||this.onSelect(i,{source:"keyboard"}),e.preventDefault(),1;default:return this.__emit("keydown",e),1}}},onClear:function(){this.onSelect(null),this.__emit("clear")},onOk:function(){var e=this.sSelectedValue;this.isAllowedDate(e)&&this.__emit("ok",e)},onDateInputChange:function(e){this.onSelect(e,{source:"dateInput"})},onDateInputSelect:function(e){this.onSelect(e,{source:"dateInputSelect"})},onDateTableSelect:function(e){var t=this.timePicker,n=this.sSelectedValue;if(!n&&t){var r=Object(u["k"])(t),i=r.defaultValue;i&&C(i,e)}this.onSelect(e)},onToday:function(){var e=this.sValue,t=M(e);this.onSelect(t,{source:"todayButton"})},onBlur:function(e){var t=this;setTimeout((function(){var n=Ee.getInstance(),r=t.rootInstance;!r||r.contains(document.activeElement)||n&&n.contains(document.activeElement)||t.$emit("blur",e)}),0)},getRootDOMNode:function(){return this.$el},openTimePicker:function(){this.onPanelChange(null,"time")},closeTimePicker:function(){this.onPanelChange(null,"date")},goTime:function(e,t){this.setValue(Ve(this.sValue,e,t))}},render:function(){var e=arguments[0],t=this.locale,n=this.prefixCls,r=this.disabledDate,a=this.dateInputPlaceholder,o=this.timePicker,s=this.disabledTime,c=this.showDateInput,l=this.sValue,f=this.sSelectedValue,d=this.sMode,p=this.renderFooter,m=this.inputMode,v=this.inputReadOnly,g=this.monthCellRender,b=this.monthCellContentRender,y=this.$props,_=Object(u["f"])(this,"clearIcon"),w="time"===d,M=w&&s&&o?L(f,s):null,x=null;if(o&&w){var S=Object(u["k"])(o),k={props:i()({showHour:!0,showSecond:!0,showMinute:!0},S,M,{value:f,disabledTime:s}),on:{change:this.onDateInputChange}};void 0!==S.defaultValue&&(k.props.defaultOpenValue=S.defaultValue),x=Object(h["a"])(o,k)}var C=c?e(Ee,{attrs:{format:this.getFormat(),value:l,locale:t,placeholder:a,showClear:!0,disabledTime:s,disabledDate:r,prefixCls:n,selectedValue:f,clearIcon:_,inputMode:m,inputReadOnly:v},key:"date-input",on:{clear:this.onClear,change:this.onDateInputChange,select:this.onDateInputSelect}}):null,T=[];return y.renderSidebar&&T.push(y.renderSidebar()),T.push(e("div",{class:n+"-panel",key:"panel"},[C,e("div",{attrs:{tabIndex:y.focusablePanel?0:void 0},class:n+"-date-panel"},[e(fe,{attrs:{locale:t,mode:d,value:l,renderFooter:p,showTimePicker:w,prefixCls:n,monthCellRender:g,monthCellContentRender:b},on:{valueChange:this.setValue,panelChange:this.onPanelChange}}),o&&w?e("div",{class:n+"-time-picker"},[e("div",{class:n+"-time-picker-panel"},[x])]):null,e("div",{class:n+"-body"},[e(I,{attrs:{locale:t,value:l,selectedValue:f,prefixCls:n,dateRender:y.dateRender,disabledDate:r,showWeekNumber:y.showWeekNumber},on:{select:this.onDateTableSelect}})]),e(Me,{attrs:{showOk:y.showOk,mode:d,renderFooter:y.renderFooter,locale:t,prefixCls:n,showToday:y.showToday,disabledTime:s,showTimePicker:w,showDateInput:y.showDateInput,timePicker:o,selectedValue:f,timePickerDisabled:!f,value:l,disabledDate:r,okDisabled:!1!==y.showOk&&(!f||!this.isAllowedDate(f))},on:{ok:this.onOk,select:this.onSelect,today:this.onToday,openTimePicker:this.openTimePicker,closeTimePicker:this.closeTimePicker}})])])),this.renderRoot({children:T,class:y.showWeekNumber?n+"-week-number":""})}},Ne=Re,Be=Ne;a["a"].use(s.a,{name:"ant-ref"});var $e=Be,We={name:"MonthCalendar",props:{locale:c["a"].object.def(Ae["a"]),format:c["a"].string,visible:c["a"].bool.def(!0),prefixCls:c["a"].string.def("rc-calendar"),monthCellRender:c["a"].func,value:c["a"].object,defaultValue:c["a"].object,selectedValue:c["a"].object,defaultSelectedValue:c["a"].object,disabledDate:c["a"].func,monthCellContentRender:c["a"].func,renderFooter:c["a"].func.def((function(){return null})),renderSidebar:c["a"].func.def((function(){return null}))},mixins:[l["a"],Oe,Te],data:function(){var e=this.$props;return{mode:"month",sValue:e.value||e.defaultValue||p()(),sSelectedValue:e.selectedValue||e.defaultSelectedValue}},methods:{onKeyDown:function(e){var t=e.keyCode,n=e.ctrlKey||e.metaKey,r=this.sValue,i=this.disabledDate,a=r;switch(t){case f["a"].DOWN:a=r.clone(),a.add(3,"months");break;case f["a"].UP:a=r.clone(),a.add(-3,"months");break;case f["a"].LEFT:a=r.clone(),n?a.add(-1,"years"):a.add(-1,"months");break;case f["a"].RIGHT:a=r.clone(),n?a.add(1,"years"):a.add(1,"months");break;case f["a"].ENTER:return i&&i(r)||this.onSelect(r),e.preventDefault(),1;default:return}if(a!==r)return this.setValue(a),e.preventDefault(),1},handlePanelChange:function(e,t){"date"!==t&&this.setState({mode:t})}},render:function(){var e=arguments[0],t=this.mode,n=this.sValue,r=this.$props,i=this.$scopedSlots,a=r.prefixCls,o=r.locale,s=r.disabledDate,c=this.monthCellRender||i.monthCellRender,l=this.monthCellContentRender||i.monthCellContentRender,u=this.renderFooter||i.renderFooter,h=e("div",{class:a+"-month-calendar-content"},[e("div",{class:a+"-month-header-wrap"},[e(fe,{attrs:{prefixCls:a,mode:t,value:n,locale:o,disabledMonth:s,monthCellRender:c,monthCellContentRender:l},on:{monthSelect:this.onSelect,valueChange:this.setValue,panelChange:this.handlePanelChange}})]),e(Me,{attrs:{prefixCls:a,renderFooter:u}})]);return this.renderRoot({class:r.prefixCls+"-month-calendar",children:h})}},Ue=We,qe=n("3eea"),Ke=n.n(qe),Ge=n("3f50"),Xe={adjustX:1,adjustY:1},Je=[0,0],Ze={bottomLeft:{points:["tl","tl"],overflow:Xe,offset:[0,-3],targetOffset:Je},bottomRight:{points:["tr","tr"],overflow:Xe,offset:[0,-3],targetOffset:Je},topRight:{points:["br","br"],overflow:Xe,offset:[0,3],targetOffset:Je},topLeft:{points:["bl","bl"],overflow:Xe,offset:[0,3],targetOffset:Je}},Qe=Ze,et=n("8496"),tt=n("2768"),nt=n.n(tt),rt={validator:function(e){return Array.isArray(e)?0===e.length||-1===e.findIndex((function(e){return!nt()(e)&&!p.a.isMoment(e)})):nt()(e)||p.a.isMoment(e)}},it={name:"Picker",props:{animation:c["a"].oneOfType([c["a"].func,c["a"].string]),disabled:c["a"].bool,transitionName:c["a"].string,format:c["a"].oneOfType([c["a"].string,c["a"].array,c["a"].func]),children:c["a"].func,getCalendarContainer:c["a"].func,calendar:c["a"].any,open:c["a"].bool,defaultOpen:c["a"].bool.def(!1),prefixCls:c["a"].string.def("rc-calendar-picker"),placement:c["a"].any.def("bottomLeft"),value:rt,defaultValue:rt,align:c["a"].object.def((function(){return{}})),dropdownClassName:c["a"].string,dateRender:c["a"].func},mixins:[l["a"]],data:function(){var e=this.$props,t=void 0;t=Object(u["r"])(this,"open")?e.open:e.defaultOpen;var n=e.value||e.defaultValue;return{sOpen:t,sValue:n}},watch:{value:function(e){this.setState({sValue:e})},open:function(e){this.setState({sOpen:e})}},mounted:function(){this.preSOpen=this.sOpen},updated:function(){!this.preSOpen&&this.sOpen&&(this.focusTimeout=setTimeout(this.focusCalendar,0)),this.preSOpen=this.sOpen},beforeDestroy:function(){clearTimeout(this.focusTimeout)},methods:{onCalendarKeyDown:function(e){e.keyCode===f["a"].ESC&&(e.stopPropagation(),this.closeCalendar(this.focus))},onCalendarSelect:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},n=this.$props;Object(u["r"])(this,"value")||this.setState({sValue:e});var r=Object(u["k"])(n.calendar);("keyboard"===t.source||"dateInputSelect"===t.source||!r.timePicker&&"dateInput"!==t.source||"todayButton"===t.source)&&this.closeCalendar(this.focus),this.__emit("change",e)},onKeyDown:function(e){this.sOpen||e.keyCode!==f["a"].DOWN&&e.keyCode!==f["a"].ENTER||(this.openCalendar(),e.preventDefault())},onCalendarOk:function(){this.closeCalendar(this.focus)},onCalendarClear:function(){this.closeCalendar(this.focus)},onCalendarBlur:function(){this.setOpen(!1)},onVisibleChange:function(e){this.setOpen(e)},getCalendarElement:function(){var e=this.$props,t=Object(u["k"])(e.calendar),n=Object(u["h"])(e.calendar),r=this.sValue,i=r,a={ref:"calendarInstance",props:{defaultValue:i||t.defaultValue,selectedValue:r},on:{keydown:this.onCalendarKeyDown,ok:Object(Ge["a"])(n.ok,this.onCalendarOk),select:Object(Ge["a"])(n.select,this.onCalendarSelect),clear:Object(Ge["a"])(n.clear,this.onCalendarClear),blur:Object(Ge["a"])(n.blur,this.onCalendarBlur)}};return Object(h["a"])(e.calendar,a)},setOpen:function(e,t){this.sOpen!==e&&(Object(u["r"])(this,"open")||this.setState({sOpen:e},t),this.__emit("openChange",e))},openCalendar:function(e){this.setOpen(!0,e)},closeCalendar:function(e){this.setOpen(!1,e)},focus:function(){this.sOpen||this.$el.focus()},focusCalendar:function(){this.sOpen&&this.calendarInstance&&this.calendarInstance.componentInstance&&this.calendarInstance.componentInstance.focus()}},render:function(){var e=arguments[0],t=Object(u["k"])(this),n=Object(u["p"])(this),r=t.prefixCls,i=t.placement,a=t.getCalendarContainer,o=t.align,s=t.animation,c=t.disabled,l=t.dropdownClassName,f=t.transitionName,d=this.sValue,p=this.sOpen,m=this.$scopedSlots["default"],v={value:d,open:p};return!this.sOpen&&this.calendarInstance||(this.calendarInstance=this.getCalendarElement()),e(et["a"],{attrs:{popupAlign:o,builtinPlacements:Qe,popupPlacement:i,action:c&&!p?[]:["click"],destroyPopupOnHide:!0,getPopupContainer:a,popupStyle:n,popupAnimation:s,popupTransitionName:f,popupVisible:p,prefixCls:r,popupClassName:l},on:{popupVisibleChange:this.onVisibleChange}},[e("template",{slot:"popup"},[this.calendarInstance]),Object(h["a"])(m(v,t),{on:{keydown:this.onKeyDown}})])}},at=it,ot=n("0c63"),st=n("4df5"),ct=n("2cf8");function lt(e,t){if(!e)return"";if(Array.isArray(t)&&(t=t[0]),"function"===typeof t){var n=t(e);if("string"===typeof n)return n;throw new Error("The function of format does not return a string")}return e.format(t)}function ut(){}function ht(e,t){return{props:Object(u["s"])(t,{allowClear:!0,showToday:!0}),mixins:[l["a"]],model:{prop:"value",event:"change"},inject:{configProvider:{default:function(){return st["a"]}}},data:function(){var e=this.value||this.defaultValue;if(e&&!Object(ct["a"])(d).isMoment(e))throw new Error("The value/defaultValue of DatePicker or MonthPicker must be a moment object");return{sValue:e,showDate:e,_open:!!this.open}},watch:{open:function(e){var t=Object(u["k"])(this),n={};n._open=e,"value"in t&&!e&&t.value!==this.showDate&&(n.showDate=t.value),this.setState(n)},value:function(e){var t={};t.sValue=e,e!==this.sValue&&(t.showDate=e),this.setState(t)},_open:function(e,t){var n=this;this.$nextTick((function(){Object(u["r"])(n,"open")||!t||e||n.focus()}))}},methods:{clearSelection:function(e){e.preventDefault(),e.stopPropagation(),this.handleChange(null)},handleChange:function(e){Object(u["r"])(this,"value")||this.setState({sValue:e,showDate:e}),this.$emit("change",e,lt(e,this.format))},handleCalendarChange:function(e){this.setState({showDate:e})},handleOpenChange:function(e){var t=Object(u["k"])(this);"open"in t||this.setState({_open:e}),this.$emit("openChange",e)},focus:function(){this.$refs.input.focus()},blur:function(){this.$refs.input.blur()},renderFooter:function(){var e=this.$createElement,t=this.$scopedSlots,n=this.$slots,r=this._prefixCls,i=this.renderExtraFooter||t.renderExtraFooter||n.renderExtraFooter;return i?e("div",{class:r+"-footer-extra"},["function"===typeof i?i.apply(void 0,arguments):i]):null},onMouseEnter:function(e){this.$emit("mouseenter",e)},onMouseLeave:function(e){this.$emit("mouseleave",e)}},render:function(){var t,n=this,r=arguments[0],a=this.$scopedSlots,o=this.$data,s=o.sValue,c=o.showDate,l=o._open,f=Object(u["f"])(this,"suffixIcon");f=Array.isArray(f)?f[0]:f;var p=Object(u["j"])(this),m=p.panelChange,v=void 0===m?ut:m,g=p.focus,y=void 0===g?ut:g,w=p.blur,M=void 0===w?ut:w,x=p.ok,S=void 0===x?ut:x,k=Object(u["k"])(this),C=k.prefixCls,L=k.locale,T=k.localeCode,O=k.inputReadOnly,z=this.configProvider.getPrefixCls,D=z("calendar",C);this._prefixCls=D;var j=k.dateRender||a.dateRender,H=k.monthCellContentRender||a.monthCellContentRender,E="placeholder"in k?k.placeholder:L.lang.placeholder,A=k.showTime?k.disabledTime:null,P=_()((t={},b()(t,D+"-time",k.showTime),b()(t,D+"-month",Ue===e),t));s&&T&&s.locale(T);var Y={props:{},on:{}},V={props:{},on:{}},I={};k.showTime?(V.on.select=this.handleChange,I.minWidth="195px"):Y.on.change=this.handleChange,"mode"in k&&(V.props.mode=k.mode);var F=Object(u["w"])(V,{props:{disabledDate:k.disabledDate,disabledTime:A,locale:L.lang,timePicker:k.timePicker,defaultValue:k.defaultPickerValue||Object(ct["a"])(d)(),dateInputPlaceholder:E,prefixCls:D,dateRender:j,format:k.format,showToday:k.showToday,monthCellContentRender:H,renderFooter:this.renderFooter,value:c,inputReadOnly:O},on:{ok:S,panelChange:v,change:this.handleCalendarChange},class:P,scopedSlots:a}),R=r(e,F),N=!k.disabled&&k.allowClear&&s?r(ot["a"],{attrs:{type:"close-circle",theme:"filled"},class:D+"-picker-clear",on:{click:this.clearSelection}}):null,B=f&&(Object(u["v"])(f)?Object(h["a"])(f,{class:D+"-picker-icon"}):r("span",{class:D+"-picker-icon"},[f]))||r(ot["a"],{attrs:{type:"calendar"},class:D+"-picker-icon"}),$=function(e){var t=e.value;return r("div",[r("input",{ref:"input",attrs:{disabled:k.disabled,readOnly:!0,placeholder:E,tabIndex:k.tabIndex,name:n.name},on:{focus:y,blur:M},domProps:{value:lt(t,n.format)},class:k.pickerInputClass}),N,B])},W={props:i()({},k,Y.props,{calendar:R,value:s,prefixCls:D+"-picker-container"}),on:i()({},Ke()(p,"change"),Y.on,{open:l,onOpenChange:this.handleOpenChange}),style:k.popupStyle,scopedSlots:i()({default:$},a)};return r("span",{class:k.pickerClass,style:I,on:{mouseenter:this.onMouseEnter,mouseleave:this.onMouseLeave}},[r(at,W)])}}}var ft={mixins:[l["a"]],props:{format:c["a"].string,prefixCls:c["a"].string,disabledDate:c["a"].func,placeholder:c["a"].string,clearText:c["a"].string,value:c["a"].object,inputReadOnly:c["a"].bool.def(!1),hourOptions:c["a"].array,minuteOptions:c["a"].array,secondOptions:c["a"].array,disabledHours:c["a"].func,disabledMinutes:c["a"].func,disabledSeconds:c["a"].func,allowEmpty:c["a"].bool,defaultOpenValue:c["a"].object,currentSelectPanel:c["a"].string,focusOnOpen:c["a"].bool,clearIcon:c["a"].any},data:function(){var e=this.value,t=this.format;return{str:e&&e.format(t)||"",invalid:!1}},mounted:function(){var e=this;if(this.focusOnOpen){var t=window.requestAnimationFrame||window.setTimeout;t((function(){e.$refs.input.focus(),e.$refs.input.select()}))}},watch:{value:function(e){var t=this;this.$nextTick((function(){t.setState({str:e&&e.format(t.format)||"",invalid:!1})}))}},methods:{onInputChange:function(e){var t=e.target,n=t.value,r=t.composing,i=this.str,a=void 0===i?"":i;if(!e.isComposing&&!r&&a!==n){this.setState({str:n});var o=this.format,s=this.hourOptions,c=this.minuteOptions,l=this.secondOptions,u=this.disabledHours,h=this.disabledMinutes,f=this.disabledSeconds,d=this.value;if(n){var m=this.getProtoValue().clone(),v=p()(n,o,!0);if(!v.isValid())return void this.setState({invalid:!0});if(m.hour(v.hour()).minute(v.minute()).second(v.second()),s.indexOf(m.hour())<0||c.indexOf(m.minute())<0||l.indexOf(m.second())<0)return void this.setState({invalid:!0});var g=u(),b=h(m.hour()),y=f(m.hour(),m.minute());if(g&&g.indexOf(m.hour())>=0||b&&b.indexOf(m.minute())>=0||y&&y.indexOf(m.second())>=0)return void this.setState({invalid:!0});if(d){if(d.hour()!==m.hour()||d.minute()!==m.minute()||d.second()!==m.second()){var _=d.clone();_.hour(m.hour()),_.minute(m.minute()),_.second(m.second()),this.__emit("change",_)}}else d!==m&&this.__emit("change",m)}else this.__emit("change",null);this.setState({invalid:!1})}},onKeyDown:function(e){27===e.keyCode&&this.__emit("esc"),this.__emit("keydown",e)},getProtoValue:function(){return this.value||this.defaultOpenValue},getInput:function(){var e=this.$createElement,t=this.prefixCls,n=this.placeholder,r=this.inputReadOnly,i=this.invalid,a=this.str,o=i?t+"-input-invalid":"";return e("input",pe()([{class:t+"-input "+o,ref:"input",on:{keydown:this.onKeyDown,input:this.onInputChange},domProps:{value:a},attrs:{placeholder:n,readOnly:!!r}},{directives:[{name:"ant-input"}]}]))}},render:function(){var e=arguments[0],t=this.prefixCls;return e("div",{class:t+"-input-wrap"},[this.getInput()])}},dt=ft,pt=n("c449"),mt=n.n(pt);function vt(){}var gt=function e(t,n,r){if(r<=0)mt()((function(){t.scrollTop=n}));else{var i=n-t.scrollTop,a=i/r*10;mt()((function(){t.scrollTop+=a,t.scrollTop!==n&&e(t,n,r-10)}))}},bt={mixins:[l["a"]],props:{prefixCls:c["a"].string,options:c["a"].array,selectedIndex:c["a"].number,type:c["a"].string},data:function(){return{active:!1}},mounted:function(){var e=this;this.$nextTick((function(){e.scrollToSelected(0)}))},watch:{selectedIndex:function(){var e=this;this.$nextTick((function(){e.scrollToSelected(120)}))}},methods:{onSelect:function(e){var t=this.type;this.__emit("select",t,e)},onEsc:function(e){this.__emit("esc",e)},getOptions:function(){var e=this,t=this.$createElement,n=this.options,r=this.selectedIndex,i=this.prefixCls;return n.map((function(n,a){var o,s=_()((o={},b()(o,i+"-select-option-selected",r===a),b()(o,i+"-select-option-disabled",n.disabled),o)),c=n.disabled?vt:function(){e.onSelect(n.value)},l=function(t){13===t.keyCode?c():27===t.keyCode&&e.onEsc()};return t("li",{attrs:{role:"button",disabled:n.disabled,tabIndex:"0"},on:{click:c,keydown:l},class:s,key:a},[n.value])}))},handleMouseEnter:function(e){this.setState({active:!0}),this.__emit("mouseenter",e)},handleMouseLeave:function(){this.setState({active:!1})},scrollToSelected:function(e){var t=this.$el,n=this.$refs.list;if(n){var r=this.selectedIndex;r<0&&(r=0);var i=n.children[r],a=i.offsetTop;gt(t,a,e)}}},render:function(){var e,t=arguments[0],n=this.prefixCls,r=this.options,i=this.active;if(0===r.length)return null;var a=(e={},b()(e,n+"-select",1),b()(e,n+"-select-active",i),e);return t("div",{class:a,on:{mouseenter:this.handleMouseEnter,mouseleave:this.handleMouseLeave}},[t("ul",{ref:"list"},[this.getOptions()])])}},yt=bt,_t=function(e,t){var n=""+e;e<10&&(n="0"+e);var r=!1;return t&&t.indexOf(e)>=0&&(r=!0),{value:n,disabled:r}},wt={mixins:[l["a"]],name:"Combobox",props:{format:c["a"].string,defaultOpenValue:c["a"].object,prefixCls:c["a"].string,value:c["a"].object,showHour:c["a"].bool,showMinute:c["a"].bool,showSecond:c["a"].bool,hourOptions:c["a"].array,minuteOptions:c["a"].array,secondOptions:c["a"].array,disabledHours:c["a"].func,disabledMinutes:c["a"].func,disabledSeconds:c["a"].func,use12Hours:c["a"].bool,isAM:c["a"].bool},methods:{onItemChange:function(e,t){var n=this.defaultOpenValue,r=this.use12Hours,i=this.value,a=this.isAM,o=(i||n).clone();if("hour"===e)r?a?o.hour(+t%12):o.hour(+t%12+12):o.hour(+t);else if("minute"===e)o.minute(+t);else if("ampm"===e){var s=t.toUpperCase();r&&("PM"===s&&o.hour()<12&&o.hour(o.hour()%12+12),"AM"===s&&o.hour()>=12&&o.hour(o.hour()-12)),this.__emit("amPmChange",s)}else o.second(+t);this.__emit("change",o)},onEnterSelectPanel:function(e){this.__emit("currentSelectPanelChange",e)},onEsc:function(e){this.__emit("esc",e)},getHourSelect:function(e){var t=this,n=this.$createElement,r=this.prefixCls,i=this.hourOptions,a=this.disabledHours,o=this.showHour,s=this.use12Hours;if(!o)return null;var c=a(),l=void 0,u=void 0;return s?(l=[12].concat(i.filter((function(e){return e<12&&e>0}))),u=e%12||12):(l=i,u=e),n(yt,{attrs:{prefixCls:r,options:l.map((function(e){return _t(e,c)})),selectedIndex:l.indexOf(u),type:"hour"},on:{select:this.onItemChange,mouseenter:function(){return t.onEnterSelectPanel("hour")},esc:this.onEsc}})},getMinuteSelect:function(e){var t=this,n=this.$createElement,r=this.prefixCls,i=this.minuteOptions,a=this.disabledMinutes,o=this.defaultOpenValue,s=this.showMinute,c=this.value;if(!s)return null;var l=c||o,u=a(l.hour());return n(yt,{attrs:{prefixCls:r,options:i.map((function(e){return _t(e,u)})),selectedIndex:i.indexOf(e),type:"minute"},on:{select:this.onItemChange,mouseenter:function(){return t.onEnterSelectPanel("minute")},esc:this.onEsc}})},getSecondSelect:function(e){var t=this,n=this.$createElement,r=this.prefixCls,i=this.secondOptions,a=this.disabledSeconds,o=this.showSecond,s=this.defaultOpenValue,c=this.value;if(!o)return null;var l=c||s,u=a(l.hour(),l.minute());return n(yt,{attrs:{prefixCls:r,options:i.map((function(e){return _t(e,u)})),selectedIndex:i.indexOf(e),type:"second"},on:{select:this.onItemChange,mouseenter:function(){return t.onEnterSelectPanel("second")},esc:this.onEsc}})},getAMPMSelect:function(){var e=this,t=this.$createElement,n=this.prefixCls,r=this.use12Hours,i=this.format,a=this.isAM;if(!r)return null;var o=["am","pm"].map((function(e){return i.match(/\sA/)?e.toUpperCase():e})).map((function(e){return{value:e}})),s=a?0:1;return t(yt,{attrs:{prefixCls:n,options:o,selectedIndex:s,type:"ampm"},on:{select:this.onItemChange,mouseenter:function(){return e.onEnterSelectPanel("ampm")},esc:this.onEsc}})}},render:function(){var e=arguments[0],t=this.prefixCls,n=this.defaultOpenValue,r=this.value,i=r||n;return e("div",{class:t+"-combobox"},[this.getHourSelect(i.hour()),this.getMinuteSelect(i.minute()),this.getSecondSelect(i.second()),this.getAMPMSelect(i.hour())])}},Mt=wt;function xt(){}function St(e,t,n){for(var r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:1,i=[],a=0;a=0&&e.hour()<12}},render:function(){var e=arguments[0],t=this.prefixCls,n=this.placeholder,r=this.disabledMinutes,i=this.addon,a=this.disabledSeconds,o=this.hideDisabledOptions,s=this.showHour,c=this.showMinute,l=this.showSecond,h=this.format,f=this.defaultOpenValue,d=this.clearText,p=this.use12Hours,m=this.focusOnOpen,v=this.hourStep,g=this.minuteStep,b=this.secondStep,y=this.inputReadOnly,_=this.sValue,w=this.currentSelectPanel,M=Object(u["f"])(this,"clearIcon"),x=Object(u["j"])(this),S=x.esc,k=void 0===S?xt:S,C=x.keydown,L=void 0===C?xt:C,T=this.disabledHours2(),O=r(_?_.hour():null),z=a(_?_.hour():null,_?_.minute():null),D=St(24,T,o,v),j=St(60,O,o,g),H=St(60,z,o,b),E=kt(f,D,j,H);return e("div",{class:t+"-inner"},[e(dt,{attrs:{clearText:d,prefixCls:t,defaultOpenValue:E,value:_,currentSelectPanel:w,format:h,placeholder:n,hourOptions:D,minuteOptions:j,secondOptions:H,disabledHours:this.disabledHours2,disabledMinutes:r,disabledSeconds:a,focusOnOpen:m,inputReadOnly:y,clearIcon:M},on:{esc:k,change:this.onChange,keydown:L}}),e(Mt,{attrs:{prefixCls:t,value:_,defaultOpenValue:E,format:h,showHour:s,showMinute:c,showSecond:l,hourOptions:D,minuteOptions:j,secondOptions:H,disabledHours:this.disabledHours2,disabledMinutes:r,disabledSeconds:a,use12Hours:p,isAM:this.isAM()},on:{change:this.onChange,amPmChange:this.onAmPmChange,currentSelectPanelChange:this.onCurrentSelectPanelChange,esc:this.onEsc}}),i(this)])}},Lt=Ct,Tt=n("e5cd"),Ot=n("0464"),zt={adjustX:1,adjustY:1},Dt=[0,0],jt={bottomLeft:{points:["tl","tl"],overflow:zt,offset:[0,-3],targetOffset:Dt},bottomRight:{points:["tr","tr"],overflow:zt,offset:[0,-3],targetOffset:Dt},topRight:{points:["br","br"],overflow:zt,offset:[0,3],targetOffset:Dt},topLeft:{points:["bl","bl"],overflow:zt,offset:[0,3],targetOffset:Dt}},Ht=jt;function Et(){}var At={name:"VcTimePicker",mixins:[l["a"]],props:Object(u["s"])({prefixCls:c["a"].string,clearText:c["a"].string,value:c["a"].any,defaultOpenValue:{type:Object,default:function(){return p()()}},inputReadOnly:c["a"].bool,disabled:c["a"].bool,allowEmpty:c["a"].bool,defaultValue:c["a"].any,open:c["a"].bool,defaultOpen:c["a"].bool,align:c["a"].object,placement:c["a"].any,transitionName:c["a"].string,getPopupContainer:c["a"].func,placeholder:c["a"].string,format:c["a"].string,showHour:c["a"].bool,showMinute:c["a"].bool,showSecond:c["a"].bool,popupClassName:c["a"].string,popupStyle:c["a"].object,disabledHours:c["a"].func,disabledMinutes:c["a"].func,disabledSeconds:c["a"].func,hideDisabledOptions:c["a"].bool,name:c["a"].string,autoComplete:c["a"].string,use12Hours:c["a"].bool,hourStep:c["a"].number,minuteStep:c["a"].number,secondStep:c["a"].number,focusOnOpen:c["a"].bool,autoFocus:c["a"].bool,id:c["a"].string,inputIcon:c["a"].any,clearIcon:c["a"].any,addon:c["a"].func},{clearText:"clear",prefixCls:"rc-time-picker",defaultOpen:!1,inputReadOnly:!1,popupClassName:"",popupStyle:{},align:{},allowEmpty:!0,showHour:!0,showMinute:!0,showSecond:!0,disabledHours:Et,disabledMinutes:Et,disabledSeconds:Et,hideDisabledOptions:!1,placement:"bottomLeft",use12Hours:!1,focusOnOpen:!1}),data:function(){var e=this.defaultOpen,t=this.defaultValue,n=this.open,r=void 0===n?e:n,i=this.value,a=void 0===i?t:i;return{sOpen:r,sValue:a}},watch:{value:function(e){this.setState({sValue:e})},open:function(e){void 0!==e&&this.setState({sOpen:e})}},mounted:function(){var e=this;this.$nextTick((function(){e.autoFocus&&e.focus()}))},methods:{onPanelChange:function(e){this.setValue(e)},onAmPmChange:function(e){this.__emit("amPmChange",e)},onClear:function(e){e.stopPropagation(),this.setValue(null),this.setOpen(!1)},onVisibleChange:function(e){this.setOpen(e)},onEsc:function(){this.setOpen(!1),this.focus()},onKeyDown:function(e){40===e.keyCode&&this.setOpen(!0)},onKeyDown2:function(e){this.__emit("keydown",e)},setValue:function(e){Object(u["r"])(this,"value")||this.setState({sValue:e}),this.__emit("change",e)},getFormat:function(){var e=this.format,t=this.showHour,n=this.showMinute,r=this.showSecond,i=this.use12Hours;if(e)return e;if(i){var a=[t?"h":"",n?"mm":"",r?"ss":""].filter((function(e){return!!e})).join(":");return a.concat(" a")}return[t?"HH":"",n?"mm":"",r?"ss":""].filter((function(e){return!!e})).join(":")},getPanelElement:function(){var e=this.$createElement,t=this.prefixCls,n=this.placeholder,r=this.disabledHours,i=this.addon,a=this.disabledMinutes,o=this.disabledSeconds,s=this.hideDisabledOptions,c=this.inputReadOnly,l=this.showHour,h=this.showMinute,f=this.showSecond,d=this.defaultOpenValue,p=this.clearText,m=this.use12Hours,v=this.focusOnOpen,g=this.onKeyDown2,b=this.hourStep,y=this.minuteStep,_=this.secondStep,w=this.sValue,M=Object(u["f"])(this,"clearIcon");return e(Lt,{attrs:{clearText:p,prefixCls:t+"-panel",value:w,inputReadOnly:c,defaultOpenValue:d,showHour:l,showMinute:h,showSecond:f,format:this.getFormat(),placeholder:n,disabledHours:r,disabledMinutes:a,disabledSeconds:o,hideDisabledOptions:s,use12Hours:m,hourStep:b,minuteStep:y,secondStep:_,focusOnOpen:v,clearIcon:M,addon:i},ref:"panel",on:{change:this.onPanelChange,amPmChange:this.onAmPmChange,esc:this.onEsc,keydown:g}})},getPopupClassName:function(){var e=this.showHour,t=this.showMinute,n=this.showSecond,r=this.use12Hours,i=this.prefixCls,a=this.popupClassName,o=0;return e&&(o+=1),t&&(o+=1),n&&(o+=1),r&&(o+=1),_()(a,b()({},i+"-panel-narrow",(!e||!t||!n)&&!r),i+"-panel-column-"+o)},setOpen:function(e){this.sOpen!==e&&(Object(u["r"])(this,"open")||this.setState({sOpen:e}),e?this.__emit("open",{open:e}):this.__emit("close",{open:e}))},focus:function(){this.$refs.picker.focus()},blur:function(){this.$refs.picker.blur()},onFocus:function(e){this.__emit("focus",e)},onBlur:function(e){this.__emit("blur",e)},renderClearButton:function(){var e=this,t=this.$createElement,n=this.sValue,r=this.$props,i=r.prefixCls,a=r.allowEmpty,o=r.clearText,s=r.disabled;if(!a||!n||s)return null;var c=Object(u["f"])(this,"clearIcon");if(Object(u["v"])(c)){var l=Object(u["h"])(c)||{},f=l.click;return Object(h["a"])(c,{on:{click:function(){f&&f.apply(void 0,arguments),e.onClear.apply(e,arguments)}}})}return t("a",{attrs:{role:"button",title:o,tabIndex:0},class:i+"-clear",on:{click:this.onClear}},[c||t("i",{class:i+"-clear-icon"})])}},render:function(){var e=arguments[0],t=this.prefixCls,n=this.placeholder,r=this.placement,i=this.align,a=this.id,o=this.disabled,s=this.transitionName,c=this.getPopupContainer,l=this.name,h=this.autoComplete,f=this.autoFocus,d=this.inputReadOnly,p=this.sOpen,m=this.sValue,v=this.onFocus,g=this.onBlur,b=this.popupStyle,y=this.getPopupClassName(),_=Object(u["f"])(this,"inputIcon");return e(et["a"],{attrs:{prefixCls:t+"-panel",popupClassName:y,popupStyle:b,popupAlign:i,builtinPlacements:Ht,popupPlacement:r,action:o?[]:["click"],destroyPopupOnHide:!0,getPopupContainer:c,popupTransitionName:s,popupVisible:p},on:{popupVisibleChange:this.onVisibleChange}},[e("template",{slot:"popup"},[this.getPanelElement()]),e("span",{class:""+t},[e("input",{class:t+"-input",ref:"picker",attrs:{type:"text",placeholder:n,name:l,disabled:o,autoComplete:h,autoFocus:f,readOnly:!!d,id:a},on:{keydown:this.onKeyDown,focus:v,blur:g},domProps:{value:m&&m.format(this.getFormat())||""}}),_||e("span",{class:t+"-icon"}),this.renderClearButton()])])}},Pt=n("6a21"),Yt=n("01c2"),Vt=n("db14"),It={validator:function(e){return"string"===typeof e||nt()(e)||d["isMoment"](e)}},Ft={validator:function(e){return!!Array.isArray(e)&&(0===e.length||-1===e.findIndex((function(e){return"string"!==typeof e}))||-1===e.findIndex((function(e){return!nt()(e)&&!d["isMoment"](e)})))}},Rt={validator:function(e){return Array.isArray(e)?0===e.length||-1===e.findIndex((function(e){return"string"!==typeof e}))||-1===e.findIndex((function(e){return!nt()(e)&&!d["isMoment"](e)})):"string"===typeof e||nt()(e)||d["isMoment"](e)}};function Nt(e,t,n,r){var i=Array.isArray(t)?t:[t];i.forEach((function(t){t&&(r&&Object(Pt["a"])(Object(ct["a"])(d)(t,r).isValid(),e,"When set `valueFormat`, `"+n+"` should provides invalidate string time. 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t=this.$createElement,n=Object(u["f"])(this,"suffixIcon");n=Array.isArray(n)?n[0]:n;var r=n&&Object(u["v"])(n)&&Object(h["a"])(n,{class:e+"-clock-icon"})||t(ot["a"],{attrs:{type:"clock-circle"},class:e+"-clock-icon"});return t("span",{class:e+"-icon"},[r])},renderClearIcon:function(e){var t=this.$createElement,n=Object(u["f"])(this,"clearIcon"),r=e+"-clear";return n&&Object(u["v"])(n)?Object(h["a"])(n,{class:r}):t(ot["a"],{attrs:{type:"close-circle",theme:"filled"},class:r})},renderTimePicker:function(e){var t=this.$createElement,n=Object(u["k"])(this);n=Object(Ot["a"])(n,["defaultValue","suffixIcon","allowEmpty","allowClear"]);var r=n,a=r.prefixCls,o=r.getPopupContainer,s=r.placeholder,c=r.size,l=this.configProvider.getPrefixCls,h=l("time-picker",a),f=this.getDefaultFormat(),d=b()({},h+"-"+c,!!c),p=Object(u["f"])(this,"addon",{},!1),m=function(e){return p?t("div",{class:h+"-panel-addon"},["function"===typeof p?p(e):p]):null},v=this.renderInputIcon(h),g=this.renderClearIcon(h),y=this.configProvider.getPopupContainer,_={props:i()({},Wt(f),n,{allowEmpty:this.getAllowClear(),prefixCls:h,getPopupContainer:o||y,format:f,value:this.sValue,placeholder:void 0===s?e.placeholder:s,addon:m,inputIcon:v,clearIcon:g}),class:d,ref:"timePicker",on:i()({},Object(u["j"])(this),{change:this.handleChange,open:this.handleOpenClose,close:this.handleOpenClose})};return t(At,_)}},render:function(){var e=arguments[0];return e(Tt["a"],{attrs:{componentName:"TimePicker",defaultLocale:this.getDefaultLocale()},scopedSlots:{default:this.renderTimePicker}})},install:function(e){e.use(Vt["a"]),e.component(qt.name,qt)}},Kt=n("b4a0"),Gt={date:"YYYY-MM-DD",dateTime:"YYYY-MM-DD HH:mm:ss",week:"gggg-wo",month:"YYYY-MM"},Xt={date:"dateFormat",dateTime:"dateTimeFormat",week:"weekFormat",month:"monthFormat"};function Jt(e){var t=e.showHour,n=e.showMinute,r=e.showSecond,i=e.use12Hours,a=0;return t&&(a+=1),n&&(a+=1),r&&(a+=1),i&&(a+=1),a}function Zt(e,t,n){return{name:e.name,props:Object(u["s"])(t,{transitionName:"slide-up",popupStyle:{},locale:{}}),model:{prop:"value",event:"change"},inject:{configProvider:{default:function(){return st["a"]}}},provide:function(){return{savePopupRef:this.savePopupRef}},mounted:function(){var e=this,t=this.autoFocus,n=this.disabled,r=this.value,i=this.defaultValue,a=this.valueFormat;Nt("DatePicker",i,"defaultValue",a),Nt("DatePicker",r,"value",a),t&&!n&&this.$nextTick((function(){e.focus()}))},watch:{value:function(e){Nt("DatePicker",e,"value",this.valueFormat)}},methods:{getDefaultLocale:function(){var e=i()({},Kt["a"],this.locale);return e.lang=i()({},e.lang,(this.locale||{}).lang),e},savePopupRef:function(e){this.popupRef=e},handleOpenChange:function(e){this.$emit("openChange",e)},handleFocus:function(e){this.$emit("focus",e)},handleBlur:function(e){this.$emit("blur",e)},handleMouseEnter:function(e){this.$emit("mouseenter",e)},handleMouseLeave:function(e){this.$emit("mouseleave",e)},handleChange:function(e,t){this.$emit("change",this.valueFormat?$t(e,this.valueFormat):e,t)},handleOk:function(e){this.$emit("ok",this.valueFormat?$t(e,this.valueFormat):e)},handleCalendarChange:function(e,t){this.$emit("calendarChange",this.valueFormat?$t(e,this.valueFormat):e,t)},focus:function(){this.$refs.picker.focus()},blur:function(){this.$refs.picker.blur()},transformValue:function(e){"value"in e&&(e.value=Bt(e.value,this.valueFormat)),"defaultValue"in e&&(e.defaultValue=Bt(e.defaultValue,this.valueFormat)),"defaultPickerValue"in e&&(e.defaultPickerValue=Bt(e.defaultPickerValue,this.valueFormat))},renderPicker:function(t,r){var a,o=this,s=this.$createElement,c=Object(u["k"])(this);this.transformValue(c);var l=c.prefixCls,h=c.inputPrefixCls,f=c.getCalendarContainer,d=c.size,p=c.showTime,m=c.disabled,v=c.format,g=p?n+"Time":n,y=v||t[Xt[g]]||Gt[g],w=this.configProvider,M=w.getPrefixCls,x=w.getPopupContainer,S=f||x,k=M("calendar",l),C=M("input",h),L=_()(k+"-picker",b()({},k+"-picker-"+d,!!d)),T=_()(k+"-picker-input",C,(a={},b()(a,C+"-lg","large"===d),b()(a,C+"-sm","small"===d),b()(a,C+"-disabled",m),a)),O=p&&p.format||"HH:mm:ss",z=i()({},Wt(O),{format:O,use12Hours:p&&p.use12Hours}),D=Jt(z),j=k+"-time-picker-column-"+D,H={props:i()({},z,p,{prefixCls:k+"-time-picker",placeholder:t.timePickerLocale.placeholder,transitionName:"slide-up"}),class:j,on:{esc:function(){}}},E=p?s(Lt,H):null,A={props:i()({},c,{getCalendarContainer:S,format:y,pickerClass:L,pickerInputClass:T,locale:t,localeCode:r,timePicker:E}),on:i()({},Object(u["j"])(this),{openChange:this.handleOpenChange,focus:this.handleFocus,blur:this.handleBlur,mouseenter:this.handleMouseEnter,mouseleave:this.handleMouseLeave,change:this.handleChange,ok:this.handleOk,calendarChange:this.handleCalendarChange}),ref:"picker",scopedSlots:this.$scopedSlots||{}};return s(e,A,[this.$slots&&Object.keys(this.$slots).map((function(e){return s("template",{slot:e,key:e},[o.$slots[e]])}))])}},render:function(){var e=arguments[0];return e(Tt["a"],{attrs:{componentName:"DatePicker",defaultLocale:this.getDefaultLocale},scopedSlots:{default:this.renderPicker}})}}}var Qt=n("b24f"),en=n.n(Qt),tn=n("9b57"),nn=n.n(tn);function rn(){}var an={mixins:[l["a"]],props:{prefixCls:c["a"].string,value:c["a"].any,hoverValue:c["a"].any,selectedValue:c["a"].any,direction:c["a"].any,locale:c["a"].any,showDateInput:c["a"].bool,showTimePicker:c["a"].bool,showWeekNumber:c["a"].bool,format:c["a"].any,placeholder:c["a"].any,disabledDate:c["a"].any,timePicker:c["a"].any,disabledTime:c["a"].any,disabledMonth:c["a"].any,mode:c["a"].any,timePickerDisabledTime:c["a"].object,enableNext:c["a"].any,enablePrev:c["a"].any,clearIcon:c["a"].any,dateRender:c["a"].func,inputMode:c["a"].string,inputReadOnly:c["a"].bool},render:function(){var e=arguments[0],t=this.$props,n=t.prefixCls,r=t.value,a=t.hoverValue,o=t.selectedValue,s=t.mode,c=t.direction,l=t.locale,f=t.format,d=t.placeholder,p=t.disabledDate,m=t.timePicker,v=t.disabledTime,g=t.timePickerDisabledTime,b=t.showTimePicker,y=t.enablePrev,_=t.enableNext,w=t.disabledMonth,M=t.showDateInput,x=t.dateRender,S=t.showWeekNumber,k=t.showClear,C=t.inputMode,T=t.inputReadOnly,O=Object(u["f"])(this,"clearIcon"),z=Object(u["j"])(this),D=z.inputChange,j=void 0===D?rn:D,H=z.inputSelect,E=void 0===H?rn:H,A=z.valueChange,P=void 0===A?rn:A,Y=z.panelChange,V=void 0===Y?rn:Y,F=z.select,R=void 0===F?rn:F,N=z.dayHover,B=void 0===N?rn:N,$=b&&m,W=$&&v?L(o,v):null,U=n+"-range",q={locale:l,value:r,prefixCls:n,showTimePicker:b},K="left"===c?0:1,G=null;if($){var X=Object(u["k"])(m);G=Object(h["a"])(m,{props:i()({showHour:!0,showMinute:!0,showSecond:!0},X,W,g,{defaultOpenValue:r,value:o[K]}),on:{change:j}})}var J=M&&e(Ee,{attrs:{format:f,locale:l,prefixCls:n,timePicker:m,disabledDate:p,placeholder:d,disabledTime:v,value:r,showClear:k||!1,selectedValue:o[K],clearIcon:O,inputMode:C,inputReadOnly:T},on:{change:j,select:E}}),Z={props:i()({},q,{mode:s,enableNext:_,enablePrev:y,disabledMonth:w}),on:{valueChange:P,panelChange:V}},Q={props:i()({},q,{hoverValue:a,selectedValue:o,dateRender:x,disabledDate:p,showWeekNumber:S}),on:{select:R,dayHover:B}};return e("div",{class:U+"-part "+U+"-"+c},[J,e("div",{style:{outline:"none"}},[e(fe,Z),b?e("div",{class:n+"-time-picker"},[e("div",{class:n+"-time-picker-panel"},[G])]):null,e("div",{class:n+"-body"},[e(I,Q)])])])}},on=an;function sn(){}function cn(e){return Array.isArray(e)&&(0===e.length||e.every((function(e){return!e})))}function ln(e,t){if(e===t)return!0;if(null===e||"undefined"===typeof e||null===t||"undefined"===typeof t)return!1;if(e.length!==t.length)return!1;for(var n=0;n0&&(i[1-a]=this.sShowTimePicker?i[a]:void 0),this.__emit("inputSelect",i),this.fireSelectValueChange(i,null,n||{source:"dateInput"})}}var pn={props:{locale:c["a"].object.def(Ae["a"]),visible:c["a"].bool.def(!0),prefixCls:c["a"].string.def("rc-calendar"),dateInputPlaceholder:c["a"].any,seperator:c["a"].string.def("~"),defaultValue:c["a"].any,value:c["a"].any,hoverValue:c["a"].any,mode:c["a"].arrayOf(c["a"].oneOf(["time","date","month","year","decade"])),showDateInput:c["a"].bool.def(!0),timePicker:c["a"].any,showOk:c["a"].bool,showToday:c["a"].bool.def(!0),defaultSelectedValue:c["a"].array.def([]),selectedValue:c["a"].array,showClear:c["a"].bool,showWeekNumber:c["a"].bool,format:c["a"].oneOfType([c["a"].string,c["a"].arrayOf(c["a"].string),c["a"].func]),type:c["a"].any.def("both"),disabledDate:c["a"].func,disabledTime:c["a"].func.def(sn),renderFooter:c["a"].func.def((function(){return null})),renderSidebar:c["a"].func.def((function(){return null})),dateRender:c["a"].func,clearIcon:c["a"].any,inputReadOnly:c["a"].bool},mixins:[l["a"],Oe],data:function(){var e=this.$props,t=e.selectedValue||e.defaultSelectedValue,n=hn(e,1);return{sSelectedValue:t,prevSelectedValue:t,firstSelectedValue:null,sHoverValue:e.hoverValue||[],sValue:n,sShowTimePicker:!1,sMode:e.mode||["date","date"],sPanelTriggerSource:""}},watch:{value:function(){var e={};e.sValue=hn(this.$props,0),this.setState(e)},hoverValue:function(e){ln(this.sHoverValue,e)||this.setState({sHoverValue:e})},selectedValue:function(e){var t={};t.sSelectedValue=e,t.prevSelectedValue=e,this.setState(t)},mode:function(e){ln(this.sMode,e)||this.setState({sMode:e})}},methods:{onDatePanelEnter:function(){this.hasSelectedValue()&&this.fireHoverValueChange(this.sSelectedValue.concat())},onDatePanelLeave:function(){this.hasSelectedValue()&&this.fireHoverValueChange([])},onSelect:function(e){var t=this.type,n=this.sSelectedValue,r=this.prevSelectedValue,i=this.firstSelectedValue,a=void 0;if("both"===t)i?this.compare(i,e)<0?(C(r[1],e),a=[i,e]):(C(r[0],e),C(r[1],i),a=[e,i]):(C(r[0],e),a=[e]);else if("start"===t){C(r[0],e);var o=n[1];a=o&&this.compare(o,e)>0?[e,o]:[e]}else{var s=n[0];s&&this.compare(s,e)<=0?(C(r[1],e),a=[s,e]):(C(r[0],e),a=[e])}this.fireSelectValueChange(a)},onKeyDown:function(e){var t=this;if("input"!==e.target.nodeName.toLowerCase()){var n=e.keyCode,r=e.ctrlKey||e.metaKey,i=this.$data,a=i.sSelectedValue,o=i.sHoverValue,s=i.firstSelectedValue,c=i.sValue,l=this.$props.disabledDate,u=function(n){var r=void 0,i=void 0,l=void 0;if(s?1===o.length?(r=o[0].clone(),i=n(r),l=t.onDayHover(i)):(r=o[0].isSame(s,"day")?o[1]:o[0],i=n(r),l=t.onDayHover(i)):(r=o[0]||a[0]||c[0]||p()(),i=n(r),l=[i],t.fireHoverValueChange(l)),l.length>=2){var u=l.some((function(e){return!Ie(c,e,"month")}));if(u){var h=l.slice().sort((function(e,t){return e.valueOf()-t.valueOf()}));h[0].isSame(h[1],"month")&&(h[1]=h[0].clone().add(1,"month")),t.fireValueChange(h)}}else if(1===l.length){var f=c.findIndex((function(e){return e.isSame(r,"month")}));if(-1===f&&(f=0),c.every((function(e){return!e.isSame(i,"month")}))){var d=c.slice();d[f]=i.clone(),t.fireValueChange(d)}}return e.preventDefault(),i};switch(n){case f["a"].DOWN:return void u((function(e){return Ve(e,1,"weeks")}));case f["a"].UP:return void u((function(e){return Ve(e,-1,"weeks")}));case f["a"].LEFT:return void u(r?function(e){return Ve(e,-1,"years")}:function(e){return Ve(e,-1,"days")});case f["a"].RIGHT:return void u(r?function(e){return Ve(e,1,"years")}:function(e){return Ve(e,1,"days")});case f["a"].HOME:return void u((function(e){return Pe(e)}));case f["a"].END:return void u((function(e){return Ye(e)}));case f["a"].PAGE_DOWN:return void u((function(e){return Ve(e,1,"month")}));case f["a"].PAGE_UP:return void u((function(e){return Ve(e,-1,"month")}));case f["a"].ENTER:var h=void 0;return h=0===o.length?u((function(e){return e})):1===o.length?o[0]:o[0].isSame(s,"day")?o[1]:o[0],!h||l&&l(h)||this.onSelect(h),void e.preventDefault();default:this.__emit("keydown",e)}}},onDayHover:function(e){var t=[],n=this.sSelectedValue,r=this.firstSelectedValue,i=this.type;if("start"===i&&n[1])t=this.compare(e,n[1])<0?[e,n[1]]:[e];else if("end"===i&&n[0])t=this.compare(e,n[0])>0?[n[0],e]:[];else{if(!r)return this.sHoverValue.length&&this.setState({sHoverValue:[]}),t;t=this.compare(e,r)<0?[e,r]:[r,e]}return this.fireHoverValueChange(t),t},onToday:function(){var e=M(this.sValue[0]),t=e.clone().add(1,"months");this.setState({sValue:[e,t]})},onOpenTimePicker:function(){this.setState({sShowTimePicker:!0})},onCloseTimePicker:function(){this.setState({sShowTimePicker:!1})},onOk:function(){var e=this.sSelectedValue;this.isAllowedDateAndTime(e)&&this.__emit("ok",e)},onStartInputChange:function(){for(var e=arguments.length,t=Array(e),n=0;n-1},hasSelectedValue:function(){var e=this.sSelectedValue;return!!e[1]&&!!e[0]},compare:function(e,t){return this.timePicker?e.diff(t):e.diff(t,"days")},fireSelectValueChange:function(e,t,n){var r=this.timePicker,i=this.prevSelectedValue;if(r){var a=Object(u["k"])(r);if(a.defaultValue){var o=a.defaultValue;!i[0]&&e[0]&&C(o[0],e[0]),!i[1]&&e[1]&&C(o[1],e[1])}}if(!this.sSelectedValue[0]||!this.sSelectedValue[1]){var s=e[0]||p()(),c=e[1]||s.clone().add(1,"months");this.setState({sSelectedValue:e,sValue:e&&2===e.length?un([s,c]):this.sValue})}e[0]&&!e[1]&&(this.setState({firstSelectedValue:e[0]}),this.fireHoverValueChange(e.concat())),this.__emit("change",e),(t||e[0]&&e[1])&&(this.setState({prevSelectedValue:e,firstSelectedValue:null}),this.fireHoverValueChange([]),this.__emit("select",e,n)),Object(u["r"])(this,"selectedValue")||this.setState({sSelectedValue:e})},fireValueChange:function(e){Object(u["r"])(this,"value")||this.setState({sValue:e}),this.__emit("valueChange",e)},fireHoverValueChange:function(e){Object(u["r"])(this,"hoverValue")||this.setState({sHoverValue:e}),this.__emit("hoverChange",e)},clear:function(){this.fireSelectValueChange([],!0),this.__emit("clear")},disabledStartTime:function(e){return this.disabledTime(e,"start")},disabledEndTime:function(e){return this.disabledTime(e,"end")},disabledStartMonth:function(e){var t=this.sValue;return e.isAfter(t[1],"month")},disabledEndMonth:function(e){var t=this.sValue;return e.isBefore(t[0],"month")}},render:function(){var e,t,n=arguments[0],r=Object(u["k"])(this),i=r.prefixCls,a=r.dateInputPlaceholder,o=r.timePicker,s=r.showOk,c=r.locale,l=r.showClear,h=r.showToday,f=r.type,d=r.seperator,p=Object(u["f"])(this,"clearIcon"),m=this.sHoverValue,v=this.sSelectedValue,g=this.sMode,y=this.sShowTimePicker,_=this.sValue,w=(e={},b()(e,i,1),b()(e,i+"-hidden",!r.visible),b()(e,i+"-range",1),b()(e,i+"-show-time-picker",y),b()(e,i+"-week-number",r.showWeekNumber),e),x={props:r,on:Object(u["j"])(this)},S={props:{selectedValue:v},on:{select:this.onSelect,dayHover:"start"===f&&v[1]||"end"===f&&v[0]||m.length?this.onDayHover:sn}},k=void 0,C=void 0;if(a)if(Array.isArray(a)){var L=en()(a,2);k=L[0],C=L[1]}else k=C=a;var T=!0===s||!1!==s&&!!o,O=(t={},b()(t,i+"-footer",!0),b()(t,i+"-range-bottom",!0),b()(t,i+"-footer-show-ok",T),t),z=this.getStartValue(),D=this.getEndValue(),j=M(z),H=j.month(),E=j.year(),A=z.year()===E&&z.month()===H||D.year()===E&&D.month()===H,P=z.clone().add(1,"months"),Y=P.year()===D.year()&&P.month()===D.month(),V=Object(u["w"])(x,S,{props:{hoverValue:m,direction:"left",disabledTime:this.disabledStartTime,disabledMonth:this.disabledStartMonth,format:this.getFormat(),value:z,mode:g[0],placeholder:k,showDateInput:this.showDateInput,timePicker:o,showTimePicker:y||"time"===g[0],enablePrev:!0,enableNext:!Y||this.isMonthYearPanelShow(g[1]),clearIcon:p},on:{inputChange:this.onStartInputChange,inputSelect:this.onStartInputSelect,valueChange:this.onStartValueChange,panelChange:this.onStartPanelChange}}),I=Object(u["w"])(x,S,{props:{hoverValue:m,direction:"right",format:this.getFormat(),timePickerDisabledTime:this.getEndDisableTime(),placeholder:C,value:D,mode:g[1],showDateInput:this.showDateInput,timePicker:o,showTimePicker:y||"time"===g[1],disabledTime:this.disabledEndTime,disabledMonth:this.disabledEndMonth,enablePrev:!Y||this.isMonthYearPanelShow(g[0]),enableNext:!0,clearIcon:p},on:{inputChange:this.onEndInputChange,inputSelect:this.onEndInputSelect,valueChange:this.onEndValueChange,panelChange:this.onEndPanelChange}}),F=null;if(h){var R=Object(u["w"])(x,{props:{disabled:A,value:_[0],text:c.backToToday},on:{today:this.onToday}});F=n(ve,pe()([{key:"todayButton"},R]))}var N=null;if(r.timePicker){var B=Object(u["w"])(x,{props:{showTimePicker:y||"time"===g[0]&&"time"===g[1],timePickerDisabled:!this.hasSelectedValue()||m.length},on:{openTimePicker:this.onOpenTimePicker,closeTimePicker:this.onCloseTimePicker}});N=n(_e,pe()([{key:"timePickerButton"},B]))}var $=null;if(T){var W=Object(u["w"])(x,{props:{okDisabled:!this.isAllowedDateAndTime(v)||!this.hasSelectedValue()||m.length},on:{ok:this.onOk}});$=n(be,pe()([{key:"okButtonNode"},W]))}var U=this.renderFooter(g);return 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W=Array.isArray(r.placeholder)?r.placeholder[0]:H.lang.rangePlaceholder[0],U=Array.isArray(r.placeholder)?r.placeholder[1]:H.lang.rangePlaceholder[1],q=Object(u["w"])($,{props:{separator:P,format:A,prefixCls:I,renderFooter:this.renderFooter,timePicker:r.timePicker,disabledDate:T,disabledTime:O,dateInputPlaceholder:[W,U],locale:H.lang,dateRender:R,value:s,hoverValue:c,showToday:D,inputReadOnly:Y},on:{change:p,ok:v,valueChange:this.handleShowDateChange,hoverChange:this.handleHoverChange,panelChange:S,inputSelect:this.handleCalendarInputSelect},class:N,scopedSlots:h}),K=n(mn,q),G={};r.showTime&&(G.width="350px");var X=en()(o,2),J=X[0],Z=X[1],Q=!r.disabled&&r.allowClear&&o&&(J||Z)?n(ot["a"],{attrs:{type:"close-circle",theme:"filled"},class:I+"-picker-clear",on:{click:this.clearSelection}}):null,ee=n(kn,{attrs:{suffixIcon:a,prefixCls:I}}),te=function(e){var t=e.value,i=en()(t,2),a=i[0],o=i[1];return 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n&&e.year()===n.year()&&e.week()===n.week()?t("div",{class:r+"-selected-day"},[t("div",{class:r+"-date"},[o])]):t("div",{class:r+"-date"},[o])},handleChange:function(e){Object(u["r"])(this,"value")||this.setState({_value:e}),this.$emit("change",e,jn(e,this.format))},handleOpenChange:function(e){Object(u["r"])(this,"open")||this.setState({_open:e}),this.$emit("openChange",e)},clearSelection:function(e){e.preventDefault(),e.stopPropagation(),this.handleChange(null)},focus:function(){this.$refs.input.focus()},blur:function(){this.$refs.input.blur()},renderFooter:function(){var e=this.$createElement,t=this._prefixCls,n=this.$scopedSlots,r=this.renderExtraFooter||n.renderExtraFooter;return r?e("div",{class:t+"-footer-extra"},[r.apply(void 0,arguments)]):null}},render:function(){var e=arguments[0],t=Object(u["k"])(this),n=Object(u["f"])(this,"suffixIcon");n=Array.isArray(n)?n[0]:n;var 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ge=(l+r|0)+((8191&i)<<13)|0;l=(a+(i>>>13)|0)+(ge>>>26)|0,ge&=67108863,r=Math.imul(g,R),i=Math.imul(g,N),i=i+Math.imul(b,R)|0,a=Math.imul(b,N),r=r+Math.imul(p,$)|0,i=i+Math.imul(p,W)|0,i=i+Math.imul(m,$)|0,a=a+Math.imul(m,W)|0,r=r+Math.imul(h,q)|0,i=i+Math.imul(h,K)|0,i=i+Math.imul(f,q)|0,a=a+Math.imul(f,K)|0;var be=(l+r|0)+((8191&i)<<13)|0;l=(a+(i>>>13)|0)+(be>>>26)|0,be&=67108863,r=Math.imul(_,R),i=Math.imul(_,N),i=i+Math.imul(w,R)|0,a=Math.imul(w,N),r=r+Math.imul(g,$)|0,i=i+Math.imul(g,W)|0,i=i+Math.imul(b,$)|0,a=a+Math.imul(b,W)|0,r=r+Math.imul(p,q)|0,i=i+Math.imul(p,K)|0,i=i+Math.imul(m,q)|0,a=a+Math.imul(m,K)|0,r=r+Math.imul(h,X)|0,i=i+Math.imul(h,J)|0,i=i+Math.imul(f,X)|0,a=a+Math.imul(f,J)|0;var ye=(l+r|0)+((8191&i)<<13)|0;l=(a+(i>>>13)|0)+(ye>>>26)|0,ye&=67108863,r=Math.imul(x,R),i=Math.imul(x,N),i=i+Math.imul(S,R)|0,a=Math.imul(S,N),r=r+Math.imul(_,$)|0,i=i+Math.imul(_,W)|0,i=i+Math.imul(w,$)|0,a=a+Math.imul(w,W)|0,r=r+Math.imul(g,q)|0,i=i+Math.imul(g,K)|0,i=i+Math.imul(b,q)|0,a=a+Math.imul(b,K)|0,r=r+Math.imul(p,X)|0,i=i+Math.imul(p,J)|0,i=i+Math.imul(m,X)|0,a=a+Math.imul(m,J)|0,r=r+Math.imul(h,Q)|0,i=i+Math.imul(h,ee)|0,i=i+Math.imul(f,Q)|0,a=a+Math.imul(f,ee)|0;var _e=(l+r|0)+((8191&i)<<13)|0;l=(a+(i>>>13)|0)+(_e>>>26)|0,_e&=67108863,r=Math.imul(C,R),i=Math.imul(C,N),i=i+Math.imul(L,R)|0,a=Math.imul(L,N),r=r+Math.imul(x,$)|0,i=i+Math.imul(x,W)|0,i=i+Math.imul(S,$)|0,a=a+Math.imul(S,W)|0,r=r+Math.imul(_,q)|0,i=i+Math.imul(_,K)|0,i=i+Math.imul(w,q)|0,a=a+Math.imul(w,K)|0,r=r+Math.imul(g,X)|0,i=i+Math.imul(g,J)|0,i=i+Math.imul(b,X)|0,a=a+Math.imul(b,J)|0,r=r+Math.imul(p,Q)|0,i=i+Math.imul(p,ee)|0,i=i+Math.imul(m,Q)|0,a=a+Math.imul(m,ee)|0,r=r+Math.imul(h,ne)|0,i=i+Math.imul(h,re)|0,i=i+Math.imul(f,ne)|0,a=a+Math.imul(f,re)|0;var we=(l+r|0)+((8191&i)<<13)|0;l=(a+(i>>>13)|0)+(we>>>26)|0,we&=67108863,r=Math.imul(O,R),i=Math.imul(O,N),i=i+Math.imul(z,R)|0,a=Math.imul(z,N),r=r+Math.imul(C,$)|0,i=i+Math.imul(C,W)|0,i=i+Math.imul(L,$)|0,a=a+Math.imul(L,W)|0,r=r+Math.imul(x,q)|0,i=i+Math.imul(x,K)|0,i=i+Math.imul(S,q)|0,a=a+Math.imul(S,K)|0,r=r+Math.imul(_,X)|0,i=i+Math.imul(_,J)|0,i=i+Math.imul(w,X)|0,a=a+Math.imul(w,J)|0,r=r+Math.imul(g,Q)|0,i=i+Math.imul(g,ee)|0,i=i+Math.imul(b,Q)|0,a=a+Math.imul(b,ee)|0,r=r+Math.imul(p,ne)|0,i=i+Math.imul(p,re)|0,i=i+Math.imul(m,ne)|0,a=a+Math.imul(m,re)|0,r=r+Math.imul(h,ae)|0,i=i+Math.imul(h,oe)|0,i=i+Math.imul(f,ae)|0,a=a+Math.imul(f,oe)|0;var Me=(l+r|0)+((8191&i)<<13)|0;l=(a+(i>>>13)|0)+(Me>>>26)|0,Me&=67108863,r=Math.imul(j,R),i=Math.imul(j,N),i=i+Math.imul(H,R)|0,a=Math.imul(H,N),r=r+Math.imul(O,$)|0,i=i+Math.imul(O,W)|0,i=i+Math.imul(z,$)|0,a=a+Math.imul(z,W)|0,r=r+Math.imul(C,q)|0,i=i+Math.imul(C,K)|0,i=i+Math.imul(L,q)|0,a=a+Math.imul(L,K)|0,r=r+Math.imul(x,X)|0,i=i+Math.imul(x,J)|0,i=i+Math.imul(S,X)|0,a=a+Math.imul(S,J)|0,r=r+Math.imul(_,Q)|0,i=i+Math.imul(_,ee)|0,i=i+Math.imul(w,Q)|0,a=a+Math.imul(w,ee)|0,r=r+Math.imul(g,ne)|0,i=i+Math.imul(g,re)|0,i=i+Math.imul(b,ne)|0,a=a+Math.imul(b,re)|0,r=r+Math.imul(p,ae)|0,i=i+Math.imul(p,oe)|0,i=i+Math.imul(m,ae)|0,a=a+Math.imul(m,oe)|0,r=r+Math.imul(h,ce)|0,i=i+Math.imul(h,le)|0,i=i+Math.imul(f,ce)|0,a=a+Math.imul(f,le)|0;var xe=(l+r|0)+((8191&i)<<13)|0;l=(a+(i>>>13)|0)+(xe>>>26)|0,xe&=67108863,r=Math.imul(A,R),i=Math.imul(A,N),i=i+Math.imul(P,R)|0,a=Math.imul(P,N),r=r+Math.imul(j,$)|0,i=i+Math.imul(j,W)|0,i=i+Math.imul(H,$)|0,a=a+Math.imul(H,W)|0,r=r+Math.imul(O,q)|0,i=i+Math.imul(O,K)|0,i=i+Math.imul(z,q)|0,a=a+Math.imul(z,K)|0,r=r+Math.imul(C,X)|0,i=i+Math.imul(C,J)|0,i=i+Math.imul(L,X)|0,a=a+Math.imul(L,J)|0,r=r+Math.imul(x,Q)|0,i=i+Math.imul(x,ee)|0,i=i+Math.imul(S,Q)|0,a=a+Math.imul(S,ee)|0,r=r+Math.imul(_,ne)|0,i=i+Math.imul(_,re)|0,i=i+Math.imul(w,ne)|0,a=a+Math.imul(w,re)|0,r=r+Math.imul(g,ae)|0,i=i+Math.imul(g,oe)|0,i=i+Math.imul(b,ae)|0,a=a+Math.imul(b,oe)|0,r=r+Math.imul(p,ce)|0,i=i+Math.imul(p,le)|0,i=i+Math.imul(m,ce)|0,a=a+Math.imul(m,le)|0,r=r+Math.imul(h,he)|0,i=i+Math.imul(h,fe)|0,i=i+Math.imul(f,he)|0,a=a+Math.imul(f,fe)|0;var Se=(l+r|0)+((8191&i)<<13)|0;l=(a+(i>>>13)|0)+(Se>>>26)|0,Se&=67108863,r=Math.imul(V,R),i=Math.imul(V,N),i=i+Math.imul(I,R)|0,a=Math.imul(I,N),r=r+Math.imul(A,$)|0,i=i+Math.imul(A,W)|0,i=i+Math.imul(P,$)|0,a=a+Math.imul(P,W)|0,r=r+Math.imul(j,q)|0,i=i+Math.imul(j,K)|0,i=i+Math.imul(H,q)|0,a=a+Math.imul(H,K)|0,r=r+Math.imul(O,X)|0,i=i+Math.imul(O,J)|0,i=i+Math.imul(z,X)|0,a=a+Math.imul(z,J)|0,r=r+Math.imul(C,Q)|0,i=i+Math.imul(C,ee)|0,i=i+Math.imul(L,Q)|0,a=a+Math.imul(L,ee)|0,r=r+Math.imul(x,ne)|0,i=i+Math.imul(x,re)|0,i=i+Math.imul(S,ne)|0,a=a+Math.imul(S,re)|0,r=r+Math.imul(_,ae)|0,i=i+Math.imul(_,oe)|0,i=i+Math.imul(w,ae)|0,a=a+Math.imul(w,oe)|0,r=r+Math.imul(g,ce)|0,i=i+Math.imul(g,le)|0,i=i+Math.imul(b,ce)|0,a=a+Math.imul(b,le)|0,r=r+Math.imul(p,he)|0,i=i+Math.imul(p,fe)|0,i=i+Math.imul(m,he)|0,a=a+Math.imul(m,fe)|0,r=r+Math.imul(h,pe)|0,i=i+Math.imul(h,me)|0,i=i+Math.imul(f,pe)|0,a=a+Math.imul(f,me)|0;var ke=(l+r|0)+((8191&i)<<13)|0;l=(a+(i>>>13)|0)+(ke>>>26)|0,ke&=67108863,r=Math.imul(V,$),i=Math.imul(V,W),i=i+Math.imul(I,$)|0,a=Math.imul(I,W),r=r+Math.imul(A,q)|0,i=i+Math.imul(A,K)|0,i=i+Math.imul(P,q)|0,a=a+Math.imul(P,K)|0,r=r+Math.imul(j,X)|0,i=i+Math.imul(j,J)|0,i=i+Math.imul(H,X)|0,a=a+Math.imul(H,J)|0,r=r+Math.imul(O,Q)|0,i=i+Math.imul(O,ee)|0,i=i+Math.imul(z,Q)|0,a=a+Math.imul(z,ee)|0,r=r+Math.imul(C,ne)|0,i=i+Math.imul(C,re)|0,i=i+Math.imul(L,ne)|0,a=a+Math.imul(L,re)|0,r=r+Math.imul(x,ae)|0,i=i+Math.imul(x,oe)|0,i=i+Math.imul(S,ae)|0,a=a+Math.imul(S,oe)|0,r=r+Math.imul(_,ce)|0,i=i+Math.imul(_,le)|0,i=i+Math.imul(w,ce)|0,a=a+Math.imul(w,le)|0,r=r+Math.imul(g,he)|0,i=i+Math.imul(g,fe)|0,i=i+Math.imul(b,he)|0,a=a+Math.imul(b,fe)|0,r=r+Math.imul(p,pe)|0,i=i+Math.imul(p,me)|0,i=i+Math.imul(m,pe)|0,a=a+Math.imul(m,me)|0;var Ce=(l+r|0)+((8191&i)<<13)|0;l=(a+(i>>>13)|0)+(Ce>>>26)|0,Ce&=67108863,r=Math.imul(V,q),i=Math.imul(V,K),i=i+Math.imul(I,q)|0,a=Math.imul(I,K),r=r+Math.imul(A,X)|0,i=i+Math.imul(A,J)|0,i=i+Math.imul(P,X)|0,a=a+Math.imul(P,J)|0,r=r+Math.imul(j,Q)|0,i=i+Math.imul(j,ee)|0,i=i+Math.imul(H,Q)|0,a=a+Math.imul(H,ee)|0,r=r+Math.imul(O,ne)|0,i=i+Math.imul(O,re)|0,i=i+Math.imul(z,ne)|0,a=a+Math.imul(z,re)|0,r=r+Math.imul(C,ae)|0,i=i+Math.imul(C,oe)|0,i=i+Math.imul(L,ae)|0,a=a+Math.imul(L,oe)|0,r=r+Math.imul(x,ce)|0,i=i+Math.imul(x,le)|0,i=i+Math.imul(S,ce)|0,a=a+Math.imul(S,le)|0,r=r+Math.imul(_,he)|0,i=i+Math.imul(_,fe)|0,i=i+Math.imul(w,he)|0,a=a+Math.imul(w,fe)|0,r=r+Math.imul(g,pe)|0,i=i+Math.imul(g,me)|0,i=i+Math.imul(b,pe)|0,a=a+Math.imul(b,me)|0;var Le=(l+r|0)+((8191&i)<<13)|0;l=(a+(i>>>13)|0)+(Le>>>26)|0,Le&=67108863,r=Math.imul(V,X),i=Math.imul(V,J),i=i+Math.imul(I,X)|0,a=Math.imul(I,J),r=r+Math.imul(A,Q)|0,i=i+Math.imul(A,ee)|0,i=i+Math.imul(P,Q)|0,a=a+Math.imul(P,ee)|0,r=r+Math.imul(j,ne)|0,i=i+Math.imul(j,re)|0,i=i+Math.imul(H,ne)|0,a=a+Math.imul(H,re)|0,r=r+Math.imul(O,ae)|0,i=i+Math.imul(O,oe)|0,i=i+Math.imul(z,ae)|0,a=a+Math.imul(z,oe)|0,r=r+Math.imul(C,ce)|0,i=i+Math.imul(C,le)|0,i=i+Math.imul(L,ce)|0,a=a+Math.imul(L,le)|0,r=r+Math.imul(x,he)|0,i=i+Math.imul(x,fe)|0,i=i+Math.imul(S,he)|0,a=a+Math.imul(S,fe)|0,r=r+Math.imul(_,pe)|0,i=i+Math.imul(_,me)|0,i=i+Math.imul(w,pe)|0,a=a+Math.imul(w,me)|0;var Te=(l+r|0)+((8191&i)<<13)|0;l=(a+(i>>>13)|0)+(Te>>>26)|0,Te&=67108863,r=Math.imul(V,Q),i=Math.imul(V,ee),i=i+Math.imul(I,Q)|0,a=Math.imul(I,ee),r=r+Math.imul(A,ne)|0,i=i+Math.imul(A,re)|0,i=i+Math.imul(P,ne)|0,a=a+Math.imul(P,re)|0,r=r+Math.imul(j,ae)|0,i=i+Math.imul(j,oe)|0,i=i+Math.imul(H,ae)|0,a=a+Math.imul(H,oe)|0,r=r+Math.imul(O,ce)|0,i=i+Math.imul(O,le)|0,i=i+Math.imul(z,ce)|0,a=a+Math.imul(z,le)|0,r=r+Math.imul(C,he)|0,i=i+Math.imul(C,fe)|0,i=i+Math.imul(L,he)|0,a=a+Math.imul(L,fe)|0,r=r+Math.imul(x,pe)|0,i=i+Math.imul(x,me)|0,i=i+Math.imul(S,pe)|0,a=a+Math.imul(S,me)|0;var Oe=(l+r|0)+((8191&i)<<13)|0;l=(a+(i>>>13)|0)+(Oe>>>26)|0,Oe&=67108863,r=Math.imul(V,ne),i=Math.imul(V,re),i=i+Math.imul(I,ne)|0,a=Math.imul(I,re),r=r+Math.imul(A,ae)|0,i=i+Math.imul(A,oe)|0,i=i+Math.imul(P,ae)|0,a=a+Math.imul(P,oe)|0,r=r+Math.imul(j,ce)|0,i=i+Math.imul(j,le)|0,i=i+Math.imul(H,ce)|0,a=a+Math.imul(H,le)|0,r=r+Math.imul(O,he)|0,i=i+Math.imul(O,fe)|0,i=i+Math.imul(z,he)|0,a=a+Math.imul(z,fe)|0,r=r+Math.imul(C,pe)|0,i=i+Math.imul(C,me)|0,i=i+Math.imul(L,pe)|0,a=a+Math.imul(L,me)|0;var ze=(l+r|0)+((8191&i)<<13)|0;l=(a+(i>>>13)|0)+(ze>>>26)|0,ze&=67108863,r=Math.imul(V,ae),i=Math.imul(V,oe),i=i+Math.imul(I,ae)|0,a=Math.imul(I,oe),r=r+Math.imul(A,ce)|0,i=i+Math.imul(A,le)|0,i=i+Math.imul(P,ce)|0,a=a+Math.imul(P,le)|0,r=r+Math.imul(j,he)|0,i=i+Math.imul(j,fe)|0,i=i+Math.imul(H,he)|0,a=a+Math.imul(H,fe)|0,r=r+Math.imul(O,pe)|0,i=i+Math.imul(O,me)|0,i=i+Math.imul(z,pe)|0,a=a+Math.imul(z,me)|0;var De=(l+r|0)+((8191&i)<<13)|0;l=(a+(i>>>13)|0)+(De>>>26)|0,De&=67108863,r=Math.imul(V,ce),i=Math.imul(V,le),i=i+Math.imul(I,ce)|0,a=Math.imul(I,le),r=r+Math.imul(A,he)|0,i=i+Math.imul(A,fe)|0,i=i+Math.imul(P,he)|0,a=a+Math.imul(P,fe)|0,r=r+Math.imul(j,pe)|0,i=i+Math.imul(j,me)|0,i=i+Math.imul(H,pe)|0,a=a+Math.imul(H,me)|0;var je=(l+r|0)+((8191&i)<<13)|0;l=(a+(i>>>13)|0)+(je>>>26)|0,je&=67108863,r=Math.imul(V,he),i=Math.imul(V,fe),i=i+Math.imul(I,he)|0,a=Math.imul(I,fe),r=r+Math.imul(A,pe)|0,i=i+Math.imul(A,me)|0,i=i+Math.imul(P,pe)|0,a=a+Math.imul(P,me)|0;var He=(l+r|0)+((8191&i)<<13)|0;l=(a+(i>>>13)|0)+(He>>>26)|0,He&=67108863,r=Math.imul(V,pe),i=Math.imul(V,me),i=i+Math.imul(I,pe)|0,a=Math.imul(I,me);var Ee=(l+r|0)+((8191&i)<<13)|0;return l=(a+(i>>>13)|0)+(Ee>>>26)|0,Ee&=67108863,c[0]=ve,c[1]=ge,c[2]=be,c[3]=ye,c[4]=_e,c[5]=we,c[6]=Me,c[7]=xe,c[8]=Se,c[9]=ke,c[10]=Ce,c[11]=Le,c[12]=Te,c[13]=Oe,c[14]=ze,c[15]=De,c[16]=je,c[17]=He,c[18]=Ee,0!==l&&(c[19]=l,n.length++),n};function m(e,t,n){n.negative=t.negative^e.negative,n.length=e.length+t.length;for(var r=0,i=0,a=0;a>>26)|0,i+=o>>>26,o&=67108863}n.words[a]=s,r=o,o=i}return 0!==r?n.words[a]=r:n.length--,n.strip()}function v(e,t,n){var r=new g;return r.mulp(e,t,n)}function g(e,t){this.x=e,this.y=t}Math.imul||(p=d),a.prototype.mulTo=function(e,t){var n,r=this.length+e.length;return n=10===this.length&&10===e.length?p(this,e,t):r<63?d(this,e,t):r<1024?m(this,e,t):v(this,e,t),n},g.prototype.makeRBT=function(e){for(var t=new Array(e),n=a.prototype._countBits(e)-1,r=0;r>=1;return r},g.prototype.permute=function(e,t,n,r,i,a){for(var o=0;o>>=1)i++;return 1<>>=13,n[2*o+1]=8191&a,a>>>=13;for(o=2*t;o>=26,t+=i/67108864|0,t+=a>>>26,this.words[n]=67108863&a}return 0!==t&&(this.words[n]=t,this.length++),this},a.prototype.muln=function(e){return this.clone().imuln(e)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(e){var t=f(e);if(0===t.length)return new a(1);for(var n=this,r=0;r=0);var t,n=e%26,i=(e-n)/26,a=67108863>>>26-n<<26-n;if(0!==n){var o=0;for(t=0;t>>26-n}o&&(this.words[t]=o,this.length++)}if(0!==i){for(t=this.length-1;t>=0;t--)this.words[t+i]=this.words[t];for(t=0;t=0),i=t?(t-t%26)/26:0;var a=e%26,o=Math.min((e-a)/26,this.length),s=67108863^67108863>>>a<o)for(this.length-=o,l=0;l=0&&(0!==u||l>=i);l--){var h=0|this.words[l];this.words[l]=u<<26-a|h>>>a,u=h&s}return c&&0!==u&&(c.words[c.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(e,t,n){return r(0===this.negative),this.iushrn(e,t,n)},a.prototype.shln=function(e){return this.clone().ishln(e)},a.prototype.ushln=function(e){return this.clone().iushln(e)},a.prototype.shrn=function(e){return this.clone().ishrn(e)},a.prototype.ushrn=function(e){return this.clone().iushrn(e)},a.prototype.testn=function(e){r("number"===typeof e&&e>=0);var t=e%26,n=(e-t)/26,i=1<=0);var t=e%26,n=(e-t)/26;if(r(0===this.negative,"imaskn works only with positive numbers"),this.length<=n)return this;if(0!==t&&n++,this.length=Math.min(n,this.length),0!==t){var i=67108863^67108863>>>t<=67108864;t++)this.words[t]-=67108864,t===this.length-1?this.words[t+1]=1:this.words[t+1]++;return this.length=Math.max(this.length,t+1),this},a.prototype.isubn=function(e){if(r("number"===typeof e),r(e<67108864),e<0)return this.iaddn(-e);if(0!==this.negative)return this.negative=0,this.iaddn(e),this.negative=1,this;if(this.words[0]-=e,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var t=0;t>26)-(c/67108864|0),this.words[i+n]=67108863&a}for(;i>26,this.words[i+n]=67108863&a;if(0===s)return this.strip();for(r(-1===s),s=0,i=0;i>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(e,t){var n=this.length-e.length,r=this.clone(),i=e,o=0|i.words[i.length-1],s=this._countBits(o);n=26-s,0!==n&&(i=i.ushln(n),r.iushln(n),o=0|i.words[i.length-1]);var c,l=r.length-i.length;if("mod"!==t){c=new a(null),c.length=l+1,c.words=new Array(c.length);for(var u=0;u=0;f--){var d=67108864*(0|r.words[i.length+f])+(0|r.words[i.length+f-1]);d=Math.min(d/o|0,67108863),r._ishlnsubmul(i,d,f);while(0!==r.negative)d--,r.negative=0,r._ishlnsubmul(i,1,f),r.isZero()||(r.negative^=1);c&&(c.words[f]=d)}return c&&c.strip(),r.strip(),"div"!==t&&0!==n&&r.iushrn(n),{div:c||null,mod:r}},a.prototype.divmod=function(e,t,n){return r(!e.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===e.negative?(s=this.neg().divmod(e,t),"mod"!==t&&(i=s.div.neg()),"div"!==t&&(o=s.mod.neg(),n&&0!==o.negative&&o.iadd(e)),{div:i,mod:o}):0===this.negative&&0!==e.negative?(s=this.divmod(e.neg(),t),"mod"!==t&&(i=s.div.neg()),{div:i,mod:s.mod}):0!==(this.negative&e.negative)?(s=this.neg().divmod(e.neg(),t),"div"!==t&&(o=s.mod.neg(),n&&0!==o.negative&&o.isub(e)),{div:s.div,mod:o}):e.length>this.length||this.cmp(e)<0?{div:new a(0),mod:this}:1===e.length?"div"===t?{div:this.divn(e.words[0]),mod:null}:"mod"===t?{div:null,mod:new a(this.modn(e.words[0]))}:{div:this.divn(e.words[0]),mod:new a(this.modn(e.words[0]))}:this._wordDiv(e,t);var i,o,s},a.prototype.div=function(e){return this.divmod(e,"div",!1).div},a.prototype.mod=function(e){return this.divmod(e,"mod",!1).mod},a.prototype.umod=function(e){return this.divmod(e,"mod",!0).mod},a.prototype.divRound=function(e){var t=this.divmod(e);if(t.mod.isZero())return t.div;var n=0!==t.div.negative?t.mod.isub(e):t.mod,r=e.ushrn(1),i=e.andln(1),a=n.cmp(r);return a<0||1===i&&0===a?t.div:0!==t.div.negative?t.div.isubn(1):t.div.iaddn(1)},a.prototype.modn=function(e){r(e<=67108863);for(var t=(1<<26)%e,n=0,i=this.length-1;i>=0;i--)n=(t*n+(0|this.words[i]))%e;return n},a.prototype.idivn=function(e){r(e<=67108863);for(var t=0,n=this.length-1;n>=0;n--){var i=(0|this.words[n])+67108864*t;this.words[n]=i/e|0,t=i%e}return this.strip()},a.prototype.divn=function(e){return this.clone().idivn(e)},a.prototype.egcd=function(e){r(0===e.negative),r(!e.isZero());var t=this,n=e.clone();t=0!==t.negative?t.umod(e):t.clone();var i=new a(1),o=new a(0),s=new a(0),c=new a(1),l=0;while(t.isEven()&&n.isEven())t.iushrn(1),n.iushrn(1),++l;var u=n.clone(),h=t.clone();while(!t.isZero()){for(var f=0,d=1;0===(t.words[0]&d)&&f<26;++f,d<<=1);if(f>0){t.iushrn(f);while(f-- >0)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(h)),i.iushrn(1),o.iushrn(1)}for(var p=0,m=1;0===(n.words[0]&m)&&p<26;++p,m<<=1);if(p>0){n.iushrn(p);while(p-- >0)(s.isOdd()||c.isOdd())&&(s.iadd(u),c.isub(h)),s.iushrn(1),c.iushrn(1)}t.cmp(n)>=0?(t.isub(n),i.isub(s),o.isub(c)):(n.isub(t),s.isub(i),c.isub(o))}return{a:s,b:c,gcd:n.iushln(l)}},a.prototype._invmp=function(e){r(0===e.negative),r(!e.isZero());var t=this,n=e.clone();t=0!==t.negative?t.umod(e):t.clone();var i,o=new a(1),s=new a(0),c=n.clone();while(t.cmpn(1)>0&&n.cmpn(1)>0){for(var l=0,u=1;0===(t.words[0]&u)&&l<26;++l,u<<=1);if(l>0){t.iushrn(l);while(l-- >0)o.isOdd()&&o.iadd(c),o.iushrn(1)}for(var h=0,f=1;0===(n.words[0]&f)&&h<26;++h,f<<=1);if(h>0){n.iushrn(h);while(h-- >0)s.isOdd()&&s.iadd(c),s.iushrn(1)}t.cmp(n)>=0?(t.isub(n),o.isub(s)):(n.isub(t),s.isub(o))}return i=0===t.cmpn(1)?o:s,i.cmpn(0)<0&&i.iadd(e),i},a.prototype.gcd=function(e){if(this.isZero())return e.abs();if(e.isZero())return this.abs();var t=this.clone(),n=e.clone();t.negative=0,n.negative=0;for(var r=0;t.isEven()&&n.isEven();r++)t.iushrn(1),n.iushrn(1);do{while(t.isEven())t.iushrn(1);while(n.isEven())n.iushrn(1);var i=t.cmp(n);if(i<0){var a=t;t=n,n=a}else if(0===i||0===n.cmpn(1))break;t.isub(n)}while(1);return n.iushln(r)},a.prototype.invm=function(e){return this.egcd(e).a.umod(e)},a.prototype.isEven=function(){return 0===(1&this.words[0])},a.prototype.isOdd=function(){return 1===(1&this.words[0])},a.prototype.andln=function(e){return this.words[0]&e},a.prototype.bincn=function(e){r("number"===typeof e);var t=e%26,n=(e-t)/26,i=1<>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(e){var t,n=e<0;if(0!==this.negative&&!n)return-1;if(0===this.negative&&n)return 1;if(this.strip(),this.length>1)t=1;else{n&&(e=-e),r(e<=67108863,"Number is too big");var i=0|this.words[0];t=i===e?0:ie.length)return 1;if(this.length=0;n--){var r=0|this.words[n],i=0|e.words[n];if(r!==i){ri&&(t=1);break}}return t},a.prototype.gtn=function(e){return 1===this.cmpn(e)},a.prototype.gt=function(e){return 1===this.cmp(e)},a.prototype.gten=function(e){return this.cmpn(e)>=0},a.prototype.gte=function(e){return this.cmp(e)>=0},a.prototype.ltn=function(e){return-1===this.cmpn(e)},a.prototype.lt=function(e){return-1===this.cmp(e)},a.prototype.lten=function(e){return this.cmpn(e)<=0},a.prototype.lte=function(e){return this.cmp(e)<=0},a.prototype.eqn=function(e){return 0===this.cmpn(e)},a.prototype.eq=function(e){return 0===this.cmp(e)},a.red=function(e){return new S(e)},a.prototype.toRed=function(e){return r(!this.red,"Already a number in reduction context"),r(0===this.negative,"red works only with positives"),e.convertTo(this)._forceRed(e)},a.prototype.fromRed=function(){return r(this.red,"fromRed works only with numbers in reduction context"),this.red.convertFrom(this)},a.prototype._forceRed=function(e){return this.red=e,this},a.prototype.forceRed=function(e){return r(!this.red,"Already a number in reduction context"),this._forceRed(e)},a.prototype.redAdd=function(e){return r(this.red,"redAdd works only with red numbers"),this.red.add(this,e)},a.prototype.redIAdd=function(e){return r(this.red,"redIAdd works only with red numbers"),this.red.iadd(this,e)},a.prototype.redSub=function(e){return r(this.red,"redSub works only with red numbers"),this.red.sub(this,e)},a.prototype.redISub=function(e){return r(this.red,"redISub works only with red numbers"),this.red.isub(this,e)},a.prototype.redShl=function(e){return r(this.red,"redShl works only with red numbers"),this.red.shl(this,e)},a.prototype.redMul=function(e){return r(this.red,"redMul works only with red numbers"),this.red._verify2(this,e),this.red.mul(this,e)},a.prototype.redIMul=function(e){return r(this.red,"redMul works only with red numbers"),this.red._verify2(this,e),this.red.imul(this,e)},a.prototype.redSqr=function(){return r(this.red,"redSqr works only with red numbers"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return r(this.red,"redISqr works only with red numbers"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return r(this.red,"redSqrt works only with red numbers"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return r(this.red,"redInvm works only with red numbers"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return r(this.red,"redNeg works only with red numbers"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(e){return r(this.red&&!e.red,"redPow(normalNum)"),this.red._verify1(this),this.red.pow(this,e)};var b={k256:null,p224:null,p192:null,p25519:null};function y(e,t){this.name=e,this.p=new a(t,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function _(){y.call(this,"k256","ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f")}function w(){y.call(this,"p224","ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001")}function M(){y.call(this,"p192","ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff")}function x(){y.call(this,"25519","7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed")}function S(e){if("string"===typeof e){var t=a._prime(e);this.m=t.p,this.prime=t}else r(e.gtn(1),"modulus must be greater than 1"),this.m=e,this.prime=null}function k(e){S.call(this,e),this.shift=this.m.bitLength(),this.shift%26!==0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}y.prototype._tmp=function(){var e=new a(null);return e.words=new Array(Math.ceil(this.n/13)),e},y.prototype.ireduce=function(e){var t,n=e;do{this.split(n,this.tmp),n=this.imulK(n),n=n.iadd(this.tmp),t=n.bitLength()}while(t>this.n);var r=t0?n.isub(this.p):void 0!==n.strip?n.strip():n._strip(),n},y.prototype.split=function(e,t){e.iushrn(this.n,0,t)},y.prototype.imulK=function(e){return e.imul(this.k)},i(_,y),_.prototype.split=function(e,t){for(var n=4194303,r=Math.min(e.length,9),i=0;i>>22,a=o}a>>>=22,e.words[i-10]=a,0===a&&e.length>10?e.length-=10:e.length-=9},_.prototype.imulK=function(e){e.words[e.length]=0,e.words[e.length+1]=0,e.length+=2;for(var t=0,n=0;n>>=26,e.words[n]=i,t=r}return 0!==t&&(e.words[e.length++]=t),e},a._prime=function(e){if(b[e])return b[e];var t;if("k256"===e)t=new _;else if("p224"===e)t=new w;else if("p192"===e)t=new M;else{if("p25519"!==e)throw new Error("Unknown prime "+e);t=new x}return b[e]=t,t},S.prototype._verify1=function(e){r(0===e.negative,"red works only with positives"),r(e.red,"red works only with red numbers")},S.prototype._verify2=function(e,t){r(0===(e.negative|t.negative),"red 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e=this,t=this.getValidFieldsName();return t.filter((function(t){return!e.fields[t]})).map((function(t){return{name:t,dirty:!1,value:e.getFieldMeta(t).initialValue}})).reduce((function(e,t){return z()(e,t.name,R(t))}),{})}},{key:"getNestedAllFields",value:function(){var e=this;return Object.keys(this.fields).reduce((function(t,n){return z()(t,n,R(e.fields[n]))}),this.getNotCollectedFields())}},{key:"getFieldMember",value:function(e,t){return this.getField(e)[t]}},{key:"getNestedFields",value:function(e,t){var n=e||this.getValidFieldsName();return n.reduce((function(e,n){return z()(e,n,t(n))}),{})}},{key:"getNestedField",value:function(e,t){var n=this.getValidFieldsFullName(e);if(0===n.length||1===n.length&&n[0]===e)return t(e);var r="["===n[0][e.length],i=r?e.length:e.length+1;return n.reduce((function(e,n){return z()(e,n.slice(i),t(n))}),r?[]:{})}},{key:"isValidNestedFieldName",value:function(e){var t=this.getAllFieldsName();return 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i=this,a=t.fieldNames,o=t.action,c=t.options,l=void 0===c?{}:c,u={},h={},f={},d={};if(e.forEach((function(e){var t=e.name;if(!0===l.force||!1!==e.dirty){var n=i.fieldsStore.getFieldMeta(t),r=s()({},e);r.errors=void 0,r.validating=!0,r.dirty=!0,u[t]=i.getRules(n,o),h[t]=r.value,f[t]=r}else e.errors&&z()(d,t,{errors:e.errors})})),this.setFields(f),Object.keys(h).forEach((function(e){h[e]=i.fieldsStore.getFieldValue(e)})),r&&Q(f))r(Q(d)?null:d,this.fieldsStore.getFieldsValue(a));else{var p=new S["a"](u);n&&p.messages(n),p.validate(h,l,(function(e){var t=s()({},d);e&&e.length&&e.forEach((function(e){var n=e.field,r=n;Object.keys(u).some((function(e){var t=u[e]||[];if(e===n)return r=e,!0;if(t.every((function(e){var t=e.type;return"array"!==t}))&&0!==n.indexOf(e))return!1;var i=n.slice(e.length+1);return!!/^\d+$/.test(i)&&(r=e,!0)}));var i=T()(t,r);("object"!==("undefined"===typeof i?"undefined":y()(i))||Array.isArray(i))&&z()(t,r,{errors:[]});var a=T()(t,r.concat(".errors"));a.push(e)}));var n=[],o={};Object.keys(u).forEach((function(e){var r=T()(t,e),a=i.fieldsStore.getField(e);j()(a.value,h[e])?(a.errors=r&&r.errors,a.value=h[e],a.validating=!1,a.dirty=!1,o[e]=a):n.push({name:e})})),i.setFields(o),r&&(n.length&&n.forEach((function(e){var n=e.name,r=[{message:n+" need to revalidate",field:n}];z()(t,n,{expired:!0,errors:r})})),r(Q(t)?null:t,i.fieldsStore.getFieldsValue(a)))}))}},validateFields:function(e,t,n){var r=this,i=new Promise((function(i,a){var o=Z(e,t,n),s=o.names,c=o.options,l=Z(e,t,n),u=l.callback;if(!u||"function"===typeof u){var h=u;u=function(e,t){h?h(e,t):e?a({errors:e,values:t}):i(t)}}var f=s?r.fieldsStore.getValidFieldsFullName(s):r.fieldsStore.getValidFieldsName(),d=f.filter((function(e){var t=r.fieldsStore.getFieldMeta(e);return ee(t.validate)})).map((function(e){var t=r.fieldsStore.getField(e);return t.value=r.fieldsStore.getFieldValue(e),t}));d.length?("firstFields"in c||(c.firstFields=f.filter((function(e){var t=r.fieldsStore.getFieldMeta(e);return!!t.validateFirst}))),r.validateFieldsInternal(d,{fieldNames:f,options:c},u)):u(null,r.fieldsStore.getFieldsValue(f))}));return i["catch"]((function(e){return console.error,e})),i},isSubmitting:function(){return this.submitting},submit:function(e){var t=this;var n=function(){t.setState({submitting:!1})};this.setState({submitting:!0}),e(n)}},render:function(){var t=arguments[0],n=this.$slots,r=this.$scopedSlots,i=a()({},m,this.getForm()),o=Object(le["k"])(this),c=o.wrappedComponentRef,u=w()(o,["wrappedComponentRef"]),h={props:l.call(this,s()({},i,u)),on:Object(le["j"])(this),ref:"WrappedComponent",directives:[{name:"ant-ref",value:c}]};Object.keys(r).length&&(h.scopedSlots=r);var f=Object.keys(n);return e?t(e,h,[f.length?f.map((function(e){return t("template",{slot:e},[n[e]])})):null]):null}};if(!e)return p;if(Array.isArray(e.props)){var g={};e.props.forEach((function(e){g[e]=c["a"].any})),g[m]=Object,e.props=g}else e.props=e.props||{},m in e.props||(e.props[m]=Object);return B(p,e)}}var fe=he,de={methods:{getForm:function(){return{getFieldsValue:this.fieldsStore.getFieldsValue,getFieldValue:this.fieldsStore.getFieldValue,getFieldInstance:this.getFieldInstance,setFieldsValue:this.setFieldsValue,setFields:this.setFields,setFieldsInitialValue:this.fieldsStore.setFieldsInitialValue,getFieldDecorator:this.getFieldDecorator,getFieldProps:this.getFieldProps,getFieldsError:this.fieldsStore.getFieldsError,getFieldError:this.fieldsStore.getFieldError,isFieldValidating:this.fieldsStore.isFieldValidating,isFieldsValidating:this.fieldsStore.isFieldsValidating,isFieldsTouched:this.fieldsStore.isFieldsTouched,isFieldTouched:this.fieldsStore.isFieldTouched,isSubmitting:this.isSubmitting,submit:this.submit,validateFields:this.validateFields,resetFields:this.resetFields}}}};function pe(e,t){var n=window.getComputedStyle,r=n?n(e):e.currentStyle;if(r)return r[t.replace(/-(\w)/gi,(function(e,t){return t.toUpperCase()}))]}function me(e){var t=e,n=void 0;while("body"!==(n=t.nodeName.toLowerCase())){var r=pe(t,"overflowY");if(t!==e&&("auto"===r||"scroll"===r)&&t.scrollHeight>t.clientHeight)return t;t=t.parentNode}return"body"===n?t.ownerDocument:t}var ve={methods:{getForm:function(){return s()({},de.methods.getForm.call(this),{validateFieldsAndScroll:this.validateFieldsAndScroll})},validateFieldsAndScroll:function(e,t,n){var r=this,i=Z(e,t,n),a=i.names,o=i.callback,c=i.options,l=function(e,t){if(e){var n=r.fieldsStore.getValidFieldsName(),i=void 0,a=void 0;if(n.forEach((function(t){if(g()(e,t)){var n=r.getFieldInstance(t);if(n){var o=n.$el||n.elm,s=o.getBoundingClientRect().top;"hidden"!==o.type&&(void 0===a||a>s)&&(a=s,i=o)}}})),i){var l=c.container||me(i);Object(m["a"])(i,l,s()({onlyScrollIfNeeded:!0},c.scroll))}}"function"===typeof o&&o(e,t)};return this.validateFields(a,c,l)}}};function ge(e){return fe(s()({},e),[ve])}var be=ge,ye=n("92fa"),_e=n.n(ye),we=n("2769"),Me=n.n(we),xe=n("290c"),Se="data-__meta",ke="data-__field",Ce=n("94eb"),Le=n("0c63"),Te=n("4df5");function Oe(){}function ze(e){return e.reduce((function(e,t){return[].concat(x()(e),[" ",t])}),[]).slice(1)}var De={id:c["a"].string,htmlFor:c["a"].string,prefixCls:c["a"].string,label:c["a"].any,labelCol:c["a"].shape(h["a"]).loose,wrapperCol:c["a"].shape(h["a"]).loose,help:c["a"].any,extra:c["a"].any,validateStatus:c["a"].oneOf(["","success","warning","error","validating"]),hasFeedback:c["a"].bool,required:c["a"].bool,colon:c["a"].bool,fieldDecoratorId:c["a"].string,fieldDecoratorOptions:c["a"].object,selfUpdate:c["a"].bool,labelAlign:c["a"].oneOf(["left","right"])};function je(){for(var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments[1],n=!1,r=0,i=e.length;r0&&void 0!==arguments[0]?arguments[0]:[],t=arguments[1],n=[],r=0;r0)break;var 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t=this.$el,n=t.querySelector('[id="'+e+'"]');n&&n.focus&&n.focus()}},onHelpAnimEnd:function(e,t){this.helpShow=t,t||this.$forceUpdate()},isRequired:function(){var e=this.required;if(void 0!==e)return e;if(this.getOnlyControl()){var t=this.getMeta()||{},n=t.validate||[];return n.filter((function(e){return!!e.rules})).some((function(e){return e.rules.some((function(e){return e.required}))}))}return!1},renderHelp:function(e){var t=this,n=this.$createElement,r=this.getHelpMessage(),i=r?n("div",{class:e+"-explain",key:"help"},[r]):null;i&&(this.helpShow=!!i);var a=Object(Ce["a"])("show-help",{afterEnter:function(){return t.onHelpAnimEnd("help",!0)},afterLeave:function(){return t.onHelpAnimEnd("help",!1)}});return n("transition",_e()([a,{key:"help"}]),[i])},renderExtra:function(e){var t=this.$createElement,n=Object(le["f"])(this,"extra");return n?t("div",{class:e+"-extra"},[n]):null},renderValidateWrapper:function(e,t,n,r){var i=this.$createElement,a=this.$props,o=this.getOnlyControl,s=void 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n(h["b"],p,[t])},renderLabel:function(e){var t,n=this.$createElement,r=this.FormContext,i=r.vertical,o=r.labelAlign,s=r.labelCol,c=r.colon,l=this.labelAlign,f=this.labelCol,d=this.colon,p=this.id,m=this.htmlFor,v=Object(le["f"])(this,"label"),g=this.isRequired(),b=f||s||{},y=l||o,_=e+"-item-label",M=u()(_,"left"===y&&_+"-left",b["class"]),x=(b["class"],b.style),S=b.id,k=b.on,C=w()(b,["class","style","id","on"]),L=v,T=!0===d||!1!==c&&!1!==d,O=T&&!i;O&&"string"===typeof v&&""!==v.trim()&&(L=v.replace(/[：:]\s*$/,""));var z=u()((t={},a()(t,e+"-item-required",g),a()(t,e+"-item-no-colon",!T),t)),D={props:C,class:M,key:"label",style:x,id:S,on:k};return v?n(h["b"],D,[n("label",{attrs:{for:m||p||this.getId(),title:"string"===typeof v?v:""},class:z,on:{click:this.onLabelClick}},[L])]):null},renderChildren:function(e){return[this.renderLabel(e),this.renderWrapper(e,this.renderValidateWrapper(e,this.slotDefault,this.renderHelp(e),this.renderExtra(e)))]},renderFormItem:function(){var 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r=n("23e7"),i=Math.floor,a=Math.log,o=Math.LOG2E;r({target:"Math",stat:!0},{clz32:function(e){return(e>>>=0)?31-i(a(e+.5)*o):32}})},4111:function(e,t,n){"use strict";var r=n("7f7a");t.certificate=n("56b5");var i=r.define("RSAPrivateKey",(function(){this.seq().obj(this.key("version").int(),this.key("modulus").int(),this.key("publicExponent").int(),this.key("privateExponent").int(),this.key("prime1").int(),this.key("prime2").int(),this.key("exponent1").int(),this.key("exponent2").int(),this.key("coefficient").int())}));t.RSAPrivateKey=i;var a=r.define("RSAPublicKey",(function(){this.seq().obj(this.key("modulus").int(),this.key("publicExponent").int())}));t.RSAPublicKey=a;var o=r.define("SubjectPublicKeyInfo",(function(){this.seq().obj(this.key("algorithm").use(s),this.key("subjectPublicKey").bitstr())}));t.PublicKey=o;var s=r.define("AlgorithmIdentifier",(function(){this.seq().obj(this.key("algorithm").objid(),this.key("none").null_().optional(),this.key("curve").objid().optional(),this.key("params").seq().obj(this.key("p").int(),this.key("q").int(),this.key("g").int()).optional())})),c=r.define("PrivateKeyInfo",(function(){this.seq().obj(this.key("version").int(),this.key("algorithm").use(s),this.key("subjectPrivateKey").octstr())}));t.PrivateKey=c;var l=r.define("EncryptedPrivateKeyInfo",(function(){this.seq().obj(this.key("algorithm").seq().obj(this.key("id").objid(),this.key("decrypt").seq().obj(this.key("kde").seq().obj(this.key("id").objid(),this.key("kdeparams").seq().obj(this.key("salt").octstr(),this.key("iters").int())),this.key("cipher").seq().obj(this.key("algo").objid(),this.key("iv").octstr()))),this.key("subjectPrivateKey").octstr())}));t.EncryptedPrivateKey=l;var u=r.define("DSAPrivateKey",(function(){this.seq().obj(this.key("version").int(),this.key("p").int(),this.key("q").int(),this.key("g").int(),this.key("pub_key").int(),this.key("priv_key").int())}));t.DSAPrivateKey=u,t.DSAparam=r.define("DSAparam",(function(){this.int()}));var h=r.define("ECPrivateKey",(function(){this.seq().obj(this.key("version").int(),this.key("privateKey").octstr(),this.key("parameters").optional().explicit(0).use(f),this.key("publicKey").optional().explicit(1).bitstr())}));t.ECPrivateKey=h;var f=r.define("ECParameters",(function(){this.choice({namedCurve:this.objid()})}));t.signature=r.define("signature",(function(){this.seq().obj(this.key("r").int(),this.key("s").int())}))},4127:function(e,t,n){"use strict";var r=n("d233"),i=n("b313"),a=Object.prototype.hasOwnProperty,o={brackets:function(e){return e+"[]"},comma:"comma",indices:function(e,t){return e+"["+t+"]"},repeat:function(e){return 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this.mouseUpHandler},cursorStyleHandler:function(e,t){return t.cursorStyle},getActionName:function(e,t){return t.actionName},getVisibility:function(e,t){var n=e._controlsVisibility;return n&&"undefined"!==typeof n[t]?n[t]:this.visible},setVisibility:function(e){this.visible=e},positionHandler:function(e,n){var r=t.util.transformPoint({x:this.x*e.x+this.offsetX,y:this.y*e.y+this.offsetY},n);return r},calcCornerCoords:function(e,n,r,i,a){var o,s,c,l,u=a?this.touchSizeX:this.sizeX,h=a?this.touchSizeY:this.sizeY;if(u&&h&&u!==h){var f=Math.atan2(h,u),d=Math.sqrt(u*u+h*h)/2,p=f-t.util.degreesToRadians(e),m=Math.PI/2-f-t.util.degreesToRadians(e);o=d*t.util.cos(p),s=d*t.util.sin(p),c=d*t.util.cos(m),l=d*t.util.sin(m)}else{var v=u&&h?u:n;d=.7071067812*v;p=t.util.degreesToRadians(45-e);o=c=d*t.util.cos(p),s=l=d*t.util.sin(p)}return{tl:{x:r-l,y:i-c},tr:{x:r+o,y:i-s},bl:{x:r-o,y:i+s},br:{x:r+l,y:i+c}}},render:function(e,n,r,i,a){switch(i=i||{},i.cornerStyle||a.cornerStyle){case"circle":t.controlsUtils.renderCircleControl.call(this,e,n,r,i,a);break;default:t.controlsUtils.renderSquareControl.call(this,e,n,r,i,a)}}}}(t),function(){function e(e,t){var n,i,a,o,s=e.getAttribute("style"),c=e.getAttribute("offset")||0;if(c=parseFloat(c)/(/%$/.test(c)?100:1),c=c<0?0:c>1?1:c,s){var l=s.split(/\s*;\s*/);for(""===l[l.length-1]&&l.pop(),o=l.length;o--;){var u=l[o].split(/\s*:\s*/),h=u[0].trim(),f=u[1].trim();"stop-color"===h?n=f:"stop-opacity"===h&&(a=f)}}return n||(n=e.getAttribute("stop-color")||"rgb(0,0,0)"),a||(a=e.getAttribute("stop-opacity")),n=new r.Color(n),i=n.getAlpha(),a=isNaN(parseFloat(a))?1:parseFloat(a),a*=i*t,{offset:c,color:n.toRgb(),opacity:a}}function t(e){return{x1:e.getAttribute("x1")||0,y1:e.getAttribute("y1")||0,x2:e.getAttribute("x2")||"100%",y2:e.getAttribute("y2")||0}}function n(e){return{x1:e.getAttribute("fx")||e.getAttribute("cx")||"50%",y1:e.getAttribute("fy")||e.getAttribute("cy")||"50%",r1:0,x2:e.getAttribute("cx")||"50%",y2:e.getAttribute("cy")||"50%",r2:e.getAttribute("r")||"50%"}}var i=r.util.object.clone;function a(e,t,n,r){var i,a;Object.keys(t).forEach((function(e){i=t[e],"Infinity"===i?a=1:"-Infinity"===i?a=0:(a=parseFloat(t[e],10),"string"===typeof i&&/^(\d+\.\d+)%|(\d+)%$/.test(i)&&(a*=.01,"pixels"===r&&("x1"!==e&&"x2"!==e&&"r2"!==e||(a*=n.viewBoxWidth||n.width),"y1"!==e&&"y2"!==e||(a*=n.viewBoxHeight||n.height)))),t[e]=a}))}r.Gradient=r.util.createClass({offsetX:0,offsetY:0,gradientTransform:null,gradientUnits:"pixels",type:"linear",initialize:function(e){e||(e={}),e.coords||(e.coords={});var t,n=this;Object.keys(e).forEach((function(t){n[t]=e[t]})),this.id?this.id+="_"+r.Object.__uid++:this.id=r.Object.__uid++,t={x1:e.coords.x1||0,y1:e.coords.y1||0,x2:e.coords.x2||0,y2:e.coords.y2||0},"radial"===this.type&&(t.r1=e.coords.r1||0,t.r2=e.coords.r2||0),this.coords=t,this.colorStops=e.colorStops.slice()},addColorStop:function(e){for(var t in e){var n=new r.Color(e[t]);this.colorStops.push({offset:parseFloat(t),color:n.toRgb(),opacity:n.getAlpha()})}return this},toObject:function(e){var t={type:this.type,coords:this.coords,colorStops:this.colorStops,offsetX:this.offsetX,offsetY:this.offsetY,gradientUnits:this.gradientUnits,gradientTransform:this.gradientTransform?this.gradientTransform.concat():this.gradientTransform};return r.util.populateWithProperties(this,t,e),t},toSVG:function(e,t){var n,a,o,s,c=i(this.coords,!0),l=(t=t||{},i(this.colorStops,!0)),u=c.r1>c.r2,h=this.gradientTransform?this.gradientTransform.concat():r.iMatrix.concat(),f=-this.offsetX,d=-this.offsetY,p=!!t.additionalTransform,m="pixels"===this.gradientUnits?"userSpaceOnUse":"objectBoundingBox";if(l.sort((function(e,t){return e.offset-t.offset})),"objectBoundingBox"===m?(f/=e.width,d/=e.height):(f+=e.width/2,d+=e.height/2),"path"===e.type&&"percentage"!==this.gradientUnits&&(f-=e.pathOffset.x,d-=e.pathOffset.y),h[4]-=f,h[5]-=d,s='id="SVGID_'+this.id+'" gradientUnits="'+m+'"',s+=' gradientTransform="'+(p?t.additionalTransform+" ":"")+r.util.matrixToSVG(h)+'" ',"linear"===this.type?o=["\n']:"radial"===this.type&&(o=["\n']),"radial"===this.type){if(u)for(l=l.concat(),l.reverse(),n=0,a=l.length;n0){var g=Math.max(c.r1,c.r2),b=v/g;for(n=0,a=l.length;n\n')}return o.push("linear"===this.type?"\n":"\n"),o.join("")},toLive:function(e){var t,n,i,a=r.util.object.clone(this.coords);if(this.type){for("linear"===this.type?t=e.createLinearGradient(a.x1,a.y1,a.x2,a.y2):"radial"===this.type&&(t=e.createRadialGradient(a.x1,a.y1,a.r1,a.x2,a.y2,a.r2)),n=0,i=this.colorStops.length;n1?1:l,isNaN(l)&&(l=1);var u,h,f,d,p=i.getElementsByTagName("stop"),m="userSpaceOnUse"===i.getAttribute("gradientUnits")?"pixels":"percentage",v=i.getAttribute("gradientTransform")||"",g=[],b=0,y=0;for("linearGradient"===i.nodeName||"LINEARGRADIENT"===i.nodeName?(u="linear",h=t(i)):(u="radial",h=n(i)),f=p.length;f--;)g.push(e(p[f],l));d=r.parseTransformAttribute(v),a(o,h,c,m),"pixels"===m&&(b=-o.left,y=-o.top);var _=new r.Gradient({id:i.getAttribute("id"),type:u,coords:h,colorStops:g,gradientUnits:m,gradientTransform:d,offsetX:b,offsetY:y});return _}})}(),function(){"use strict";var e=r.util.toFixed;r.Pattern=r.util.createClass({repeat:"repeat",offsetX:0,offsetY:0,crossOrigin:"",patternTransform:null,initialize:function(e,t){if(e||(e={}),this.id=r.Object.__uid++,this.setOptions(e),!e.source||e.source&&"string"!==typeof e.source)t&&t(this);else{var n=this;this.source=r.util.createImage(),r.util.loadImage(e.source,(function(e,r){n.source=e,t&&t(n,r)}),null,this.crossOrigin)}},toObject:function(t){var n,i,a=r.Object.NUM_FRACTION_DIGITS;return"string"===typeof this.source.src?n=this.source.src:"object"===typeof this.source&&this.source.toDataURL&&(n=this.source.toDataURL()),i={type:"pattern",source:n,repeat:this.repeat,crossOrigin:this.crossOrigin,offsetX:e(this.offsetX,a),offsetY:e(this.offsetY,a),patternTransform:this.patternTransform?this.patternTransform.concat():null},r.util.populateWithProperties(this,i,t),i},toSVG:function(e){var t="function"===typeof this.source?this.source():this.source,n=t.width/e.width,r=t.height/e.height,i=this.offsetX/e.width,a=this.offsetY/e.height,o="";return"repeat-x"!==this.repeat&&"no-repeat"!==this.repeat||(r=1,a&&(r+=Math.abs(a))),"repeat-y"!==this.repeat&&"no-repeat"!==this.repeat||(n=1,i&&(n+=Math.abs(i))),t.src?o=t.src:t.toDataURL&&(o=t.toDataURL()),'\n\n\n'},setOptions:function(e){for(var t in e)this[t]=e[t]},toLive:function(e){var t=this.source;if(!t)return"";if("undefined"!==typeof t.src){if(!t.complete)return"";if(0===t.naturalWidth||0===t.naturalHeight)return""}return e.createPattern(t,this.repeat)}})}(),function(e){"use strict";var t=e.fabric||(e.fabric={}),n=t.util.toFixed;t.Shadow?t.warn("fabric.Shadow is already defined."):(t.Shadow=t.util.createClass({color:"rgb(0,0,0)",blur:0,offsetX:0,offsetY:0,affectStroke:!1,includeDefaultValues:!0,nonScaling:!1,initialize:function(e){for(var n in"string"===typeof e&&(e=this._parseShadow(e)),e)this[n]=e[n];this.id=t.Object.__uid++},_parseShadow:function(e){var n=e.trim(),r=t.Shadow.reOffsetsAndBlur.exec(n)||[],i=n.replace(t.Shadow.reOffsetsAndBlur,"")||"rgb(0,0,0)";return{color:i.trim(),offsetX:parseInt(r[1],10)||0,offsetY:parseInt(r[2],10)||0,blur:parseInt(r[3],10)||0}},toString:function(){return[this.offsetX,this.offsetY,this.blur,this.color].join("px ")},toSVG:function(e){var r=40,i=40,a=t.Object.NUM_FRACTION_DIGITS,o=t.util.rotateVector({x:this.offsetX,y:this.offsetY},t.util.degreesToRadians(-e.angle)),s=20,c=new t.Color(this.color);return e.width&&e.height&&(r=100*n((Math.abs(o.x)+this.blur)/e.width,a)+s,i=100*n((Math.abs(o.y)+this.blur)/e.height,a)+s),e.flipX&&(o.x*=-1),e.flipY&&(o.y*=-1),'\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\n\n'},toObject:function(){if(this.includeDefaultValues)return{color:this.color,blur:this.blur,offsetX:this.offsetX,offsetY:this.offsetY,affectStroke:this.affectStroke,nonScaling:this.nonScaling};var e={},n=t.Shadow.prototype;return["color","blur","offsetX","offsetY","affectStroke","nonScaling"].forEach((function(t){this[t]!==n[t]&&(e[t]=this[t])}),this),e}}),t.Shadow.reOffsetsAndBlur=/(?:\s|^)(-?\d+(?:px)?(?:\s?|$))?(-?\d+(?:px)?(?:\s?|$))?(\d+(?:px)?)?(?:\s?|$)(?:$|\s)/)}(t),function(){"use strict";if(r.StaticCanvas)r.warn("fabric.StaticCanvas is already defined.");else{var e=r.util.object.extend,t=r.util.getElementOffset,n=r.util.removeFromArray,i=r.util.toFixed,a=r.util.transformPoint,o=r.util.invertTransform,s=r.util.getNodeCanvas,c=r.util.createCanvasElement,l=new Error("Could not initialize `canvas` element");r.StaticCanvas=r.util.createClass(r.CommonMethods,{initialize:function(e,t){t||(t={}),this.renderAndResetBound=this.renderAndReset.bind(this),this.requestRenderAllBound=this.requestRenderAll.bind(this),this._initStatic(e,t)},backgroundColor:"",backgroundImage:null,overlayColor:"",overlayImage:null,includeDefaultValues:!0,stateful:!1,renderOnAddRemove:!0,controlsAboveOverlay:!1,allowTouchScrolling:!1,imageSmoothingEnabled:!0,viewportTransform:r.iMatrix.concat(),backgroundVpt:!0,overlayVpt:!0,enableRetinaScaling:!0,vptCoords:{},skipOffscreen:!0,clipPath:void 0,_initStatic:function(e,t){var n=this.requestRenderAllBound;this._objects=[],this._createLowerCanvas(e),this._initOptions(t),this.interactive||this._initRetinaScaling(),t.overlayImage&&this.setOverlayImage(t.overlayImage,n),t.backgroundImage&&this.setBackgroundImage(t.backgroundImage,n),t.backgroundColor&&this.setBackgroundColor(t.backgroundColor,n),t.overlayColor&&this.setOverlayColor(t.overlayColor,n),this.calcOffset()},_isRetinaScaling:function(){return 1!==r.devicePixelRatio&&this.enableRetinaScaling},getRetinaScaling:function(){return this._isRetinaScaling()?r.devicePixelRatio:1},_initRetinaScaling:function(){if(this._isRetinaScaling()){var e=r.devicePixelRatio;this.__initRetinaScaling(e,this.lowerCanvasEl,this.contextContainer),this.upperCanvasEl&&this.__initRetinaScaling(e,this.upperCanvasEl,this.contextTop)}},__initRetinaScaling:function(e,t,n){t.setAttribute("width",this.width*e),t.setAttribute("height",this.height*e),n.scale(e,e)},calcOffset:function(){return this._offset=t(this.lowerCanvasEl),this},setOverlayImage:function(e,t,n){return this.__setBgOverlayImage("overlayImage",e,t,n)},setBackgroundImage:function(e,t,n){return this.__setBgOverlayImage("backgroundImage",e,t,n)},setOverlayColor:function(e,t){return this.__setBgOverlayColor("overlayColor",e,t)},setBackgroundColor:function(e,t){return this.__setBgOverlayColor("backgroundColor",e,t)},__setBgOverlayImage:function(e,t,n,i){return"string"===typeof t?r.util.loadImage(t,(function(t,a){if(t){var o=new r.Image(t,i);this[e]=o,o.canvas=this}n&&n(t,a)}),this,i&&i.crossOrigin):(i&&t.setOptions(i),this[e]=t,t&&(t.canvas=this),n&&n(t,!1)),this},__setBgOverlayColor:function(e,t,n){return this[e]=t,this._initGradient(t,e),this._initPattern(t,e,n),this},_createCanvasElement:function(){var e=c();if(!e)throw l;if(e.style||(e.style={}),"undefined"===typeof e.getContext)throw l;return e},_initOptions:function(e){var t=this.lowerCanvasEl;this._setOptions(e),this.width=this.width||parseInt(t.width,10)||0,this.height=this.height||parseInt(t.height,10)||0,this.lowerCanvasEl.style&&(t.width=this.width,t.height=this.height,t.style.width=this.width+"px",t.style.height=this.height+"px",this.viewportTransform=this.viewportTransform.slice())},_createLowerCanvas:function(e){e&&e.getContext?this.lowerCanvasEl=e:this.lowerCanvasEl=r.util.getById(e)||this._createCanvasElement(),r.util.addClass(this.lowerCanvasEl,"lower-canvas"),this.interactive&&this._applyCanvasStyle(this.lowerCanvasEl),this.contextContainer=this.lowerCanvasEl.getContext("2d")},getWidth:function(){return this.width},getHeight:function(){return this.height},setWidth:function(e,t){return this.setDimensions({width:e},t)},setHeight:function(e,t){return this.setDimensions({height:e},t)},setDimensions:function(e,t){var n;for(var r in t=t||{},e)n=e[r],t.cssOnly||(this._setBackstoreDimension(r,e[r]),n+="px",this.hasLostContext=!0),t.backstoreOnly||this._setCssDimension(r,n);return this._isCurrentlyDrawing&&this.freeDrawingBrush&&this.freeDrawingBrush._setBrushStyles(),this._initRetinaScaling(),this.calcOffset(),t.cssOnly||this.requestRenderAll(),this},_setBackstoreDimension:function(e,t){return this.lowerCanvasEl[e]=t,this.upperCanvasEl&&(this.upperCanvasEl[e]=t),this.cacheCanvasEl&&(this.cacheCanvasEl[e]=t),this[e]=t,this},_setCssDimension:function(e,t){return this.lowerCanvasEl.style[e]=t,this.upperCanvasEl&&(this.upperCanvasEl.style[e]=t),this.wrapperEl&&(this.wrapperEl.style[e]=t),this},getZoom:function(){return this.viewportTransform[0]},setViewportTransform:function(e){var t,n,r,i=this._activeObject;for(this.viewportTransform=e,n=0,r=this._objects.length;n\n'),this._setSVGBgOverlayColor(n,"background"),this._setSVGBgOverlayImage(n,"backgroundImage",t),this._setSVGObjects(n,t),this.clipPath&&n.push("\n"),this._setSVGBgOverlayColor(n,"overlay"),this._setSVGBgOverlayImage(n,"overlayImage",t),n.push(""),n.join("")},_setSVGPreamble:function(e,t){t.suppressPreamble||e.push('\n','\n')},_setSVGHeader:function(e,t){var n,a=t.width||this.width,o=t.height||this.height,s='viewBox="0 0 '+this.width+" "+this.height+'" ',c=r.Object.NUM_FRACTION_DIGITS;t.viewBox?s='viewBox="'+t.viewBox.x+" "+t.viewBox.y+" "+t.viewBox.width+" "+t.viewBox.height+'" ':this.svgViewportTransformation&&(n=this.viewportTransform,s='viewBox="'+i(-n[4]/n[0],c)+" "+i(-n[5]/n[3],c)+" "+i(this.width/n[0],c)+" "+i(this.height/n[3],c)+'" '),e.push("\n',"Created with Fabric.js ",r.version,"\n","\n",this.createSVGFontFacesMarkup(),this.createSVGRefElementsMarkup(),this.createSVGClipPathMarkup(t),"\n")},createSVGClipPathMarkup:function(e){var t=this.clipPath;return t?(t.clipPathId="CLIPPATH_"+r.Object.__uid++,'\n'+this.clipPath.toClipPathSVG(e.reviver)+"\n"):""},createSVGRefElementsMarkup:function(){var e=this,t=["background","overlay"].map((function(t){var n=e[t+"Color"];if(n&&n.toLive){var i=e[t+"Vpt"],a=e.viewportTransform,o={width:e.width/(i?a[0]:1),height:e.height/(i?a[3]:1)};return n.toSVG(o,{additionalTransform:i?r.util.matrixToSVG(a):""})}}));return t.join("")},createSVGFontFacesMarkup:function(){var e,t,n,i,a,o,s,c,l,u="",h={},f=r.fontPaths,d=[];for(this._objects.forEach((function e(t){d.push(t),t._objects&&t._objects.forEach(e)})),c=0,l=d.length;c',"\n",u,"","\n"].join("")),u},_setSVGObjects:function(e,t){var n,r,i,a=this._objects;for(r=0,i=a.length;r\n")}else e.push('\n")},sendToBack:function(e){if(!e)return this;var t,r,i,a=this._activeObject;if(e===a&&"activeSelection"===e.type)for(i=a._objects,t=i.length;t--;)r=i[t],n(this._objects,r),this._objects.unshift(r);else n(this._objects,e),this._objects.unshift(e);return this.renderOnAddRemove&&this.requestRenderAll(),this},bringToFront:function(e){if(!e)return this;var t,r,i,a=this._activeObject;if(e===a&&"activeSelection"===e.type)for(i=a._objects,t=0;t0+l&&(o=a-1,n(this._objects,i),this._objects.splice(o,0,i)),l++;else a=this._objects.indexOf(e),0!==a&&(o=this._findNewLowerIndex(e,a,t),n(this._objects,e),this._objects.splice(o,0,e));return this.renderOnAddRemove&&this.requestRenderAll(),this},_findNewLowerIndex:function(e,t,n){var r,i;if(n)for(r=t,i=t-1;i>=0;--i){var a=e.intersectsWithObject(this._objects[i])||e.isContainedWithinObject(this._objects[i])||this._objects[i].isContainedWithinObject(e);if(a){r=i;break}}else r=t-1;return r},bringForward:function(e,t){if(!e)return this;var r,i,a,o,s,c=this._activeObject,l=0;if(e===c&&"activeSelection"===e.type)for(s=c._objects,r=s.length;r--;)i=s[r],a=this._objects.indexOf(i),a"}}),e(r.StaticCanvas.prototype,r.Observable),e(r.StaticCanvas.prototype,r.Collection),e(r.StaticCanvas.prototype,r.DataURLExporter),e(r.StaticCanvas,{EMPTY_JSON:'{"objects": [], "background": "white"}',supports:function(e){var t=c();if(!t||!t.getContext)return null;var n=t.getContext("2d");if(!n)return null;switch(e){case"setLineDash":return"undefined"!==typeof n.setLineDash;default:return null}}}),r.StaticCanvas.prototype.toJSON=r.StaticCanvas.prototype.toObject,r.isLikelyNode&&(r.StaticCanvas.prototype.createPNGStream=function(){var e=s(this.lowerCanvasEl);return e&&e.createPNGStream()},r.StaticCanvas.prototype.createJPEGStream=function(e){var t=s(this.lowerCanvasEl);return t&&t.createJPEGStream(e)})}}(),r.BaseBrush=r.util.createClass({color:"rgb(0, 0, 0)",width:1,shadow:null,strokeLineCap:"round",strokeLineJoin:"round",strokeMiterLimit:10,strokeDashArray:null,_setBrushStyles:function(){var e=this.canvas.contextTop;e.strokeStyle=this.color,e.lineWidth=this.width,e.lineCap=this.strokeLineCap,e.miterLimit=this.strokeMiterLimit,e.lineJoin=this.strokeLineJoin,r.StaticCanvas.supports("setLineDash")&&e.setLineDash(this.strokeDashArray||[])},_saveAndTransform:function(e){var t=this.canvas.viewportTransform;e.save(),e.transform(t[0],t[1],t[2],t[3],t[4],t[5])},_setShadow:function(){if(this.shadow){var e=this.canvas,t=this.shadow,n=e.contextTop,i=e.getZoom();e&&e._isRetinaScaling()&&(i*=r.devicePixelRatio),n.shadowColor=t.color,n.shadowBlur=t.blur*i,n.shadowOffsetX=t.offsetX*i,n.shadowOffsetY=t.offsetY*i}},needsFullRender:function(){var e=new r.Color(this.color);return e.getAlpha()<1||!!this.shadow},_resetShadow:function(){var e=this.canvas.contextTop;e.shadowColor="",e.shadowBlur=e.shadowOffsetX=e.shadowOffsetY=0}}),function(){r.PencilBrush=r.util.createClass(r.BaseBrush,{decimate:.4,initialize:function(e){this.canvas=e,this._points=[]},_drawSegment:function(e,t,n){var r=t.midPointFrom(n);return e.quadraticCurveTo(t.x,t.y,r.x,r.y),r},onMouseDown:function(e,t){this.canvas._isMainEvent(t.e)&&(this._prepareForDrawing(e),this._captureDrawingPath(e),this._render())},onMouseMove:function(e,t){if(this.canvas._isMainEvent(t.e)&&this._captureDrawingPath(e)&&this._points.length>1)if(this.needsFullRender())this.canvas.clearContext(this.canvas.contextTop),this._render();else{var n=this._points,r=n.length,i=this.canvas.contextTop;this._saveAndTransform(i),this.oldEnd&&(i.beginPath(),i.moveTo(this.oldEnd.x,this.oldEnd.y)),this.oldEnd=this._drawSegment(i,n[r-2],n[r-1],!0),i.stroke(),i.restore()}},onMouseUp:function(e){return!this.canvas._isMainEvent(e.e)||(this.oldEnd=void 0,this._finalizeAndAddPath(),!1)},_prepareForDrawing:function(e){var t=new r.Point(e.x,e.y);this._reset(),this._addPoint(t),this.canvas.contextTop.moveTo(t.x,t.y)},_addPoint:function(e){return!(this._points.length>1&&e.eq(this._points[this._points.length-1]))&&(this._points.push(e),!0)},_reset:function(){this._points=[],this._setBrushStyles(),this._setShadow()},_captureDrawingPath:function(e){var t=new r.Point(e.x,e.y);return this._addPoint(t)},_render:function(){var e,t,n=this.canvas.contextTop,i=this._points[0],a=this._points[1];if(this._saveAndTransform(n),n.beginPath(),2===this._points.length&&i.x===a.x&&i.y===a.y){var o=this.width/1e3;i=new r.Point(i.x,i.y),a=new r.Point(a.x,a.y),i.x-=o,a.x+=o}for(n.moveTo(i.x,i.y),e=1,t=this._points.length;e2;for(u&&(c=e[2].xe[t-2].x?1:a.x===e[t-2].x?0:-1,l=a.y>e[t-2].y?1:a.y===e[t-2].y?0:-1),n.push("L ",a.x+c*i," ",a.y+l*i),n},createPath:function(e){var t=new r.Path(e,{fill:null,stroke:this.color,strokeWidth:this.width,strokeLineCap:this.strokeLineCap,strokeMiterLimit:this.strokeMiterLimit,strokeLineJoin:this.strokeLineJoin,strokeDashArray:this.strokeDashArray});return this.shadow&&(this.shadow.affectStroke=!0,t.shadow=new r.Shadow(this.shadow)),t},decimatePoints:function(e,t){if(e.length<=2)return e;var n,i,a=this.canvas.getZoom(),o=Math.pow(t/a,2),s=e.length-1,c=e[0],l=[c];for(n=1;n=o&&(c=e[n],l.push(c));return 1===l.length&&l.push(new r.Point(l[0].x,l[0].y)),l},_finalizeAndAddPath:function(){var e=this.canvas.contextTop;e.closePath(),this.decimate&&(this._points=this.decimatePoints(this._points,this.decimate));var t=this.convertPointsToSVGPath(this._points).join("");if("M 0 0 Q 0 0 0 0 L 0 0"!==t){var n=this.createPath(t);this.canvas.clearContext(this.canvas.contextTop),this.canvas.fire("before:path:created",{path:n}),this.canvas.add(n),this.canvas.requestRenderAll(),n.setCoords(),this._resetShadow(),this.canvas.fire("path:created",{path:n})}else this.canvas.requestRenderAll()}})}(),r.CircleBrush=r.util.createClass(r.BaseBrush,{width:10,initialize:function(e){this.canvas=e,this.points=[]},drawDot:function(e){var t=this.addPoint(e),n=this.canvas.contextTop;this._saveAndTransform(n),this.dot(n,t),n.restore()},dot:function(e,t){e.fillStyle=t.fill,e.beginPath(),e.arc(t.x,t.y,t.radius,0,2*Math.PI,!1),e.closePath(),e.fill()},onMouseDown:function(e){this.points.length=0,this.canvas.clearContext(this.canvas.contextTop),this._setShadow(),this.drawDot(e)},_render:function(){var e,t,n=this.canvas.contextTop,r=this.points;for(this._saveAndTransform(n),e=0,t=r.length;e0&&!this.preserveObjectStacking){t=[],n=[];for(var i=0,a=this._objects.length;i1&&(this._activeObject._objects=n),t.push.apply(t,n)}else t=this._objects;return t},renderAll:function(){!this.contextTopDirty||this._groupSelector||this.isDrawingMode||(this.clearContext(this.contextTop),this.contextTopDirty=!1),this.hasLostContext&&this.renderTopLayer(this.contextTop);var e=this.contextContainer;return this.renderCanvas(e,this._chooseObjectsToRender()),this},renderTopLayer:function(e){e.save(),this.isDrawingMode&&this._isCurrentlyDrawing&&(this.freeDrawingBrush&&this.freeDrawingBrush._render(),this.contextTopDirty=!0),this.selection&&this._groupSelector&&(this._drawSelection(e),this.contextTopDirty=!0),e.restore()},renderTop:function(){var e=this.contextTop;return this.clearContext(e),this.renderTopLayer(e),this.fire("after:render"),this},_normalizePointer:function(e,t){var n=e.calcTransformMatrix(),i=r.util.invertTransform(n),a=this.restorePointerVpt(t);return r.util.transformPoint(a,i)},isTargetTransparent:function(e,t,n){if(e.shouldCache()&&e._cacheCanvas&&e!==this._activeObject){var i=this._normalizePointer(e,{x:t,y:n}),a=Math.max(e.cacheTranslationX+i.x*e.zoomX,0),o=Math.max(e.cacheTranslationY+i.y*e.zoomY,0),s=r.util.isTransparent(e._cacheContext,Math.round(a),Math.round(o),this.targetFindTolerance);return s}var c=this.contextCache,l=e.selectionBackgroundColor,u=this.viewportTransform;e.selectionBackgroundColor="",this.clearContext(c),c.save(),c.transform(u[0],u[1],u[2],u[3],u[4],u[5]),e.render(c),c.restore(),e===this._activeObject&&e._renderControls(c,{hasBorders:!1,transparentCorners:!1},{hasBorders:!1}),e.selectionBackgroundColor=l;s=r.util.isTransparent(c,t,n,this.targetFindTolerance);return s},_isSelectionKeyPressed:function(e){var t=!1;return t="[object Array]"===Object.prototype.toString.call(this.selectionKey)?!!this.selectionKey.find((function(t){return!0===e[t]})):e[this.selectionKey],t},_shouldClearSelection:function(e,t){var n=this.getActiveObjects(),r=this._activeObject;return!t||t&&r&&n.length>1&&-1===n.indexOf(t)&&r!==t&&!this._isSelectionKeyPressed(e)||t&&!t.evented||t&&!t.selectable&&r&&r!==t},_shouldCenterTransform:function(e,t,n){var r;if(e)return"scale"===t||"scaleX"===t||"scaleY"===t||"resizing"===t?r=this.centeredScaling||e.centeredScaling:"rotate"===t&&(r=this.centeredRotation||e.centeredRotation),r?!n:n},_getOriginFromCorner:function(e,t){var n={x:e.originX,y:e.originY};return"ml"===t||"tl"===t||"bl"===t?n.x="right":"mr"!==t&&"tr"!==t&&"br"!==t||(n.x="left"),"tl"===t||"mt"===t||"tr"===t?n.y="bottom":"bl"!==t&&"mb"!==t&&"br"!==t||(n.y="top"),n},_getActionFromCorner:function(e,t,n,r){if(!t||!e)return"drag";var i=r.controls[t];return i.getActionName(n,i,r)},_setupCurrentTransform:function(e,n,i){if(n){var a=this.getPointer(e),o=n.__corner,s=n.controls[o],c=i&&o?s.getActionHandler(e,n,s):r.controlsUtils.dragHandler,l=this._getActionFromCorner(i,o,e,n),u=this._getOriginFromCorner(n,o),h=e[this.centeredKey],f={target:n,action:l,actionHandler:c,corner:o,scaleX:n.scaleX,scaleY:n.scaleY,skewX:n.skewX,skewY:n.skewY,offsetX:a.x-n.left,offsetY:a.y-n.top,originX:u.x,originY:u.y,ex:a.x,ey:a.y,lastX:a.x,lastY:a.y,theta:t(n.angle),width:n.width*n.scaleX,shiftKey:e.shiftKey,altKey:h,original:r.util.saveObjectTransform(n)};this._shouldCenterTransform(n,l,h)&&(f.originX="center",f.originY="center"),f.original.originX=u.x,f.original.originY=u.y,this._currentTransform=f,this._beforeTransform(e)}},setCursor:function(e){this.upperCanvasEl.style.cursor=e},_drawSelection:function(e){var t=this._groupSelector,a=t.left,s=t.top,c=n(a),l=n(s);if(this.selectionColor&&(e.fillStyle=this.selectionColor,e.fillRect(t.ex-(a>0?0:-a),t.ey-(s>0?0:-s),c,l)),this.selectionLineWidth&&this.selectionBorderColor)if(e.lineWidth=this.selectionLineWidth,e.strokeStyle=this.selectionBorderColor,this.selectionDashArray.length>1&&!i){var u=t.ex+o-(a>0?0:c),h=t.ey+o-(s>0?0:l);e.beginPath(),r.util.drawDashedLine(e,u,h,u+c,h,this.selectionDashArray),r.util.drawDashedLine(e,u,h+l-1,u+c,h+l-1,this.selectionDashArray),r.util.drawDashedLine(e,u,h,u,h+l,this.selectionDashArray),r.util.drawDashedLine(e,u+c-1,h,u+c-1,h+l,this.selectionDashArray),e.closePath(),e.stroke()}else r.Object.prototype._setLineDash.call(this,e,this.selectionDashArray),e.strokeRect(t.ex+o-(a>0?0:c),t.ey+o-(s>0?0:l),c,l)},findTarget:function(e,t){if(!this.skipTargetFind){var n,r,i=!0,o=this.getPointer(e,i),s=this._activeObject,c=this.getActiveObjects(),l=a(e);if(this.targets=[],c.length>1&&!t&&s===this._searchPossibleTargets([s],o))return s;if(1===c.length&&s._findTargetCorner(o,l))return s;if(1===c.length&&s===this._searchPossibleTargets([s],o)){if(!this.preserveObjectStacking)return s;n=s,r=this.targets,this.targets=[]}var u=this._searchPossibleTargets(this._objects,o);return e[this.altSelectionKey]&&u&&n&&u!==n&&(u=n,this.targets=r),u}},_checkTarget:function(e,t,n){if(t&&t.visible&&t.evented&&(t.containsPoint(e)||t._findTargetCorner(e))){if(!this.perPixelTargetFind&&!t.perPixelTargetFind||t.isEditing)return!0;var r=this.isTargetTransparent(t,n.x,n.y);if(!r)return!0}},_searchPossibleTargets:function(e,t){var n,i,a=e.length;while(a--){var o=e[a],s=o.group?this._normalizePointer(o.group,t):t;if(this._checkTarget(s,o,t)){n=e[a],n.subTargetCheck&&n instanceof r.Group&&(i=this._searchPossibleTargets(n._objects,t),i&&this.targets.push(i));break}}return n},restorePointerVpt:function(e){return r.util.transformPoint(e,r.util.invertTransform(this.viewportTransform))},getPointer:function(t,n){if(this._absolutePointer&&!n)return this._absolutePointer;if(this._pointer&&n)return this._pointer;var r,i=e(t),a=this.upperCanvasEl,o=a.getBoundingClientRect(),s=o.width||0,c=o.height||0;s&&c||("top"in o&&"bottom"in o&&(c=Math.abs(o.top-o.bottom)),"right"in o&&"left"in o&&(s=Math.abs(o.right-o.left))),this.calcOffset(),i.x=i.x-this._offset.left,i.y=i.y-this._offset.top,n||(i=this.restorePointerVpt(i));var l=this.getRetinaScaling();return 1!==l&&(i.x/=l,i.y/=l),r=0===s||0===c?{width:1,height:1}:{width:a.width/s,height:a.height/c},{x:i.x*r.width,y:i.y*r.height}},_createUpperCanvas:function(){var e=this.lowerCanvasEl.className.replace(/\s*lower-canvas\s*/,""),t=this.lowerCanvasEl,n=this.upperCanvasEl;n?n.className="":(n=this._createCanvasElement(),this.upperCanvasEl=n),r.util.addClass(n,"upper-canvas "+e),this.wrapperEl.appendChild(n),this._copyCanvasStyle(t,n),this._applyCanvasStyle(n),this.contextTop=n.getContext("2d")},_createCacheCanvas:function(){this.cacheCanvasEl=this._createCanvasElement(),this.cacheCanvasEl.setAttribute("width",this.width),this.cacheCanvasEl.setAttribute("height",this.height),this.contextCache=this.cacheCanvasEl.getContext("2d")},_initWrapperElement:function(){this.wrapperEl=r.util.wrapElement(this.lowerCanvasEl,"div",{class:this.containerClass}),r.util.setStyle(this.wrapperEl,{width:this.width+"px",height:this.height+"px",position:"relative"}),r.util.makeElementUnselectable(this.wrapperEl)},_applyCanvasStyle:function(e){var t=this.width||e.width,n=this.height||e.height;r.util.setStyle(e,{position:"absolute",width:t+"px",height:n+"px",left:0,top:0,"touch-action":this.allowTouchScrolling?"manipulation":"none","-ms-touch-action":this.allowTouchScrolling?"manipulation":"none"}),e.width=t,e.height=n,r.util.makeElementUnselectable(e)},_copyCanvasStyle:function(e,t){t.style.cssText=e.style.cssText},getSelectionContext:function(){return this.contextTop},getSelectionElement:function(){return this.upperCanvasEl},getActiveObject:function(){return this._activeObject},getActiveObjects:function(){var e=this._activeObject;return e?"activeSelection"===e.type&&e._objects?e._objects.slice(0):[e]:[]},_onObjectRemoved:function(e){e===this._activeObject&&(this.fire("before:selection:cleared",{target:e}),this._discardActiveObject(),this.fire("selection:cleared",{target:e}),e.fire("deselected")),e===this._hoveredTarget&&(this._hoveredTarget=null,this._hoveredTargets=[]),this.callSuper("_onObjectRemoved",e)},_fireSelectionEvents:function(e,t){var n=!1,r=this.getActiveObjects(),i=[],a=[],o={e:t};e.forEach((function(e){-1===r.indexOf(e)&&(n=!0,e.fire("deselected",o),a.push(e))})),r.forEach((function(t){-1===e.indexOf(t)&&(n=!0,t.fire("selected",o),i.push(t))})),e.length>0&&r.length>0?(o.selected=i,o.deselected=a,o.updated=i[0]||a[0],o.target=this._activeObject,n&&this.fire("selection:updated",o)):r.length>0?(o.selected=i,o.target=this._activeObject,this.fire("selection:created",o)):e.length>0&&(o.deselected=a,this.fire("selection:cleared",o))},setActiveObject:function(e,t){var n=this.getActiveObjects();return this._setActiveObject(e,t),this._fireSelectionEvents(n,t),this},_setActiveObject:function(e,t){return this._activeObject!==e&&(!!this._discardActiveObject(t,e)&&(!e.onSelect({e:t})&&(this._activeObject=e,!0)))},_discardActiveObject:function(e,t){var n=this._activeObject;if(n){if(n.onDeselect({e:e,object:t}))return!1;this._activeObject=null}return!0},discardActiveObject:function(e){var t=this.getActiveObjects(),n=this.getActiveObject();return t.length&&this.fire("before:selection:cleared",{target:n,e:e}),this._discardActiveObject(e),this._fireSelectionEvents(t,e),this},dispose:function(){var e=this.wrapperEl;return this.removeListeners(),e.removeChild(this.upperCanvasEl),e.removeChild(this.lowerCanvasEl),this.contextCache=null,this.contextTop=null,["upperCanvasEl","cacheCanvasEl"].forEach(function(e){r.util.cleanUpJsdomNode(this[e]),this[e]=void 0}.bind(this)),e.parentNode&&e.parentNode.replaceChild(this.lowerCanvasEl,this.wrapperEl),delete this.wrapperEl,r.StaticCanvas.prototype.dispose.call(this),this},clear:function(){return this.discardActiveObject(),this.clearContext(this.contextTop),this.callSuper("clear")},drawControls:function(e){var t=this._activeObject;t&&t._renderControls(e)},_toObject:function(e,t,n){var r=this._realizeGroupTransformOnObject(e),i=this.callSuper("_toObject",e,t,n);return this._unwindGroupTransformOnObject(e,r),i},_realizeGroupTransformOnObject:function(e){if(e.group&&"activeSelection"===e.group.type&&this._activeObject===e.group){var t=["angle","flipX","flipY","left","scaleX","scaleY","skewX","skewY","top"],n={};return t.forEach((function(t){n[t]=e[t]})),this._activeObject.realizeTransform(e),n}return null},_unwindGroupTransformOnObject:function(e,t){t&&e.set(t)},_setSVGObject:function(e,t,n){var r=this._realizeGroupTransformOnObject(t);this.callSuper("_setSVGObject",e,t,n),this._unwindGroupTransformOnObject(t,r)},setViewportTransform:function(e){this.renderOnAddRemove&&this._activeObject&&this._activeObject.isEditing&&this._activeObject.clearContextTop(),r.StaticCanvas.prototype.setViewportTransform.call(this,e)}}),r.StaticCanvas)"prototype"!==s&&(r.Canvas[s]=r.StaticCanvas[s])}(),function(){var e=r.util.addListener,t=r.util.removeListener,n=3,i=2,a=1,o={passive:!1};function s(e,t){return e.button&&e.button===t-1}r.util.object.extend(r.Canvas.prototype,{mainTouchId:null,_initEventListeners:function(){this.removeListeners(),this._bindEvents(),this.addOrRemove(e,"add")},_getEventPrefix:function(){return this.enablePointerEvents?"pointer":"mouse"},addOrRemove:function(e,t){var n=this.upperCanvasEl,i=this._getEventPrefix();e(r.window,"resize",this._onResize),e(n,i+"down",this._onMouseDown),e(n,i+"move",this._onMouseMove,o),e(n,i+"out",this._onMouseOut),e(n,i+"enter",this._onMouseEnter),e(n,"wheel",this._onMouseWheel),e(n,"contextmenu",this._onContextMenu),e(n,"dblclick",this._onDoubleClick),e(n,"dragover",this._onDragOver),e(n,"dragenter",this._onDragEnter),e(n,"dragleave",this._onDragLeave),e(n,"drop",this._onDrop),this.enablePointerEvents||e(n,"touchstart",this._onTouchStart,o),"undefined"!==typeof eventjs&&t in eventjs&&(eventjs[t](n,"gesture",this._onGesture),eventjs[t](n,"drag",this._onDrag),eventjs[t](n,"orientation",this._onOrientationChange),eventjs[t](n,"shake",this._onShake),eventjs[t](n,"longpress",this._onLongPress))},removeListeners:function(){this.addOrRemove(t,"remove");var e=this._getEventPrefix();t(r.document,e+"up",this._onMouseUp),t(r.document,"touchend",this._onTouchEnd,o),t(r.document,e+"move",this._onMouseMove,o),t(r.document,"touchmove",this._onMouseMove,o)},_bindEvents:function(){this.eventsBound||(this._onMouseDown=this._onMouseDown.bind(this),this._onTouchStart=this._onTouchStart.bind(this),this._onMouseMove=this._onMouseMove.bind(this),this._onMouseUp=this._onMouseUp.bind(this),this._onTouchEnd=this._onTouchEnd.bind(this),this._onResize=this._onResize.bind(this),this._onGesture=this._onGesture.bind(this),this._onDrag=this._onDrag.bind(this),this._onShake=this._onShake.bind(this),this._onLongPress=this._onLongPress.bind(this),this._onOrientationChange=this._onOrientationChange.bind(this),this._onMouseWheel=this._onMouseWheel.bind(this),this._onMouseOut=this._onMouseOut.bind(this),this._onMouseEnter=this._onMouseEnter.bind(this),this._onContextMenu=this._onContextMenu.bind(this),this._onDoubleClick=this._onDoubleClick.bind(this),this._onDragOver=this._onDragOver.bind(this),this._onDragEnter=this._simpleEventHandler.bind(this,"dragenter"),this._onDragLeave=this._simpleEventHandler.bind(this,"dragleave"),this._onDrop=this._simpleEventHandler.bind(this,"drop"),this.eventsBound=!0)},_onGesture:function(e,t){this.__onTransformGesture&&this.__onTransformGesture(e,t)},_onDrag:function(e,t){this.__onDrag&&this.__onDrag(e,t)},_onMouseWheel:function(e){this.__onMouseWheel(e)},_onMouseOut:function(e){var t=this._hoveredTarget;this.fire("mouse:out",{target:t,e:e}),this._hoveredTarget=null,t&&t.fire("mouseout",{e:e});var n=this;this._hoveredTargets.forEach((function(r){n.fire("mouse:out",{target:t,e:e}),r&&t.fire("mouseout",{e:e})})),this._hoveredTargets=[],this._iTextInstances&&this._iTextInstances.forEach((function(e){e.isEditing&&e.hiddenTextarea.focus()}))},_onMouseEnter:function(e){this._currentTransform||this.findTarget(e)||(this.fire("mouse:over",{target:null,e:e}),this._hoveredTarget=null,this._hoveredTargets=[])},_onOrientationChange:function(e,t){this.__onOrientationChange&&this.__onOrientationChange(e,t)},_onShake:function(e,t){this.__onShake&&this.__onShake(e,t)},_onLongPress:function(e,t){this.__onLongPress&&this.__onLongPress(e,t)},_onDragOver:function(e){e.preventDefault();var t=this._simpleEventHandler("dragover",e);this._fireEnterLeaveEvents(t,e)},_onContextMenu:function(e){return this.stopContextMenu&&(e.stopPropagation(),e.preventDefault()),!1},_onDoubleClick:function(e){this._cacheTransformEventData(e),this._handleEvent(e,"dblclick"),this._resetTransformEventData(e)},getPointerId:function(e){var t=e.changedTouches;return t?t[0]&&t[0].identifier:this.enablePointerEvents?e.pointerId:-1},_isMainEvent:function(e){return!0===e.isPrimary||!1!==e.isPrimary&&("touchend"===e.type&&0===e.touches.length||(!e.changedTouches||e.changedTouches[0].identifier===this.mainTouchId))},_onTouchStart:function(n){n.preventDefault(),null===this.mainTouchId&&(this.mainTouchId=this.getPointerId(n)),this.__onMouseDown(n),this._resetTransformEventData();var i=this.upperCanvasEl,a=this._getEventPrefix();e(r.document,"touchend",this._onTouchEnd,o),e(r.document,"touchmove",this._onMouseMove,o),t(i,a+"down",this._onMouseDown)},_onMouseDown:function(n){this.__onMouseDown(n),this._resetTransformEventData();var i=this.upperCanvasEl,a=this._getEventPrefix();t(i,a+"move",this._onMouseMove,o),e(r.document,a+"up",this._onMouseUp),e(r.document,a+"move",this._onMouseMove,o)},_onTouchEnd:function(n){if(!(n.touches.length>0)){this.__onMouseUp(n),this._resetTransformEventData(),this.mainTouchId=null;var i=this._getEventPrefix();t(r.document,"touchend",this._onTouchEnd,o),t(r.document,"touchmove",this._onMouseMove,o);var a=this;this._willAddMouseDown&&clearTimeout(this._willAddMouseDown),this._willAddMouseDown=setTimeout((function(){e(a.upperCanvasEl,i+"down",a._onMouseDown),a._willAddMouseDown=0}),400)}},_onMouseUp:function(n){this.__onMouseUp(n),this._resetTransformEventData();var i=this.upperCanvasEl,a=this._getEventPrefix();this._isMainEvent(n)&&(t(r.document,a+"up",this._onMouseUp),t(r.document,a+"move",this._onMouseMove,o),e(i,a+"move",this._onMouseMove,o))},_onMouseMove:function(e){!this.allowTouchScrolling&&e.preventDefault&&e.preventDefault(),this.__onMouseMove(e)},_onResize:function(){this.calcOffset()},_shouldRender:function(e){var t=this._activeObject;return!!(!!t!==!!e||t&&e&&t!==e)||(t&&t.isEditing,!1)},__onMouseUp:function(e){var t,o=this._currentTransform,c=this._groupSelector,l=!1,u=!c||0===c.left&&0===c.top;if(this._cacheTransformEventData(e),t=this._target,this._handleEvent(e,"up:before"),s(e,n))this.fireRightClick&&this._handleEvent(e,"up",n,u);else{if(s(e,i))return this.fireMiddleClick&&this._handleEvent(e,"up",i,u),void this._resetTransformEventData();if(this.isDrawingMode&&this._isCurrentlyDrawing)this._onMouseUpInDrawingMode(e);else if(this._isMainEvent(e)){if(o&&(this._finalizeCurrentTransform(e),l=o.actionPerformed),!u){var h=t===this._activeObject;this._maybeGroupObjects(e),l||(l=this._shouldRender(t)||!h&&t===this._activeObject)}if(t){var f=t._findTargetCorner(this.getPointer(e,!0),r.util.isTouchEvent(e)),d=t.controls[f],p=d&&d.getMouseUpHandler(e,t,d);if(p){var m=this.getPointer(e);p(e,o,m.x,m.y)}t.isMoving=!1}this._setCursorFromEvent(e,t),this._handleEvent(e,"up",a,u),this._groupSelector=null,this._currentTransform=null,t&&(t.__corner=0),l?this.requestRenderAll():u||this.renderTop()}}},_simpleEventHandler:function(e,t){var n=this.findTarget(t),r=this.targets,i={e:t,target:n,subTargets:r};if(this.fire(e,i),n&&n.fire(e,i),!r)return n;for(var a=0;a1&&(t=new r.ActiveSelection(n.reverse(),{canvas:this}),this.setActiveObject(t,e))},_collectObjects:function(n){for(var i,a=[],o=this._groupSelector.ex,s=this._groupSelector.ey,c=o+this._groupSelector.left,l=s+this._groupSelector.top,u=new r.Point(e(o,c),e(s,l)),h=new r.Point(t(o,c),t(s,l)),f=!this.selectionFullyContained,d=o===c&&s===l,p=this._objects.length;p--;)if(i=this._objects[p],i&&i.selectable&&i.visible&&(f&&i.intersectsWithRect(u,h)||i.isContainedWithinRect(u,h)||f&&i.containsPoint(u)||f&&i.containsPoint(h))&&(a.push(i),d))break;return a.length>1&&(a=a.filter((function(e){return!e.onSelect({e:n})}))),a},_maybeGroupObjects:function(e){this.selection&&this._groupSelector&&this._groupSelectedObjects(e),this.setCursor(this.defaultCursor),this._groupSelector=null}})}(),function(){r.util.object.extend(r.StaticCanvas.prototype,{toDataURL:function(e){e||(e={});var t=e.format||"png",n=e.quality||1,i=(e.multiplier||1)*(e.enableRetinaScaling?this.getRetinaScaling():1),a=this.toCanvasElement(i,e);return r.util.toDataURL(a,t,n)},toCanvasElement:function(e,t){e=e||1,t=t||{};var n=(t.width||this.width)*e,i=(t.height||this.height)*e,a=this.getZoom(),o=this.width,s=this.height,c=a*e,l=this.viewportTransform,u=(l[4]-(t.left||0))*e,h=(l[5]-(t.top||0))*e,f=this.interactive,d=[c,0,0,c,u,h],p=this.enableRetinaScaling,m=r.util.createCanvasElement(),v=this.contextTop;return m.width=n,m.height=i,this.contextTop=null,this.enableRetinaScaling=!1,this.interactive=!1,this.viewportTransform=d,this.width=n,this.height=i,this.calcViewportBoundaries(),this.renderCanvas(m.getContext("2d"),this._objects),this.viewportTransform=l,this.width=o,this.height=s,this.calcViewportBoundaries(),this.interactive=f,this.enableRetinaScaling=p,this.contextTop=v,m}})}(),r.util.object.extend(r.StaticCanvas.prototype,{loadFromJSON:function(e,t,n){if(e){var i="string"===typeof e?JSON.parse(e):r.util.object.clone(e),a=this,o=i.clipPath,s=this.renderOnAddRemove;return this.renderOnAddRemove=!1,delete i.clipPath,this._enlivenObjects(i.objects,(function(e){a.clear(),a._setBgOverlay(i,(function(){o?a._enlivenObjects([o],(function(n){a.clipPath=n[0],a.__setupCanvas.call(a,i,e,s,t)})):a.__setupCanvas.call(a,i,e,s,t)}))}),n),this}},__setupCanvas:function(e,t,n,r){var i=this;t.forEach((function(e,t){i.insertAt(e,t)})),this.renderOnAddRemove=n,delete e.objects,delete e.backgroundImage,delete e.overlayImage,delete e.background,delete e.overlay,this._setOptions(e),this.renderAll(),r&&r()},_setBgOverlay:function(e,t){var n={backgroundColor:!1,overlayColor:!1,backgroundImage:!1,overlayImage:!1};if(e.backgroundImage||e.overlayImage||e.background||e.overlay){var r=function(){n.backgroundImage&&n.overlayImage&&n.backgroundColor&&n.overlayColor&&t&&t()};this.__setBgOverlay("backgroundImage",e.backgroundImage,n,r),this.__setBgOverlay("overlayImage",e.overlayImage,n,r),this.__setBgOverlay("backgroundColor",e.background,n,r),this.__setBgOverlay("overlayColor",e.overlay,n,r)}else t&&t()},__setBgOverlay:function(e,t,n,i){var a=this;if(!t)return n[e]=!0,void(i&&i());"backgroundImage"===e||"overlayImage"===e?r.util.enlivenObjects([t],(function(t){a[e]=t[0],n[e]=!0,i&&i()})):this["set"+r.util.string.capitalize(e,!0)](t,(function(){n[e]=!0,i&&i()}))},_enlivenObjects:function(e,t,n){e&&0!==e.length?r.util.enlivenObjects(e,(function(e){t&&t(e)}),null,n):t&&t([])},_toDataURL:function(e,t){this.clone((function(n){t(n.toDataURL(e))}))},_toDataURLWithMultiplier:function(e,t,n){this.clone((function(r){n(r.toDataURLWithMultiplier(e,t))}))},clone:function(e,t){var n=JSON.stringify(this.toJSON(t));this.cloneWithoutData((function(t){t.loadFromJSON(n,(function(){e&&e(t)}))}))},cloneWithoutData:function(e){var t=r.util.createCanvasElement();t.width=this.width,t.height=this.height;var n=new r.Canvas(t);this.backgroundImage?(n.setBackgroundImage(this.backgroundImage.src,(function(){n.renderAll(),e&&e(n)})),n.backgroundImageOpacity=this.backgroundImageOpacity,n.backgroundImageStretch=this.backgroundImageStretch):e&&e(n)}}),function(e){"use strict";var t=e.fabric||(e.fabric={}),n=t.util.object.extend,r=t.util.object.clone,i=t.util.toFixed,a=t.util.string.capitalize,o=t.util.degreesToRadians,s=t.StaticCanvas.supports("setLineDash"),c=!t.isLikelyNode,l=2;t.Object||(t.Object=t.util.createClass(t.CommonMethods,{type:"object",originX:"left",originY:"top",top:0,left:0,width:0,height:0,scaleX:1,scaleY:1,flipX:!1,flipY:!1,opacity:1,angle:0,skewX:0,skewY:0,cornerSize:13,touchCornerSize:24,transparentCorners:!0,hoverCursor:null,moveCursor:null,padding:0,borderColor:"rgb(178,204,255)",borderDashArray:null,cornerColor:"rgb(178,204,255)",cornerStrokeColor:null,cornerStyle:"rect",cornerDashArray:null,centeredScaling:!1,centeredRotation:!0,fill:"rgb(0,0,0)",fillRule:"nonzero",globalCompositeOperation:"source-over",backgroundColor:"",selectionBackgroundColor:"",stroke:null,strokeWidth:1,strokeDashArray:null,strokeDashOffset:0,strokeLineCap:"butt",strokeLineJoin:"miter",strokeMiterLimit:4,shadow:null,borderOpacityWhenMoving:.4,borderScaleFactor:1,minScaleLimit:0,selectable:!0,evented:!0,visible:!0,hasControls:!0,hasBorders:!0,perPixelTargetFind:!1,includeDefaultValues:!0,lockMovementX:!1,lockMovementY:!1,lockRotation:!1,lockScalingX:!1,lockScalingY:!1,lockSkewingX:!1,lockSkewingY:!1,lockScalingFlip:!1,excludeFromExport:!1,objectCaching:c,statefullCache:!1,noScaleCache:!0,strokeUniform:!1,dirty:!0,__corner:0,paintFirst:"fill",stateProperties:"top left width height scaleX scaleY flipX flipY originX originY transformMatrix stroke strokeWidth strokeDashArray strokeLineCap strokeDashOffset strokeLineJoin strokeMiterLimit angle opacity fill globalCompositeOperation shadow visible backgroundColor skewX skewY fillRule paintFirst clipPath strokeUniform".split(" "),cacheProperties:"fill stroke strokeWidth strokeDashArray width height paintFirst strokeUniform strokeLineCap strokeDashOffset strokeLineJoin strokeMiterLimit backgroundColor clipPath".split(" "),colorProperties:"fill stroke backgroundColor".split(" "),clipPath:void 0,inverted:!1,absolutePositioned:!1,initialize:function(e){e&&this.setOptions(e)},_createCacheCanvas:function(){this._cacheProperties={},this._cacheCanvas=t.util.createCanvasElement(),this._cacheContext=this._cacheCanvas.getContext("2d"),this._updateCacheCanvas(),this.dirty=!0},_limitCacheSize:function(e){var n=t.perfLimitSizeTotal,r=e.width,i=e.height,a=t.maxCacheSideLimit,o=t.minCacheSideLimit;if(r<=a&&i<=a&&r*i<=n)return ru&&(e.zoomX/=r/u,e.width=u,e.capped=!0),i>h&&(e.zoomY/=i/h,e.height=h,e.capped=!0),e},_getCacheCanvasDimensions:function(){var e=this.getTotalObjectScaling(),t=this._getTransformedDimensions(0,0),n=t.x*e.scaleX/this.scaleX,r=t.y*e.scaleY/this.scaleY;return{width:n+l,height:r+l,zoomX:e.scaleX,zoomY:e.scaleY,x:n,y:r}},_updateCacheCanvas:function(){var e=this.canvas;if(this.noScaleCache&&e&&e._currentTransform){var n=e._currentTransform.target,r=e._currentTransform.action;if(this===n&&r.slice&&"scale"===r.slice(0,5))return!1}var i,a,o=this._cacheCanvas,s=this._limitCacheSize(this._getCacheCanvasDimensions()),c=t.minCacheSideLimit,l=s.width,u=s.height,h=s.zoomX,f=s.zoomY,d=l!==this.cacheWidth||u!==this.cacheHeight,p=this.zoomX!==h||this.zoomY!==f,m=d||p,v=0,g=0,b=!1;if(d){var y=this._cacheCanvas.width,_=this._cacheCanvas.height,w=l>y||u>_,M=(l<.9*y||u<.9*_)&&y>c&&_>c;b=w||M,w&&!s.capped&&(l>c||u>c)&&(v=.1*l,g=.1*u)}return!!m&&(b?(o.width=Math.ceil(l+v),o.height=Math.ceil(u+g)):(this._cacheContext.setTransform(1,0,0,1,0,0),this._cacheContext.clearRect(0,0,o.width,o.height)),i=s.x/2,a=s.y/2,this.cacheTranslationX=Math.round(o.width/2-i)+i,this.cacheTranslationY=Math.round(o.height/2-a)+a,this.cacheWidth=l,this.cacheHeight=u,this._cacheContext.translate(this.cacheTranslationX,this.cacheTranslationY),this._cacheContext.scale(h,f),this.zoomX=h,this.zoomY=f,!0)},setOptions:function(e){this._setOptions(e),this._initGradient(e.fill,"fill"),this._initGradient(e.stroke,"stroke"),this._initPattern(e.fill,"fill"),this._initPattern(e.stroke,"stroke")},transform:function(e){var t=this.group&&!this.group._transformDone||this.group&&this.canvas&&e===this.canvas.contextTop,n=this.calcTransformMatrix(!t);e.transform(n[0],n[1],n[2],n[3],n[4],n[5])},toObject:function(e){var n=t.Object.NUM_FRACTION_DIGITS,r={type:this.type,version:t.version,originX:this.originX,originY:this.originY,left:i(this.left,n),top:i(this.top,n),width:i(this.width,n),height:i(this.height,n),fill:this.fill&&this.fill.toObject?this.fill.toObject():this.fill,stroke:this.stroke&&this.stroke.toObject?this.stroke.toObject():this.stroke,strokeWidth:i(this.strokeWidth,n),strokeDashArray:this.strokeDashArray?this.strokeDashArray.concat():this.strokeDashArray,strokeLineCap:this.strokeLineCap,strokeDashOffset:this.strokeDashOffset,strokeLineJoin:this.strokeLineJoin,strokeMiterLimit:i(this.strokeMiterLimit,n),scaleX:i(this.scaleX,n),scaleY:i(this.scaleY,n),angle:i(this.angle,n),flipX:this.flipX,flipY:this.flipY,opacity:i(this.opacity,n),shadow:this.shadow&&this.shadow.toObject?this.shadow.toObject():this.shadow,visible:this.visible,backgroundColor:this.backgroundColor,fillRule:this.fillRule,paintFirst:this.paintFirst,globalCompositeOperation:this.globalCompositeOperation,skewX:i(this.skewX,n),skewY:i(this.skewY,n)};return this.clipPath&&(r.clipPath=this.clipPath.toObject(e),r.clipPath.inverted=this.clipPath.inverted,r.clipPath.absolutePositioned=this.clipPath.absolutePositioned),t.util.populateWithProperties(this,r,e),this.includeDefaultValues||(r=this._removeDefaultValues(r)),r},toDatalessObject:function(e){return this.toObject(e)},_removeDefaultValues:function(e){var n=t.util.getKlass(e.type).prototype,r=n.stateProperties;return r.forEach((function(t){if("left"!==t&&"top"!==t){e[t]===n[t]&&delete e[t];var r="[object Array]"===Object.prototype.toString.call(e[t])&&"[object Array]"===Object.prototype.toString.call(n[t]);r&&0===e[t].length&&0===n[t].length&&delete e[t]}})),e},toString:function(){return"#"},getObjectScaling:function(){var e=t.util.qrDecompose(this.calcTransformMatrix());return{scaleX:Math.abs(e.scaleX),scaleY:Math.abs(e.scaleY)}},getTotalObjectScaling:function(){var e=this.getObjectScaling(),t=e.scaleX,n=e.scaleY;if(this.canvas){var r=this.canvas.getZoom(),i=this.canvas.getRetinaScaling();t*=r*i,n*=r*i}return{scaleX:t,scaleY:n}},getObjectOpacity:function(){var e=this.opacity;return this.group&&(e*=this.group.getObjectOpacity()),e},_set:function(e,n){var r="scaleX"===e||"scaleY"===e,i=this[e]!==n,a=!1;return r&&(n=this._constrainScale(n)),"scaleX"===e&&n<0?(this.flipX=!this.flipX,n*=-1):"scaleY"===e&&n<0?(this.flipY=!this.flipY,n*=-1):"shadow"!==e||!n||n instanceof t.Shadow?"dirty"===e&&this.group&&this.group.set("dirty",n):n=new t.Shadow(n),this[e]=n,i&&(a=this.group&&this.group.isOnACache(),this.cacheProperties.indexOf(e)>-1?(this.dirty=!0,a&&this.group.set("dirty",!0)):a&&this.stateProperties.indexOf(e)>-1&&this.group.set("dirty",!0)),this},setOnGroup:function(){},getViewportTransform:function(){return this.canvas&&this.canvas.viewportTransform?this.canvas.viewportTransform:t.iMatrix.concat()},isNotVisible:function(){return 0===this.opacity||!this.width&&!this.height&&0===this.strokeWidth||!this.visible},render:function(e){this.isNotVisible()||this.canvas&&this.canvas.skipOffscreen&&!this.group&&!this.isOnScreen()||(e.save(),this._setupCompositeOperation(e),this.drawSelectionBackground(e),this.transform(e),this._setOpacity(e),this._setShadow(e,this),this.shouldCache()?(this.renderCache(),this.drawCacheOnCanvas(e)):(this._removeCacheCanvas(),this.dirty=!1,this.drawObject(e),this.objectCaching&&this.statefullCache&&this.saveState({propertySet:"cacheProperties"})),e.restore())},renderCache:function(e){e=e||{},this._cacheCanvas||this._createCacheCanvas(),this.isCacheDirty()&&(this.statefullCache&&this.saveState({propertySet:"cacheProperties"}),this.drawObject(this._cacheContext,e.forClipping),this.dirty=!1)},_removeCacheCanvas:function(){this._cacheCanvas=null,this.cacheWidth=0,this.cacheHeight=0},hasStroke:function(){return this.stroke&&"transparent"!==this.stroke&&0!==this.strokeWidth},hasFill:function(){return this.fill&&"transparent"!==this.fill},needsItsOwnCache:function(){return!("stroke"!==this.paintFirst||!this.hasFill()||!this.hasStroke()||"object"!==typeof this.shadow)||!!this.clipPath},shouldCache:function(){return this.ownCaching=this.needsItsOwnCache()||this.objectCaching&&(!this.group||!this.group.isOnACache()),this.ownCaching},willDrawShadow:function(){return!!this.shadow&&(0!==this.shadow.offsetX||0!==this.shadow.offsetY)},drawClipPathOnCache:function(e){var n=this.clipPath;if(e.save(),n.inverted?e.globalCompositeOperation="destination-out":e.globalCompositeOperation="destination-in",n.absolutePositioned){var r=t.util.invertTransform(this.calcTransformMatrix());e.transform(r[0],r[1],r[2],r[3],r[4],r[5])}n.transform(e),e.scale(1/n.zoomX,1/n.zoomY),e.drawImage(n._cacheCanvas,-n.cacheTranslationX,-n.cacheTranslationY),e.restore()},drawObject:function(e,t){var n=this.fill,r=this.stroke;t?(this.fill="black",this.stroke="",this._setClippingProperties(e)):this._renderBackground(e),this._render(e),this._drawClipPath(e),this.fill=n,this.stroke=r},_drawClipPath:function(e){var t=this.clipPath;t&&(t.canvas=this.canvas,t.shouldCache(),t._transformDone=!0,t.renderCache({forClipping:!0}),this.drawClipPathOnCache(e))},drawCacheOnCanvas:function(e){e.scale(1/this.zoomX,1/this.zoomY),e.drawImage(this._cacheCanvas,-this.cacheTranslationX,-this.cacheTranslationY)},isCacheDirty:function(e){if(this.isNotVisible())return!1;if(this._cacheCanvas&&!e&&this._updateCacheCanvas())return!0;if(this.dirty||this.clipPath&&this.clipPath.absolutePositioned||this.statefullCache&&this.hasStateChanged("cacheProperties")){if(this._cacheCanvas&&!e){var t=this.cacheWidth/this.zoomX,n=this.cacheHeight/this.zoomY;this._cacheContext.clearRect(-t/2,-n/2,t,n)}return!0}return!1},_renderBackground:function(e){if(this.backgroundColor){var t=this._getNonTransformedDimensions();e.fillStyle=this.backgroundColor,e.fillRect(-t.x/2,-t.y/2,t.x,t.y),this._removeShadow(e)}},_setOpacity:function(e){this.group&&!this.group._transformDone?e.globalAlpha=this.getObjectOpacity():e.globalAlpha*=this.opacity},_setStrokeStyles:function(e,t){var n=t.stroke;n&&(e.lineWidth=t.strokeWidth,e.lineCap=t.strokeLineCap,e.lineDashOffset=t.strokeDashOffset,e.lineJoin=t.strokeLineJoin,e.miterLimit=t.strokeMiterLimit,n.toLive?"percentage"===n.gradientUnits||n.gradientTrasnform||n.patternTransform?this._applyPatternForTransformedGradient(e,n):(e.strokeStyle=n.toLive(e,this),this._applyPatternGradientTransform(e,n)):e.strokeStyle=t.stroke)},_setFillStyles:function(e,t){var n=t.fill;n&&(n.toLive?(e.fillStyle=n.toLive(e,this),this._applyPatternGradientTransform(e,t.fill)):e.fillStyle=n)},_setClippingProperties:function(e){e.globalAlpha=1,e.strokeStyle="transparent",e.fillStyle="#000000"},_setLineDash:function(e,t,n){t&&0!==t.length&&(1&t.length&&t.push.apply(t,t),s?e.setLineDash(t):n&&n(e))},_renderControls:function(e,n){var r,i,a,s=this.getViewportTransform(),c=this.calcTransformMatrix();n=n||{},i="undefined"!==typeof n.hasBorders?n.hasBorders:this.hasBorders,a="undefined"!==typeof n.hasControls?n.hasControls:this.hasControls,c=t.util.multiplyTransformMatrices(s,c),r=t.util.qrDecompose(c),e.save(),e.translate(r.translateX,r.translateY),e.lineWidth=1*this.borderScaleFactor,this.group||(e.globalAlpha=this.isMoving?this.borderOpacityWhenMoving:1),n.forActiveSelection?(e.rotate(o(r.angle)),i&&this.drawBordersInGroup(e,r,n)):(e.rotate(o(this.angle)),i&&this.drawBorders(e,n)),a&&this.drawControls(e,n),e.restore()},_setShadow:function(e){if(this.shadow){var n,r=this.shadow,i=this.canvas,a=i&&i.viewportTransform[0]||1,o=i&&i.viewportTransform[3]||1;n=r.nonScaling?{scaleX:1,scaleY:1}:this.getObjectScaling(),i&&i._isRetinaScaling()&&(a*=t.devicePixelRatio,o*=t.devicePixelRatio),e.shadowColor=r.color,e.shadowBlur=r.blur*t.browserShadowBlurConstant*(a+o)*(n.scaleX+n.scaleY)/4,e.shadowOffsetX=r.offsetX*a*n.scaleX,e.shadowOffsetY=r.offsetY*o*n.scaleY}},_removeShadow:function(e){this.shadow&&(e.shadowColor="",e.shadowBlur=e.shadowOffsetX=e.shadowOffsetY=0)},_applyPatternGradientTransform:function(e,t){if(!t||!t.toLive)return{offsetX:0,offsetY:0};var n=t.gradientTransform||t.patternTransform,r=-this.width/2+t.offsetX||0,i=-this.height/2+t.offsetY||0;return"percentage"===t.gradientUnits?e.transform(this.width,0,0,this.height,r,i):e.transform(1,0,0,1,r,i),n&&e.transform(n[0],n[1],n[2],n[3],n[4],n[5]),{offsetX:r,offsetY:i}},_renderPaintInOrder:function(e){"stroke"===this.paintFirst?(this._renderStroke(e),this._renderFill(e)):(this._renderFill(e),this._renderStroke(e))},_render:function(){},_renderFill:function(e){this.fill&&(e.save(),this._setFillStyles(e,this),"evenodd"===this.fillRule?e.fill("evenodd"):e.fill(),e.restore())},_renderStroke:function(e){if(this.stroke&&0!==this.strokeWidth){if(this.shadow&&!this.shadow.affectStroke&&this._removeShadow(e),e.save(),this.strokeUniform&&this.group){var t=this.getObjectScaling();e.scale(1/t.scaleX,1/t.scaleY)}else this.strokeUniform&&e.scale(1/this.scaleX,1/this.scaleY);this._setLineDash(e,this.strokeDashArray,this._renderDashedStroke),this._setStrokeStyles(e,this),e.stroke(),e.restore()}},_applyPatternForTransformedGradient:function(e,n){var r,i=this._limitCacheSize(this._getCacheCanvasDimensions()),a=t.util.createCanvasElement(),o=this.canvas.getRetinaScaling(),s=i.x/this.scaleX/o,c=i.y/this.scaleY/o;a.width=s,a.height=c,r=a.getContext("2d"),r.beginPath(),r.moveTo(0,0),r.lineTo(s,0),r.lineTo(s,c),r.lineTo(0,c),r.closePath(),r.translate(s/2,c/2),r.scale(i.zoomX/this.scaleX/o,i.zoomY/this.scaleY/o),this._applyPatternGradientTransform(r,n),r.fillStyle=n.toLive(e),r.fill(),e.translate(-this.width/2-this.strokeWidth/2,-this.height/2-this.strokeWidth/2),e.scale(o*this.scaleX/i.zoomX,o*this.scaleY/i.zoomY),e.strokeStyle=r.createPattern(a,"no-repeat")},_findCenterFromElement:function(){return{x:this.left+this.width/2,y:this.top+this.height/2}},_assignTransformMatrixProps:function(){if(this.transformMatrix){var e=t.util.qrDecompose(this.transformMatrix);this.flipX=!1,this.flipY=!1,this.set("scaleX",e.scaleX),this.set("scaleY",e.scaleY),this.angle=e.angle,this.skewX=e.skewX,this.skewY=0}},_removeTransformMatrix:function(e){var n=this._findCenterFromElement();this.transformMatrix&&(this._assignTransformMatrixProps(),n=t.util.transformPoint(n,this.transformMatrix)),this.transformMatrix=null,e&&(this.scaleX*=e.scaleX,this.scaleY*=e.scaleY,this.cropX=e.cropX,this.cropY=e.cropY,n.x+=e.offsetLeft,n.y+=e.offsetTop,this.width=e.width,this.height=e.height),this.setPositionByOrigin(n,"center","center")},clone:function(e,n){var r=this.toObject(n);this.constructor.fromObject?this.constructor.fromObject(r,e):t.Object._fromObject("Object",r,e)},cloneAsImage:function(e,n){var r=this.toCanvasElement(n);return e&&e(new t.Image(r)),this},toCanvasElement:function(e){e||(e={});var n=t.util,r=n.saveObjectTransform(this),i=this.group,a=this.shadow,o=Math.abs,s=(e.multiplier||1)*(e.enableRetinaScaling?t.devicePixelRatio:1);delete this.group,e.withoutTransform&&n.resetObjectTransform(this),e.withoutShadow&&(this.shadow=null);var c,l,u,h,f=t.util.createCanvasElement(),d=this.getBoundingRect(!0,!0),p=this.shadow,m={x:0,y:0};p&&(l=p.blur,c=p.nonScaling?{scaleX:1,scaleY:1}:this.getObjectScaling(),m.x=2*Math.round(o(p.offsetX)+l)*o(c.scaleX),m.y=2*Math.round(o(p.offsetY)+l)*o(c.scaleY)),u=d.width+m.x,h=d.height+m.y,f.width=Math.ceil(u),f.height=Math.ceil(h);var v=new t.StaticCanvas(f,{enableRetinaScaling:!1,renderOnAddRemove:!1,skipOffscreen:!1});"jpeg"===e.format&&(v.backgroundColor="#fff"),this.setPositionByOrigin(new t.Point(v.width/2,v.height/2),"center","center");var g=this.canvas;v.add(this);var b=v.toCanvasElement(s||1,e);return this.shadow=a,this.set("canvas",g),i&&(this.group=i),this.set(r).setCoords(),v._objects=[],v.dispose(),v=null,b},toDataURL:function(e){return e||(e={}),t.util.toDataURL(this.toCanvasElement(e),e.format||"png",e.quality||1)},isType:function(e){return this.type===e},complexity:function(){return 1},toJSON:function(e){return this.toObject(e)},rotate:function(e){var t=("center"!==this.originX||"center"!==this.originY)&&this.centeredRotation;return t&&this._setOriginToCenter(),this.set("angle",e),t&&this._resetOrigin(),this},centerH:function(){return this.canvas&&this.canvas.centerObjectH(this),this},viewportCenterH:function(){return this.canvas&&this.canvas.viewportCenterObjectH(this),this},centerV:function(){return this.canvas&&this.canvas.centerObjectV(this),this},viewportCenterV:function(){return this.canvas&&this.canvas.viewportCenterObjectV(this),this},center:function(){return this.canvas&&this.canvas.centerObject(this),this},viewportCenter:function(){return this.canvas&&this.canvas.viewportCenterObject(this),this},getLocalPointer:function(e,n){n=n||this.canvas.getPointer(e);var r=new t.Point(n.x,n.y),i=this._getLeftTopCoords();return this.angle&&(r=t.util.rotatePoint(r,i,o(-this.angle))),{x:r.x-i.x,y:r.y-i.y}},_setupCompositeOperation:function(e){this.globalCompositeOperation&&(e.globalCompositeOperation=this.globalCompositeOperation)}}),t.util.createAccessors&&t.util.createAccessors(t.Object),n(t.Object.prototype,t.Observable),t.Object.NUM_FRACTION_DIGITS=2,t.Object._fromObject=function(e,n,i,a){var o=t[e];n=r(n,!0),t.util.enlivenPatterns([n.fill,n.stroke],(function(e){"undefined"!==typeof e[0]&&(n.fill=e[0]),"undefined"!==typeof e[1]&&(n.stroke=e[1]),t.util.enlivenObjects([n.clipPath],(function(e){n.clipPath=e[0];var t=a?new o(n[a],n):new o(n);i&&i(t)}))}))},t.Object.__uid=0)}(t),function(){var e=r.util.degreesToRadians,t={left:-.5,center:0,right:.5},n={top:-.5,center:0,bottom:.5};r.util.object.extend(r.Object.prototype,{translateToGivenOrigin:function(e,i,a,o,s){var c,l,u,h=e.x,f=e.y;return"string"===typeof i?i=t[i]:i-=.5,"string"===typeof o?o=t[o]:o-=.5,c=o-i,"string"===typeof a?a=n[a]:a-=.5,"string"===typeof s?s=n[s]:s-=.5,l=s-a,(c||l)&&(u=this._getTransformedDimensions(),h=e.x+c*u.x,f=e.y+l*u.y),new r.Point(h,f)},translateToCenterPoint:function(t,n,i){var a=this.translateToGivenOrigin(t,n,i,"center","center");return this.angle?r.util.rotatePoint(a,t,e(this.angle)):a},translateToOriginPoint:function(t,n,i){var a=this.translateToGivenOrigin(t,"center","center",n,i);return this.angle?r.util.rotatePoint(a,t,e(this.angle)):a},getCenterPoint:function(){var e=new r.Point(this.left,this.top);return this.translateToCenterPoint(e,this.originX,this.originY)},getPointByOrigin:function(e,t){var n=this.getCenterPoint();return this.translateToOriginPoint(n,e,t)},toLocalPoint:function(t,n,i){var a,o,s=this.getCenterPoint();return a="undefined"!==typeof n&&"undefined"!==typeof i?this.translateToGivenOrigin(s,"center","center",n,i):new r.Point(this.left,this.top),o=new r.Point(t.x,t.y),this.angle&&(o=r.util.rotatePoint(o,s,-e(this.angle))),o.subtractEquals(a)},setPositionByOrigin:function(e,t,n){var r=this.translateToCenterPoint(e,t,n),i=this.translateToOriginPoint(r,this.originX,this.originY);this.set("left",i.x),this.set("top",i.y)},adjustPosition:function(n){var i,a,o=e(this.angle),s=this.getScaledWidth(),c=r.util.cos(o)*s,l=r.util.sin(o)*s;i="string"===typeof this.originX?t[this.originX]:this.originX-.5,a="string"===typeof n?t[n]:n-.5,this.left+=c*(a-i),this.top+=l*(a-i),this.setCoords(),this.originX=n},_setOriginToCenter:function(){this._originalOriginX=this.originX,this._originalOriginY=this.originY;var e=this.getCenterPoint();this.originX="center",this.originY="center",this.left=e.x,this.top=e.y},_resetOrigin:function(){var e=this.translateToOriginPoint(this.getCenterPoint(),this._originalOriginX,this._originalOriginY);this.originX=this._originalOriginX,this.originY=this._originalOriginY,this.left=e.x,this.top=e.y,this._originalOriginX=null,this._originalOriginY=null},_getLeftTopCoords:function(){return this.translateToOriginPoint(this.getCenterPoint(),"left","top")}})}(),function(){function e(e){return[new r.Point(e.tl.x,e.tl.y),new r.Point(e.tr.x,e.tr.y),new r.Point(e.br.x,e.br.y),new r.Point(e.bl.x,e.bl.y)]}var t=r.util,n=t.degreesToRadians,i=t.multiplyTransformMatrices,a=t.transformPoint;t.object.extend(r.Object.prototype,{oCoords:null,aCoords:null,lineCoords:null,ownMatrixCache:null,matrixCache:null,controls:{},_getCoords:function(e,t){return t?e?this.calcACoords():this.calcLineCoords():(this.aCoords&&this.lineCoords||this.setCoords(!0),e?this.aCoords:this.lineCoords)},getCoords:function(t,n){return e(this._getCoords(t,n))},intersectsWithRect:function(e,t,n,i){var a=this.getCoords(n,i),o=r.Intersection.intersectPolygonRectangle(a,e,t);return"Intersection"===o.status},intersectsWithObject:function(e,t,n){var i=r.Intersection.intersectPolygonPolygon(this.getCoords(t,n),e.getCoords(t,n));return"Intersection"===i.status||e.isContainedWithinObject(this,t,n)||this.isContainedWithinObject(e,t,n)},isContainedWithinObject:function(e,t,n){for(var r=this.getCoords(t,n),i=t?e.aCoords:e.lineCoords,a=0,o=e._getImageLines(i);a<4;a++)if(!e.containsPoint(r[a],o))return!1;return!0},isContainedWithinRect:function(e,t,n,r){var i=this.getBoundingRect(n,r);return i.left>=e.x&&i.left+i.width<=t.x&&i.top>=e.y&&i.top+i.height<=t.y},containsPoint:function(e,t,n,r){var i=this._getCoords(n,r),a=(t=t||this._getImageLines(i),this._findCrossPoints(e,t));return 0!==a&&a%2===1},isOnScreen:function(e){if(!this.canvas)return!1;var t=this.canvas.vptCoords.tl,n=this.canvas.vptCoords.br,r=this.getCoords(!0,e);return!!r.some((function(e){return e.x<=n.x&&e.x>=t.x&&e.y<=n.y&&e.y>=t.y}))||(!!this.intersectsWithRect(t,n,!0,e)||this._containsCenterOfCanvas(t,n,e))},_containsCenterOfCanvas:function(e,t,n){var r={x:(e.x+t.x)/2,y:(e.y+t.y)/2};return!!this.containsPoint(r,null,!0,n)},isPartiallyOnScreen:function(e){if(!this.canvas)return!1;var t=this.canvas.vptCoords.tl,n=this.canvas.vptCoords.br;if(this.intersectsWithRect(t,n,!0,e))return!0;var r=this.getCoords(!0,e).every((function(e){return(e.x>=n.x||e.x<=t.x)&&(e.y>=n.y||e.y<=t.y)}));return r&&this._containsCenterOfCanvas(t,n,e)},_getImageLines:function(e){var t={topline:{o:e.tl,d:e.tr},rightline:{o:e.tr,d:e.br},bottomline:{o:e.br,d:e.bl},leftline:{o:e.bl,d:e.tl}};return t},_findCrossPoints:function(e,t){var n,r,i,a,o,s,c=0;for(var l in t)if(s=t[l],!(s.o.y=e.y&&s.d.y>=e.y)&&(s.o.x===s.d.x&&s.o.x>=e.x?o=s.o.x:(n=0,r=(s.d.y-s.o.y)/(s.d.x-s.o.x),i=e.y-n*e.x,a=s.o.y-r*s.o.x,o=-(i-a)/(n-r)),o>=e.x&&(c+=1),2===c))break;return c},getBoundingRect:function(e,n){var r=this.getCoords(e,n);return t.makeBoundingBoxFromPoints(r)},getScaledWidth:function(){return this._getTransformedDimensions().x},getScaledHeight:function(){return this._getTransformedDimensions().y},_constrainScale:function(e){return Math.abs(e)\n')}},toSVG:function(e){return this._createBaseSVGMarkup(this._toSVG(e),{reviver:e})},toClipPathSVG:function(e){return"\t"+this._createBaseClipPathSVGMarkup(this._toSVG(e),{reviver:e})},_createBaseClipPathSVGMarkup:function(e,t){t=t||{};var n=t.reviver,r=t.additionalTransform||"",i=[this.getSvgTransform(!0,r),this.getSvgCommons()].join(""),a=e.indexOf("COMMON_PARTS");return e[a]=i,n?n(e.join("")):e.join("")},_createBaseSVGMarkup:function(e,t){t=t||{};var n,i,a=t.noStyle,o=t.reviver,s=a?"":'style="'+this.getSvgStyles()+'" ',c=t.withShadow?'style="'+this.getSvgFilter()+'" ':"",l=this.clipPath,u=this.strokeUniform?'vector-effect="non-scaling-stroke" ':"",h=l&&l.absolutePositioned,f=this.stroke,d=this.fill,p=this.shadow,m=[],v=e.indexOf("COMMON_PARTS"),g=t.additionalTransform;return l&&(l.clipPathId="CLIPPATH_"+r.Object.__uid++,i='\n'+l.toClipPathSVG(o)+"\n"),h&&m.push("\n"),m.push("\n"),n=[s,u,a?"":this.addPaintOrder()," ",g?'transform="'+g+'" ':""].join(""),e[v]=n,d&&d.toLive&&m.push(d.toSVG(this)),f&&f.toLive&&m.push(f.toSVG(this)),p&&m.push(p.toSVG(this)),l&&m.push(i),m.push(e.join("")),m.push("\n"),h&&m.push("\n"),o?o(m.join("")):m.join("")},addPaintOrder:function(){return"fill"!==this.paintFirst?' paint-order="'+this.paintFirst+'" ':""}})}(),function(){var e=r.util.object.extend,t="stateProperties";function n(t,n,r){var i={},a=!0;r.forEach((function(e){i[e]=t[e]})),e(t[n],i,a)}function i(e,t,n){if(e===t)return!0;if(Array.isArray(e)){if(!Array.isArray(t)||e.length!==t.length)return!1;for(var r=0,a=e.length;r=0;c--)if(i=s[c],this.isControlVisible(i)&&(r=this._getImageLines(t?this.oCoords[i].touchCorner:this.oCoords[i].corner),n=this._findCrossPoints({x:a,y:o},r),0!==n&&n%2===1))return this.__corner=i,i;return!1},forEachControl:function(e){for(var t in this.controls)e(this.controls[t],t,this)},_setCornerCoords:function(){var e=this.oCoords;for(var t in e){var n=this.controls[t];e[t].corner=n.calcCornerCoords(this.angle,this.cornerSize,e[t].x,e[t].y,!1),e[t].touchCorner=n.calcCornerCoords(this.angle,this.touchCornerSize,e[t].x,e[t].y,!0)}},drawSelectionBackground:function(t){if(!this.selectionBackgroundColor||this.canvas&&!this.canvas.interactive||this.canvas&&this.canvas._activeObject!==this)return this;t.save();var n=this.getCenterPoint(),r=this._calculateCurrentDimensions(),i=this.canvas.viewportTransform;return t.translate(n.x,n.y),t.scale(1/i[0],1/i[3]),t.rotate(e(this.angle)),t.fillStyle=this.selectionBackgroundColor,t.fillRect(-r.x/2,-r.y/2,r.x,r.y),t.restore(),this},drawBorders:function(e,t){t=t||{};var n=this._calculateCurrentDimensions(),r=this.borderScaleFactor,i=n.x+r,a=n.y+r,o="undefined"!==typeof t.hasControls?t.hasControls:this.hasControls,s=!1;return e.save(),e.strokeStyle=t.borderColor||this.borderColor,this._setLineDash(e,t.borderDashArray||this.borderDashArray,null),e.strokeRect(-i/2,-a/2,i,a),o&&(e.beginPath(),this.forEachControl((function(t,n,r){t.withConnection&&t.getVisibility(r,n)&&(s=!0,e.moveTo(t.x*i,t.y*a),e.lineTo(t.x*i+t.offsetX,t.y*a+t.offsetY))})),s&&e.stroke()),e.restore(),this},drawBordersInGroup:function(e,t,n){n=n||{};var i=r.util.sizeAfterTransform(this.width,this.height,t),a=this.strokeWidth,o=this.strokeUniform,s=this.borderScaleFactor,c=i.x+a*(o?this.canvas.getZoom():t.scaleX)+s,l=i.y+a*(o?this.canvas.getZoom():t.scaleY)+s;return e.save(),this._setLineDash(e,n.borderDashArray||this.borderDashArray,null),e.strokeStyle=n.borderColor||this.borderColor,e.strokeRect(-c/2,-l/2,c,l),e.restore(),this},drawControls:function(e,t){return t=t||{},e.save(),e.setTransform(this.canvas.getRetinaScaling(),0,0,this.canvas.getRetinaScaling(),0,0),e.strokeStyle=e.fillStyle=t.cornerColor||this.cornerColor,this.transparentCorners||(e.strokeStyle=t.cornerStrokeColor||this.cornerStrokeColor),this._setLineDash(e,t.cornerDashArray||this.cornerDashArray,null),this.setCoords(),this.forEachControl((function(n,r,i){n.getVisibility(i,r)&&n.render(e,i.oCoords[r].x,i.oCoords[r].y,t,i)})),e.restore(),this},isControlVisible:function(e){return this.controls[e]&&this.controls[e].getVisibility(this,e)},setControlVisible:function(e,t){return this._controlsVisibility||(this._controlsVisibility={}),this._controlsVisibility[e]=t,this},setControlsVisibility:function(e){for(var t in e||(e={}),e)this.setControlVisible(t,e[t]);return this},onDeselect:function(){},onSelect:function(){}})}(),r.util.object.extend(r.StaticCanvas.prototype,{FX_DURATION:500,fxCenterObjectH:function(e,t){t=t||{};var n=function(){},i=t.onComplete||n,a=t.onChange||n,o=this;return r.util.animate({startValue:e.left,endValue:this.getCenter().left,duration:this.FX_DURATION,onChange:function(t){e.set("left",t),o.requestRenderAll(),a()},onComplete:function(){e.setCoords(),i()}}),this},fxCenterObjectV:function(e,t){t=t||{};var n=function(){},i=t.onComplete||n,a=t.onChange||n,o=this;return r.util.animate({startValue:e.top,endValue:this.getCenter().top,duration:this.FX_DURATION,onChange:function(t){e.set("top",t),o.requestRenderAll(),a()},onComplete:function(){e.setCoords(),i()}}),this},fxRemove:function(e,t){t=t||{};var n=function(){},i=t.onComplete||n,a=t.onChange||n,o=this;return r.util.animate({startValue:e.opacity,endValue:0,duration:this.FX_DURATION,onChange:function(t){e.set("opacity",t),o.requestRenderAll(),a()},onComplete:function(){o.remove(e),i()}}),this}}),r.util.object.extend(r.Object.prototype,{animate:function(){if(arguments[0]&&"object"===typeof arguments[0]){var e,t,n=[];for(e in arguments[0])n.push(e);for(var r=0,i=n.length;r-1||a&&o.colorProperties.indexOf(a[1])>-1,c=a?this.get(a[0])[a[1]]:this.get(e);"from"in n||(n.from=c),s||(t=~t.indexOf("=")?c+parseFloat(t.replace("=","")):parseFloat(t));var l={startValue:n.from,endValue:t,byValue:n.by,easing:n.easing,duration:n.duration,abort:n.abort&&function(){return n.abort.call(o)},onChange:function(t,r,s){a?o[a[0]][a[1]]=t:o.set(e,t),i||n.onChange&&n.onChange(t,r,s)},onComplete:function(e,t,r){i||(o.setCoords(),n.onComplete&&n.onComplete(e,t,r))}};s?r.util.animateColor(l.startValue,l.endValue,l.duration,l):r.util.animate(l)}}),function(e){"use strict";var t=e.fabric||(e.fabric={}),n=t.util.object.extend,r=t.util.object.clone,i={x1:1,x2:1,y1:1,y2:1},a=t.StaticCanvas.supports("setLineDash");function o(e,t){var n=e.origin,r=e.axis1,i=e.axis2,a=e.dimension,o=t.nearest,s=t.center,c=t.farthest;return function(){switch(this.get(n)){case o:return Math.min(this.get(r),this.get(i));case s:return Math.min(this.get(r),this.get(i))+.5*this.get(a);case c:return Math.max(this.get(r),this.get(i))}}}t.Line?t.warn("fabric.Line is already defined"):(t.Line=t.util.createClass(t.Object,{type:"line",x1:0,y1:0,x2:0,y2:0,cacheProperties:t.Object.prototype.cacheProperties.concat("x1","x2","y1","y2"),initialize:function(e,t){e||(e=[0,0,0,0]),this.callSuper("initialize",t),this.set("x1",e[0]),this.set("y1",e[1]),this.set("x2",e[2]),this.set("y2",e[3]),this._setWidthHeight(t)},_setWidthHeight:function(e){e||(e={}),this.width=Math.abs(this.x2-this.x1),this.height=Math.abs(this.y2-this.y1),this.left="left"in e?e.left:this._getLeftToOriginX(),this.top="top"in e?e.top:this._getTopToOriginY()},_set:function(e,t){return this.callSuper("_set",e,t),"undefined"!==typeof i[e]&&this._setWidthHeight(),this},_getLeftToOriginX:o({origin:"originX",axis1:"x1",axis2:"x2",dimension:"width"},{nearest:"left",center:"center",farthest:"right"}),_getTopToOriginY:o({origin:"originY",axis1:"y1",axis2:"y2",dimension:"height"},{nearest:"top",center:"center",farthest:"bottom"}),_render:function(e){if(e.beginPath(),!this.strokeDashArray||this.strokeDashArray&&a){var t=this.calcLinePoints();e.moveTo(t.x1,t.y1),e.lineTo(t.x2,t.y2)}e.lineWidth=this.strokeWidth;var n=e.strokeStyle;e.strokeStyle=this.stroke||e.fillStyle,this.stroke&&this._renderStroke(e),e.strokeStyle=n},_renderDashedStroke:function(e){var n=this.calcLinePoints();e.beginPath(),t.util.drawDashedLine(e,n.x1,n.y1,n.x2,n.y2,this.strokeDashArray),e.closePath()},_findCenterFromElement:function(){return{x:(this.x1+this.x2)/2,y:(this.y1+this.y2)/2}},toObject:function(e){return n(this.callSuper("toObject",e),this.calcLinePoints())},_getNonTransformedDimensions:function(){var e=this.callSuper("_getNonTransformedDimensions");return"butt"===this.strokeLineCap&&(0===this.width&&(e.y-=this.strokeWidth),0===this.height&&(e.x-=this.strokeWidth)),e},calcLinePoints:function(){var e=this.x1<=this.x2?-1:1,t=this.y1<=this.y2?-1:1,n=e*this.width*.5,r=t*this.height*.5,i=e*this.width*-.5,a=t*this.height*-.5;return{x1:n,x2:i,y1:r,y2:a}},_toSVG:function(){var e=this.calcLinePoints();return["\n']}}),t.Line.ATTRIBUTE_NAMES=t.SHARED_ATTRIBUTES.concat("x1 y1 x2 y2".split(" ")),t.Line.fromElement=function(e,r,i){i=i||{};var a=t.parseAttributes(e,t.Line.ATTRIBUTE_NAMES),o=[a.x1||0,a.y1||0,a.x2||0,a.y2||0];r(new t.Line(o,n(a,i)))},t.Line.fromObject=function(e,n){function i(e){delete e.points,n&&n(e)}var a=r(e,!0);a.points=[e.x1,e.y1,e.x2,e.y2],t.Object._fromObject("Line",a,i,"points")})}(t),function(e){"use strict";var t=e.fabric||(e.fabric={}),n=Math.PI;function r(e){return"radius"in e&&e.radius>=0}t.Circle?t.warn("fabric.Circle is already defined."):(t.Circle=t.util.createClass(t.Object,{type:"circle",radius:0,startAngle:0,endAngle:2*n,cacheProperties:t.Object.prototype.cacheProperties.concat("radius","startAngle","endAngle"),_set:function(e,t){return this.callSuper("_set",e,t),"radius"===e&&this.setRadius(t),this},toObject:function(e){return this.callSuper("toObject",["radius","startAngle","endAngle"].concat(e))},_toSVG:function(){var e,r=0,i=0,a=(this.endAngle-this.startAngle)%(2*n);if(0===a)e=["\n'];else{var o=t.util.cos(this.startAngle)*this.radius,s=t.util.sin(this.startAngle)*this.radius,c=t.util.cos(this.endAngle)*this.radius,l=t.util.sin(this.endAngle)*this.radius,u=a>n?"1":"0";e=['\n"]}return e},_render:function(e){e.beginPath(),e.arc(0,0,this.radius,this.startAngle,this.endAngle,!1),this._renderPaintInOrder(e)},getRadiusX:function(){return this.get("radius")*this.get("scaleX")},getRadiusY:function(){return this.get("radius")*this.get("scaleY")},setRadius:function(e){return this.radius=e,this.set("width",2*e).set("height",2*e)}}),t.Circle.ATTRIBUTE_NAMES=t.SHARED_ATTRIBUTES.concat("cx cy r".split(" ")),t.Circle.fromElement=function(e,n){var i=t.parseAttributes(e,t.Circle.ATTRIBUTE_NAMES);if(!r(i))throw new Error("value of `r` attribute is required and can not be negative");i.left=(i.left||0)-i.radius,i.top=(i.top||0)-i.radius,n(new t.Circle(i))},t.Circle.fromObject=function(e,n){return t.Object._fromObject("Circle",e,n)})}(t),function(e){"use strict";var t=e.fabric||(e.fabric={});t.Triangle?t.warn("fabric.Triangle is already defined"):(t.Triangle=t.util.createClass(t.Object,{type:"triangle",width:100,height:100,_render:function(e){var t=this.width/2,n=this.height/2;e.beginPath(),e.moveTo(-t,n),e.lineTo(0,-n),e.lineTo(t,n),e.closePath(),this._renderPaintInOrder(e)},_renderDashedStroke:function(e){var n=this.width/2,r=this.height/2;e.beginPath(),t.util.drawDashedLine(e,-n,r,0,-r,this.strokeDashArray),t.util.drawDashedLine(e,0,-r,n,r,this.strokeDashArray),t.util.drawDashedLine(e,n,r,-n,r,this.strokeDashArray),e.closePath()},_toSVG:function(){var e=this.width/2,t=this.height/2,n=[-e+" "+t,"0 "+-t,e+" "+t].join(",");return["']}}),t.Triangle.fromObject=function(e,n){return t.Object._fromObject("Triangle",e,n)})}(t),function(e){"use strict";var t=e.fabric||(e.fabric={}),n=2*Math.PI;t.Ellipse?t.warn("fabric.Ellipse is already defined."):(t.Ellipse=t.util.createClass(t.Object,{type:"ellipse",rx:0,ry:0,cacheProperties:t.Object.prototype.cacheProperties.concat("rx","ry"),initialize:function(e){this.callSuper("initialize",e),this.set("rx",e&&e.rx||0),this.set("ry",e&&e.ry||0)},_set:function(e,t){switch(this.callSuper("_set",e,t),e){case"rx":this.rx=t,this.set("width",2*t);break;case"ry":this.ry=t,this.set("height",2*t);break}return this},getRx:function(){return this.get("rx")*this.get("scaleX")},getRy:function(){return this.get("ry")*this.get("scaleY")},toObject:function(e){return this.callSuper("toObject",["rx","ry"].concat(e))},_toSVG:function(){return["\n']},_render:function(e){e.beginPath(),e.save(),e.transform(1,0,0,this.ry/this.rx,0,0),e.arc(0,0,this.rx,0,n,!1),e.restore(),this._renderPaintInOrder(e)}}),t.Ellipse.ATTRIBUTE_NAMES=t.SHARED_ATTRIBUTES.concat("cx cy rx ry".split(" ")),t.Ellipse.fromElement=function(e,n){var r=t.parseAttributes(e,t.Ellipse.ATTRIBUTE_NAMES);r.left=(r.left||0)-r.rx,r.top=(r.top||0)-r.ry,n(new t.Ellipse(r))},t.Ellipse.fromObject=function(e,n){return t.Object._fromObject("Ellipse",e,n)})}(t),function(e){"use strict";var t=e.fabric||(e.fabric={}),n=t.util.object.extend;t.Rect?t.warn("fabric.Rect is already defined"):(t.Rect=t.util.createClass(t.Object,{stateProperties:t.Object.prototype.stateProperties.concat("rx","ry"),type:"rect",rx:0,ry:0,cacheProperties:t.Object.prototype.cacheProperties.concat("rx","ry"),initialize:function(e){this.callSuper("initialize",e),this._initRxRy()},_initRxRy:function(){this.rx&&!this.ry?this.ry=this.rx:this.ry&&!this.rx&&(this.rx=this.ry)},_render:function(e){var t=this.rx?Math.min(this.rx,this.width/2):0,n=this.ry?Math.min(this.ry,this.height/2):0,r=this.width,i=this.height,a=-this.width/2,o=-this.height/2,s=0!==t||0!==n,c=.4477152502;e.beginPath(),e.moveTo(a+t,o),e.lineTo(a+r-t,o),s&&e.bezierCurveTo(a+r-c*t,o,a+r,o+c*n,a+r,o+n),e.lineTo(a+r,o+i-n),s&&e.bezierCurveTo(a+r,o+i-c*n,a+r-c*t,o+i,a+r-t,o+i),e.lineTo(a+t,o+i),s&&e.bezierCurveTo(a+c*t,o+i,a,o+i-c*n,a,o+i-n),e.lineTo(a,o+n),s&&e.bezierCurveTo(a,o+c*n,a+c*t,o,a+t,o),e.closePath(),this._renderPaintInOrder(e)},_renderDashedStroke:function(e){var n=-this.width/2,r=-this.height/2,i=this.width,a=this.height;e.beginPath(),t.util.drawDashedLine(e,n,r,n+i,r,this.strokeDashArray),t.util.drawDashedLine(e,n+i,r,n+i,r+a,this.strokeDashArray),t.util.drawDashedLine(e,n+i,r+a,n,r+a,this.strokeDashArray),t.util.drawDashedLine(e,n,r+a,n,r,this.strokeDashArray),e.closePath()},toObject:function(e){return this.callSuper("toObject",["rx","ry"].concat(e))},_toSVG:function(){var e=-this.width/2,t=-this.height/2;return["\n']}}),t.Rect.ATTRIBUTE_NAMES=t.SHARED_ATTRIBUTES.concat("x y rx ry width height".split(" ")),t.Rect.fromElement=function(e,r,i){if(!e)return r(null);i=i||{};var a=t.parseAttributes(e,t.Rect.ATTRIBUTE_NAMES);a.left=a.left||0,a.top=a.top||0,a.height=a.height||0,a.width=a.width||0;var o=new t.Rect(n(i?t.util.object.clone(i):{},a));o.visible=o.visible&&o.width>0&&o.height>0,r(o)},t.Rect.fromObject=function(e,n){return t.Object._fromObject("Rect",e,n)})}(t),function(e){"use strict";var t=e.fabric||(e.fabric={}),n=t.util.object.extend,r=t.util.array.min,i=t.util.array.max,a=t.util.toFixed;t.Polyline?t.warn("fabric.Polyline is already defined"):(t.Polyline=t.util.createClass(t.Object,{type:"polyline",points:null,cacheProperties:t.Object.prototype.cacheProperties.concat("points"),initialize:function(e,t){t=t||{},this.points=e||[],this.callSuper("initialize",t),this._setPositionDimensions(t)},_setPositionDimensions:function(e){var t,n=this._calcDimensions(e);this.width=n.width,this.height=n.height,e.fromSVG||(t=this.translateToGivenOrigin({x:n.left-this.strokeWidth/2,y:n.top-this.strokeWidth/2},"left","top",this.originX,this.originY)),"undefined"===typeof e.left&&(this.left=e.fromSVG?n.left:t.x),"undefined"===typeof e.top&&(this.top=e.fromSVG?n.top:t.y),this.pathOffset={x:n.left+this.width/2,y:n.top+this.height/2}},_calcDimensions:function(){var e=this.points,t=r(e,"x")||0,n=r(e,"y")||0,a=i(e,"x")||0,o=i(e,"y")||0,s=a-t,c=o-n;return{left:t,top:n,width:s,height:c}},toObject:function(e){return n(this.callSuper("toObject",e),{points:this.points.concat()})},_toSVG:function(){for(var e=[],n=this.pathOffset.x,r=this.pathOffset.y,i=t.Object.NUM_FRACTION_DIGITS,o=0,s=this.points.length;o\n']},commonRender:function(e){var t,n=this.points.length,r=this.pathOffset.x,i=this.pathOffset.y;if(!n||isNaN(this.points[n-1].y))return!1;e.beginPath(),e.moveTo(this.points[0].x-r,this.points[0].y-i);for(var a=0;a"},toObject:function(e){return i(this.callSuper("toObject",e),{path:this.path.map((function(e){return e.slice()}))})},toDatalessObject:function(e){var t=this.toObject(["sourcePath"].concat(e));return t.sourcePath&&delete t.path,t},_toSVG:function(){var e=this.path.map((function(e){return e.join(" ")})).join(" ");return["\n"]},_getOffsetTransform:function(){var e=t.Object.NUM_FRACTION_DIGITS;return" translate("+o(-this.pathOffset.x,e)+", "+o(-this.pathOffset.y,e)+")"},toClipPathSVG:function(e){var t=this._getOffsetTransform();return"\t"+this._createBaseClipPathSVGMarkup(this._toSVG(),{reviver:e,additionalTransform:t})},toSVG:function(e){var t=this._getOffsetTransform();return this._createBaseSVGMarkup(this._toSVG(),{reviver:e,additionalTransform:t})},complexity:function(){return this.path.length},_calcDimensions:function(){for(var e,i,a=[],o=[],s=0,c=0,l=0,u=0,h=0,f=this.path.length;h"},addWithUpdate:function(e){return this._restoreObjectsState(),t.util.resetObjectTransform(this),e&&(this._objects.push(e),e.group=this,e._set("canvas",this.canvas)),this._calcBounds(),this._updateObjectsCoords(),this.setCoords(),this.dirty=!0,this},removeWithUpdate:function(e){return this._restoreObjectsState(),t.util.resetObjectTransform(this),this.remove(e),this._calcBounds(),this._updateObjectsCoords(),this.setCoords(),this.dirty=!0,this},_onObjectAdded:function(e){this.dirty=!0,e.group=this,e._set("canvas",this.canvas)},_onObjectRemoved:function(e){this.dirty=!0,delete e.group},_set:function(e,n){var r=this._objects.length;if(this.useSetOnGroup)while(r--)this._objects[r].setOnGroup(e,n);if("canvas"===e)while(r--)this._objects[r]._set(e,n);t.Object.prototype._set.call(this,e,n)},toObject:function(e){var n=this.includeDefaultValues,r=this._objects.map((function(t){var r=t.includeDefaultValues;t.includeDefaultValues=n;var i=t.toObject(e);return t.includeDefaultValues=r,i})),i=t.Object.prototype.toObject.call(this,e);return i.objects=r,i},toDatalessObject:function(e){var n,r=this.sourcePath;if(r)n=r;else{var i=this.includeDefaultValues;n=this._objects.map((function(t){var n=t.includeDefaultValues;t.includeDefaultValues=i;var r=t.toDatalessObject(e);return t.includeDefaultValues=n,r}))}var a=t.Object.prototype.toDatalessObject.call(this,e);return a.objects=n,a},render:function(e){this._transformDone=!0,this.callSuper("render",e),this._transformDone=!1},shouldCache:function(){var e=t.Object.prototype.shouldCache.call(this);if(e)for(var n=0,r=this._objects.length;n\n"],n=0,r=this._objects.length;n\n"),t},getSvgStyles:function(){var e="undefined"!==typeof this.opacity&&1!==this.opacity?"opacity: "+this.opacity+";":"",t=this.visible?"":" visibility: hidden;";return[e,this.getSvgFilter(),t].join("")},toClipPathSVG:function(e){for(var t=[],n=0,r=this._objects.length;n"},shouldCache:function(){return!1},isOnACache:function(){return!1},_renderControls:function(e,t,n){e.save(),e.globalAlpha=this.isMoving?this.borderOpacityWhenMoving:1,this.callSuper("_renderControls",e,t),n=n||{},"undefined"===typeof n.hasControls&&(n.hasControls=!1),n.forActiveSelection=!0;for(var r=0,i=this._objects.length;r\n','\t\n',"\n"),s=' clip-path="url(#imageCrop_'+l+')" '}if(this.imageSmoothing||(c='" image-rendering="optimizeSpeed'),n.push("\t\n"),this.stroke||this.strokeDashArray){var u=this.fill;this.fill=null,e=["\t\n'],this.fill=u}return t="fill"!==this.paintFirst?t.concat(e,n):t.concat(n,e),t},getSrc:function(e){var t=e?this._element:this._originalElement;return t?t.toDataURL?t.toDataURL():this.srcFromAttribute?t.getAttribute("src"):t.src:this.src||""},setSrc:function(e,t,n){return r.util.loadImage(e,(function(e,r){this.setElement(e,n),this._setWidthHeight(),t&&t(this,r)}),this,n&&n.crossOrigin),this},toString:function(){return'#'},applyResizeFilters:function(){var e=this.resizeFilter,t=this.minimumScaleTrigger,n=this.getTotalObjectScaling(),i=n.scaleX,a=n.scaleY,o=this._filteredEl||this._originalElement;if(this.group&&this.set("dirty",!0),!e||i>t&&a>t)return this._element=o,this._filterScalingX=1,this._filterScalingY=1,this._lastScaleX=i,void(this._lastScaleY=a);r.filterBackend||(r.filterBackend=r.initFilterBackend());var s=r.util.createCanvasElement(),c=this._filteredEl?this.cacheKey+"_filtered":this.cacheKey,l=o.width,u=o.height;s.width=l,s.height=u,this._element=s,this._lastScaleX=e.scaleX=i,this._lastScaleY=e.scaleY=a,r.filterBackend.applyFilters([e],o,l,u,this._element,c),this._filterScalingX=s.width/this._originalElement.width,this._filterScalingY=s.height/this._originalElement.height},applyFilters:function(e){if(e=e||this.filters||[],e=e.filter((function(e){return e&&!e.isNeutralState()})),this.set("dirty",!0),this.removeTexture(this.cacheKey+"_filtered"),0===e.length)return this._element=this._originalElement,this._filteredEl=null,this._filterScalingX=1,this._filterScalingY=1,this;var t=this._originalElement,n=t.naturalWidth||t.width,i=t.naturalHeight||t.height;if(this._element===this._originalElement){var a=r.util.createCanvasElement();a.width=n,a.height=i,this._element=a,this._filteredEl=a}else this._element=this._filteredEl,this._filteredEl.getContext("2d").clearRect(0,0,n,i),this._lastScaleX=1,this._lastScaleY=1;return r.filterBackend||(r.filterBackend=r.initFilterBackend()),r.filterBackend.applyFilters(e,this._originalElement,n,i,this._element,this.cacheKey),this._originalElement.width===this._element.width&&this._originalElement.height===this._element.height||(this._filterScalingX=this._element.width/this._originalElement.width,this._filterScalingY=this._element.height/this._originalElement.height),this},_render:function(e){r.util.setImageSmoothing(e,this.imageSmoothing),!0!==this.isMoving&&this.resizeFilter&&this._needsResize()&&this.applyResizeFilters(),this._stroke(e),this._renderPaintInOrder(e)},drawCacheOnCanvas:function(e){r.util.setImageSmoothing(e,this.imageSmoothing),r.Object.prototype.drawCacheOnCanvas.call(this,e)},shouldCache:function(){return this.needsItsOwnCache()},_renderFill:function(e){var t=this._element;if(t){var n=this._filterScalingX,r=this._filterScalingY,i=this.width,a=this.height,o=Math.min,s=Math.max,c=s(this.cropX,0),l=s(this.cropY,0),u=t.naturalWidth||t.width,h=t.naturalHeight||t.height,f=c*n,d=l*r,p=o(i*n,u-f),m=o(a*r,h-d),v=-i/2,g=-a/2,b=o(i,u/n-c),y=o(a,h/n-l);t&&e.drawImage(t,f,d,p,m,v,g,b,y)}},_needsResize:function(){var e=this.getTotalObjectScaling();return e.scaleX!==this._lastScaleX||e.scaleY!==this._lastScaleY},_resetWidthHeight:function(){this.set(this.getOriginalSize())},_initElement:function(e,t){this.setElement(r.util.getById(e),t),r.util.addClass(this.getElement(),r.Image.CSS_CANVAS)},_initConfig:function(e){e||(e={}),this.setOptions(e),this._setWidthHeight(e)},_initFilters:function(e,t){e&&e.length?r.util.enlivenObjects(e,(function(e){t&&t(e)}),"fabric.Image.filters"):t&&t()},_setWidthHeight:function(e){e||(e={});var t=this.getElement();this.width=e.width||t.naturalWidth||t.width||0,this.height=e.height||t.naturalHeight||t.height||0},parsePreserveAspectRatioAttribute:function(){var e,t=r.util.parsePreserveAspectRatioAttribute(this.preserveAspectRatio||""),n=this._element.width,i=this._element.height,a=1,o=1,s=0,c=0,l=0,u=0,h=this.width,f=this.height,d={width:h,height:f};return!t||"none"===t.alignX&&"none"===t.alignY?(a=h/n,o=f/i):("meet"===t.meetOrSlice&&(a=o=r.util.findScaleToFit(this._element,d),e=(h-n*a)/2,"Min"===t.alignX&&(s=-e),"Max"===t.alignX&&(s=e),e=(f-i*o)/2,"Min"===t.alignY&&(c=-e),"Max"===t.alignY&&(c=e)),"slice"===t.meetOrSlice&&(a=o=r.util.findScaleToCover(this._element,d),e=n-h/a,"Mid"===t.alignX&&(l=e/2),"Max"===t.alignX&&(l=e),e=i-f/o,"Mid"===t.alignY&&(u=e/2),"Max"===t.alignY&&(u=e),n=h/a,i=f/o)),{width:n,height:i,scaleX:a,scaleY:o,offsetLeft:s,offsetTop:c,cropX:l,cropY:u}}}),r.Image.CSS_CANVAS="canvas-img",r.Image.prototype.getSvgSrc=r.Image.prototype.getSrc,r.Image.fromObject=function(e,t){var n=r.util.object.clone(e);r.util.loadImage(n.src,(function(e,i){i?t&&t(null,!0):r.Image.prototype._initFilters.call(n,n.filters,(function(i){n.filters=i||[],r.Image.prototype._initFilters.call(n,[n.resizeFilter],(function(i){n.resizeFilter=i[0],r.util.enlivenObjects([n.clipPath],(function(i){n.clipPath=i[0];var a=new r.Image(e,n);t(a,!1)}))}))}))}),null,n.crossOrigin)},r.Image.fromURL=function(e,t,n){r.util.loadImage(e,(function(e,i){t&&t(new r.Image(e,n),i)}),null,n&&n.crossOrigin)},r.Image.ATTRIBUTE_NAMES=r.SHARED_ATTRIBUTES.concat("x y width height preserveAspectRatio xlink:href crossOrigin image-rendering".split(" ")),r.Image.fromElement=function(e,n,i){var a=r.parseAttributes(e,r.Image.ATTRIBUTE_NAMES);r.Image.fromURL(a["xlink:href"],n,t(i?r.util.object.clone(i):{},a))})}(t),r.util.object.extend(r.Object.prototype,{_getAngleValueForStraighten:function(){var e=this.angle%360;return e>0?90*Math.round((e-1)/90):90*Math.round(e/90)},straighten:function(){return this.rotate(this._getAngleValueForStraighten()),this},fxStraighten:function(e){e=e||{};var t=function(){},n=e.onComplete||t,i=e.onChange||t,a=this;return r.util.animate({startValue:this.get("angle"),endValue:this._getAngleValueForStraighten(),duration:this.FX_DURATION,onChange:function(e){a.rotate(e),i()},onComplete:function(){a.setCoords(),n()}}),this}}),r.util.object.extend(r.StaticCanvas.prototype,{straightenObject:function(e){return e.straighten(),this.requestRenderAll(),this},fxStraightenObject:function(e){return e.fxStraighten({onChange:this.requestRenderAllBound}),this}}),function(){"use strict";function e(e,t){var n="precision "+t+" float;\nvoid main(){}",r=e.createShader(e.FRAGMENT_SHADER);return e.shaderSource(r,n),e.compileShader(r),!!e.getShaderParameter(r,e.COMPILE_STATUS)}function t(e){e&&e.tileSize&&(this.tileSize=e.tileSize),this.setupGLContext(this.tileSize,this.tileSize),this.captureGPUInfo()}r.isWebglSupported=function(t){if(r.isLikelyNode)return!1;t=t||r.WebglFilterBackend.prototype.tileSize;var n=document.createElement("canvas"),i=n.getContext("webgl")||n.getContext("experimental-webgl"),a=!1;if(i){r.maxTextureSize=i.getParameter(i.MAX_TEXTURE_SIZE),a=r.maxTextureSize>=t;for(var o=["highp","mediump","lowp"],s=0;s<3;s++)if(e(i,o[s])){r.webGlPrecision=o[s];break}}return this.isSupported=a,a},r.WebglFilterBackend=t,t.prototype={tileSize:2048,resources:{},setupGLContext:function(e,t){this.dispose(),this.createWebGLCanvas(e,t),this.aPosition=new Float32Array([0,0,0,1,1,0,1,1]),this.chooseFastestCopyGLTo2DMethod(e,t)},chooseFastestCopyGLTo2DMethod:function(e,t){var n,i="undefined"!==typeof window.performance;try{new ImageData(1,1),n=!0}catch(m){n=!1}var a="undefined"!==typeof ArrayBuffer,o="undefined"!==typeof Uint8ClampedArray;if(i&&n&&a&&o){var l=r.util.createCanvasElement(),u=new ArrayBuffer(e*t*4);if(r.forceGLPutImageData)return this.imageBuffer=u,void(this.copyGLTo2D=c);var h,f,d,p={imageBuffer:u,destinationWidth:e,destinationHeight:t,targetCanvas:l};l.width=e,l.height=t,h=window.performance.now(),s.call(p,this.gl,p),f=window.performance.now()-h,h=window.performance.now(),c.call(p,this.gl,p),d=window.performance.now()-h,f>d?(this.imageBuffer=u,this.copyGLTo2D=c):this.copyGLTo2D=s}},createWebGLCanvas:function(e,t){var n=r.util.createCanvasElement();n.width=e,n.height=t;var i={alpha:!0,premultipliedAlpha:!1,depth:!1,stencil:!1,antialias:!1},a=n.getContext("webgl",i);a||(a=n.getContext("experimental-webgl",i)),a&&(a.clearColor(0,0,0,0),this.canvas=n,this.gl=a)},applyFilters:function(e,t,n,r,i,a){var s,c=this.gl;a&&(s=this.getCachedTexture(a,t));var l={originalWidth:t.width||t.originalWidth,originalHeight:t.height||t.originalHeight,sourceWidth:n,sourceHeight:r,destinationWidth:n,destinationHeight:r,context:c,sourceTexture:this.createTexture(c,n,r,!s&&t),targetTexture:this.createTexture(c,n,r),originalTexture:s||this.createTexture(c,n,r,!s&&t),passes:e.length,webgl:!0,aPosition:this.aPosition,programCache:this.programCache,pass:0,filterBackend:this,targetCanvas:i},u=c.createFramebuffer();return c.bindFramebuffer(c.FRAMEBUFFER,u),e.forEach((function(e){e&&e.applyTo(l)})),o(l),this.copyGLTo2D(c,l),c.bindTexture(c.TEXTURE_2D,null),c.deleteTexture(l.sourceTexture),c.deleteTexture(l.targetTexture),c.deleteFramebuffer(u),i.getContext("2d").setTransform(1,0,0,1,0,0),l},dispose:function(){this.canvas&&(this.canvas=null,this.gl=null),this.clearWebGLCaches()},clearWebGLCaches:function(){this.programCache={},this.textureCache={}},createTexture:function(e,t,n,r){var i=e.createTexture();return e.bindTexture(e.TEXTURE_2D,i),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.NEAREST),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.NEAREST),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE),r?e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,r):e.texImage2D(e.TEXTURE_2D,0,e.RGBA,t,n,0,e.RGBA,e.UNSIGNED_BYTE,null),i},getCachedTexture:function(e,t){if(this.textureCache[e])return this.textureCache[e];var n=this.createTexture(this.gl,t.width,t.height,t);return this.textureCache[e]=n,n},evictCachesForKey:function(e){this.textureCache[e]&&(this.gl.deleteTexture(this.textureCache[e]),delete this.textureCache[e])},copyGLTo2D:s,captureGPUInfo:function(){if(this.gpuInfo)return this.gpuInfo;var e=this.gl,t={renderer:"",vendor:""};if(!e)return t;var n=e.getExtension("WEBGL_debug_renderer_info");if(n){var r=e.getParameter(n.UNMASKED_RENDERER_WEBGL),i=e.getParameter(n.UNMASKED_VENDOR_WEBGL);r&&(t.renderer=r.toLowerCase()),i&&(t.vendor=i.toLowerCase())}return this.gpuInfo=t,t}}}(),function(){"use strict";var e=function(){};function t(){}r.Canvas2dFilterBackend=t,t.prototype={evictCachesForKey:e,dispose:e,clearWebGLCaches:e,resources:{},applyFilters:function(e,t,n,r,i){var a=i.getContext("2d");a.drawImage(t,0,0,n,r);var o=a.getImageData(0,0,n,r),s=a.getImageData(0,0,n,r),c={sourceWidth:n,sourceHeight:r,imageData:o,originalEl:t,originalImageData:s,canvasEl:i,ctx:a,filterBackend:this};return e.forEach((function(e){e.applyTo(c)})),c.imageData.width===n&&c.imageData.height===r||(i.width=c.imageData.width,i.height=c.imageData.height),a.putImageData(c.imageData,0,0),c}}}(),r.Image=r.Image||{},r.Image.filters=r.Image.filters||{},r.Image.filters.BaseFilter=r.util.createClass({type:"BaseFilter",vertexSource:"attribute vec2 aPosition;\nvarying vec2 vTexCoord;\nvoid main() {\nvTexCoord = aPosition;\ngl_Position = vec4(aPosition * 2.0 - 1.0, 0.0, 1.0);\n}",fragmentSource:"precision highp float;\nvarying vec2 vTexCoord;\nuniform sampler2D uTexture;\nvoid main() {\ngl_FragColor = texture2D(uTexture, vTexCoord);\n}",initialize:function(e){e&&this.setOptions(e)},setOptions:function(e){for(var t in e)this[t]=e[t]},createProgram:function(e,t,n){t=t||this.fragmentSource,n=n||this.vertexSource,"highp"!==r.webGlPrecision&&(t=t.replace(/precision highp float/g,"precision "+r.webGlPrecision+" float"));var i=e.createShader(e.VERTEX_SHADER);if(e.shaderSource(i,n),e.compileShader(i),!e.getShaderParameter(i,e.COMPILE_STATUS))throw new Error("Vertex shader compile error for "+this.type+": "+e.getShaderInfoLog(i));var a=e.createShader(e.FRAGMENT_SHADER);if(e.shaderSource(a,t),e.compileShader(a),!e.getShaderParameter(a,e.COMPILE_STATUS))throw new Error("Fragment shader compile error for "+this.type+": "+e.getShaderInfoLog(a));var o=e.createProgram();if(e.attachShader(o,i),e.attachShader(o,a),e.linkProgram(o),!e.getProgramParameter(o,e.LINK_STATUS))throw new Error('Shader link error for "${this.type}" '+e.getProgramInfoLog(o));var s=this.getAttributeLocations(e,o),c=this.getUniformLocations(e,o)||{};return c.uStepW=e.getUniformLocation(o,"uStepW"),c.uStepH=e.getUniformLocation(o,"uStepH"),{program:o,attributeLocations:s,uniformLocations:c}},getAttributeLocations:function(e,t){return{aPosition:e.getAttribLocation(t,"aPosition")}},getUniformLocations:function(){return{}},sendAttributeData:function(e,t,n){var r=t.aPosition,i=e.createBuffer();e.bindBuffer(e.ARRAY_BUFFER,i),e.enableVertexAttribArray(r),e.vertexAttribPointer(r,2,e.FLOAT,!1,0,0),e.bufferData(e.ARRAY_BUFFER,n,e.STATIC_DRAW)},_setupFrameBuffer:function(e){var t,n,r=e.context;e.passes>1?(t=e.destinationWidth,n=e.destinationHeight,e.sourceWidth===t&&e.sourceHeight===n||(r.deleteTexture(e.targetTexture),e.targetTexture=e.filterBackend.createTexture(r,t,n)),r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,e.targetTexture,0)):(r.bindFramebuffer(r.FRAMEBUFFER,null),r.finish())},_swapTextures:function(e){e.passes--,e.pass++;var t=e.targetTexture;e.targetTexture=e.sourceTexture,e.sourceTexture=t},isNeutralState:function(){var e=this.mainParameter,t=r.Image.filters[this.type].prototype;if(e){if(Array.isArray(t[e])){for(var n=t[e].length;n--;)if(this[e][n]!==t[e][n])return!1;return!0}return t[e]===this[e]}return!1},applyTo:function(e){e.webgl?(this._setupFrameBuffer(e),this.applyToWebGL(e),this._swapTextures(e)):this.applyTo2d(e)},retrieveShader:function(e){return e.programCache.hasOwnProperty(this.type)||(e.programCache[this.type]=this.createProgram(e.context)),e.programCache[this.type]},applyToWebGL:function(e){var t=e.context,n=this.retrieveShader(e);0===e.pass&&e.originalTexture?t.bindTexture(t.TEXTURE_2D,e.originalTexture):t.bindTexture(t.TEXTURE_2D,e.sourceTexture),t.useProgram(n.program),this.sendAttributeData(t,n.attributeLocations,e.aPosition),t.uniform1f(n.uniformLocations.uStepW,1/e.sourceWidth),t.uniform1f(n.uniformLocations.uStepH,1/e.sourceHeight),this.sendUniformData(t,n.uniformLocations),t.viewport(0,0,e.destinationWidth,e.destinationHeight),t.drawArrays(t.TRIANGLE_STRIP,0,4)},bindAdditionalTexture:function(e,t,n){e.activeTexture(n),e.bindTexture(e.TEXTURE_2D,t),e.activeTexture(e.TEXTURE0)},unbindAdditionalTexture:function(e,t){e.activeTexture(t),e.bindTexture(e.TEXTURE_2D,null),e.activeTexture(e.TEXTURE0)},getMainParameter:function(){return this[this.mainParameter]},setMainParameter:function(e){this[this.mainParameter]=e},sendUniformData:function(){},createHelpLayer:function(e){if(!e.helpLayer){var t=document.createElement("canvas");t.width=e.sourceWidth,t.height=e.sourceHeight,e.helpLayer=t}},toObject:function(){var e={type:this.type},t=this.mainParameter;return t&&(e[t]=this[t]),e},toJSON:function(){return this.toObject()}}),r.Image.filters.BaseFilter.fromObject=function(e,t){var n=new r.Image.filters[e.type](e);return t&&t(n),n},function(e){"use strict";var t=e.fabric||(e.fabric={}),n=t.Image.filters,r=t.util.createClass;n.ColorMatrix=r(n.BaseFilter,{type:"ColorMatrix",fragmentSource:"precision highp float;\nuniform sampler2D uTexture;\nvarying vec2 vTexCoord;\nuniform mat4 uColorMatrix;\nuniform vec4 uConstants;\nvoid main() {\nvec4 color = texture2D(uTexture, vTexCoord);\ncolor *= uColorMatrix;\ncolor += uConstants;\ngl_FragColor = color;\n}",matrix:[1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0],mainParameter:"matrix",colorsOnly:!0,initialize:function(e){this.callSuper("initialize",e),this.matrix=this.matrix.slice(0)},applyTo2d:function(e){var t,n,r,i,a,o=e.imageData,s=o.data,c=s.length,l=this.matrix,u=this.colorsOnly;for(a=0;a=_||o<0||o>=y||(c=4*(s*y+o),l=v[d*g+f],t+=m[c]*l,n+=m[c+1]*l,r+=m[c+2]*l,x||(i+=m[c+3]*l));M[a]=t,M[a+1]=n,M[a+2]=r,M[a+3]=x?m[a+3]:i}e.imageData=w},getUniformLocations:function(e,t){return{uMatrix:e.getUniformLocation(t,"uMatrix"),uOpaque:e.getUniformLocation(t,"uOpaque"),uHalfSize:e.getUniformLocation(t,"uHalfSize"),uSize:e.getUniformLocation(t,"uSize")}},sendUniformData:function(e,t){e.uniform1fv(t.uMatrix,this.matrix)},toObject:function(){return n(this.callSuper("toObject"),{opaque:this.opaque,matrix:this.matrix})}}),t.Image.filters.Convolute.fromObject=t.Image.filters.BaseFilter.fromObject}(t),function(e){"use strict";var t=e.fabric||(e.fabric={}),n=t.Image.filters,r=t.util.createClass;n.Grayscale=r(n.BaseFilter,{type:"Grayscale",fragmentSource:{average:"precision highp float;\nuniform sampler2D uTexture;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 color = texture2D(uTexture, vTexCoord);\nfloat average = (color.r + color.b + color.g) / 3.0;\ngl_FragColor = vec4(average, average, average, color.a);\n}",lightness:"precision highp float;\nuniform sampler2D uTexture;\nuniform int uMode;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 col = texture2D(uTexture, vTexCoord);\nfloat average = (max(max(col.r, col.g),col.b) + min(min(col.r, col.g),col.b)) / 2.0;\ngl_FragColor = vec4(average, average, average, col.a);\n}",luminosity:"precision highp float;\nuniform sampler2D uTexture;\nuniform int uMode;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 col = texture2D(uTexture, vTexCoord);\nfloat average = 0.21 * col.r + 0.72 * col.g + 0.07 * col.b;\ngl_FragColor = vec4(average, average, average, col.a);\n}"},mode:"average",mainParameter:"mode",applyTo2d:function(e){var t,n,r=e.imageData,i=r.data,a=i.length,o=this.mode;for(t=0;tu[0]&&i>u[1]&&a>u[2]&&r 0.0) {\n"+this.fragmentSource[e]+"}\n}"},retrieveShader:function(e){var t,n=this.type+"_"+this.mode;return e.programCache.hasOwnProperty(n)||(t=this.buildSource(this.mode),e.programCache[n]=this.createProgram(e.context,t)),e.programCache[n]},applyTo2d:function(e){var n,r,i,a,o,s,c,l=e.imageData,u=l.data,h=u.length,f=1-this.alpha;c=new t.Color(this.color).getSource(),n=c[0]*this.alpha,r=c[1]*this.alpha,i=c[2]*this.alpha;for(var d=0;d=e||t<=-e)return 0;if(t<1.1920929e-7&&t>-1.1920929e-7)return 1;t*=Math.PI;var n=t/e;return s(t)/t*s(n)/n}},applyTo2d:function(e){var t=e.imageData,n=this.scaleX,r=this.scaleY;this.rcpScaleX=1/n,this.rcpScaleY=1/r;var i,a=t.width,s=t.height,c=o(a*n),l=o(s*r);"sliceHack"===this.resizeType?i=this.sliceByTwo(e,a,s,c,l):"hermite"===this.resizeType?i=this.hermiteFastResize(e,a,s,c,l):"bilinear"===this.resizeType?i=this.bilinearFiltering(e,a,s,c,l):"lanczos"===this.resizeType&&(i=this.lanczosResize(e,a,s,c,l)),e.imageData=i},sliceByTwo:function(e,n,i,a,o){var s,c,l=e.imageData,u=.5,h=!1,f=!1,d=n*u,p=i*u,m=t.filterBackend.resources,v=0,g=0,b=n,y=0;m.sliceByTwo||(m.sliceByTwo=document.createElement("canvas")),s=m.sliceByTwo,(s.width<1.5*n||s.height=t)){j=r(1e3*a(S-M.x)),w[j]||(w[j]={});for(var E=x.y-_;E<=x.y+_;E++)E<0||E>=o||(H=r(1e3*a(E-M.y)),w[j][H]||(w[j][H]=p(i(n(j*g,2)+n(H*b,2))/1e3)),k=w[j][H],k>0&&(C=4*(E*t+S),L+=k,T+=k*h[C],O+=k*h[C+1],z+=k*h[C+2],D+=k*h[C+3]))}C=4*(c*s+e),d[C]=T/L,d[C+1]=O/L,d[C+2]=z/L,d[C+3]=D/L}return++e1&&E<-1||(_=2*E*E*E-3*E*E+1,_>0&&(H=4*(j+T*t),C+=_*p[H+3],M+=_,p[H+3]<255&&(_=_*p[H+3]/250),x+=_*p[H],S+=_*p[H+1],k+=_*p[H+2],w+=_))}v[y]=x/w,v[y+1]=S/w,v[y+2]=k/w,v[y+3]=C/M}return m},toObject:function(){return{type:this.type,scaleX:this.scaleX,scaleY:this.scaleY,resizeType:this.resizeType,lanczosLobes:this.lanczosLobes}}}),t.Image.filters.Resize.fromObject=t.Image.filters.BaseFilter.fromObject}(t),function(e){"use strict";var t=e.fabric||(e.fabric={}),n=t.Image.filters,r=t.util.createClass;n.Contrast=r(n.BaseFilter,{type:"Contrast",fragmentSource:"precision highp float;\nuniform sampler2D uTexture;\nuniform float uContrast;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 color = texture2D(uTexture, vTexCoord);\nfloat contrastF = 1.015 * (uContrast + 1.0) / (1.0 * (1.015 - uContrast));\ncolor.rgb = contrastF * (color.rgb - 0.5) + 0.5;\ngl_FragColor = color;\n}",contrast:0,mainParameter:"contrast",applyTo2d:function(e){if(0!==this.contrast){var t,n=e.imageData,r=n.data,i=r.length,a=Math.floor(255*this.contrast),o=259*(a+255)/(255*(259-a));for(t=0;t1&&(t=1/this.aspectRatio):this.aspectRatio<1&&(t=this.aspectRatio),e=t*this.blur*.12,this.horizontal?n[0]=e:n[1]=e,n}}),n.Blur.fromObject=t.Image.filters.BaseFilter.fromObject}(t),function(e){"use strict";var t=e.fabric||(e.fabric={}),n=t.Image.filters,r=t.util.createClass;n.Gamma=r(n.BaseFilter,{type:"Gamma",fragmentSource:"precision highp float;\nuniform sampler2D uTexture;\nuniform vec3 uGamma;\nvarying vec2 vTexCoord;\nvoid main() {\nvec4 color = texture2D(uTexture, vTexCoord);\nvec3 correction = (1.0 / uGamma);\ncolor.r = pow(color.r, correction.r);\ncolor.g = pow(color.g, correction.g);\ncolor.b = pow(color.b, correction.b);\ngl_FragColor = color;\ngl_FragColor.rgb *= color.a;\n}",gamma:[1,1,1],mainParameter:"gamma",initialize:function(e){this.gamma=[1,1,1],n.BaseFilter.prototype.initialize.call(this,e)},applyTo2d:function(e){var t,n=e.imageData,r=n.data,i=this.gamma,a=r.length,o=1/i[0],s=1/i[1],c=1/i[2];for(this.rVals||(this.rVals=new Uint8Array(256),this.gVals=new Uint8Array(256),this.bVals=new Uint8Array(256)),t=0,a=256;t'},_getCacheCanvasDimensions:function(){var e=this.callSuper("_getCacheCanvasDimensions"),t=this.fontSize;return e.width+=t*e.zoomX,e.height+=t*e.zoomY,e},_render:function(e){this._setTextStyles(e),this._renderTextLinesBackground(e),this._renderTextDecoration(e,"underline"),this._renderText(e),this._renderTextDecoration(e,"overline"),this._renderTextDecoration(e,"linethrough")},_renderText:function(e){"stroke"===this.paintFirst?(this._renderTextStroke(e),this._renderTextFill(e)):(this._renderTextFill(e),this._renderTextStroke(e))},_setTextStyles:function(e,t,n){e.textBaseline="alphabetic",e.font=this._getFontDeclaration(t,n)},calcTextWidth:function(){for(var e=this.getLineWidth(0),t=1,n=this._textLines.length;te&&(e=r)}return e},_renderTextLine:function(e,t,n,r,i,a){this._renderChars(e,t,n,r,i,a)},_renderTextLinesBackground:function(e){if(this.textBackgroundColor||this.styleHas("textBackgroundColor")){for(var t,n,r,i,a,o,s=e.fillStyle,c=this._getLeftOffset(),l=this._getTopOffset(),u=0,h=0,f=this.path,d=0,p=this._textLines.length;ds&&(f%=s),this._setGraphemeOnPath(f,a,o)),h[n]=a,c+=a.kernedWidth,f+=a.kernedWidth,i=r;return h[n]={left:a?a.left+a.width:0,width:0,kernedWidth:0,height:this.fontSize},{width:c,numOfSpaces:u}},_setGraphemeOnPath:function(e,n,r){var i=e+n.kernedWidth/2,a=this.path,o=t.util.getPointOnPath(a.path,i-.1,a.segmentsInfo),s=t.util.getPointOnPath(a.path,i+.1,a.segmentsInfo);n.renderLeft=o.x-r.x,n.renderTop=o.y-r.y,n.angle=Math.atan2(s.y-o.y,s.x-o.x)},_getGraphemeBox:function(e,t,n,r,i){var a,o=this.getCompleteStyleDeclaration(t,n),s=r?this.getCompleteStyleDeclaration(t,n-1):{},c=this._measureChar(e,o,r,s),l=c.kernedWidth,u=c.width;0!==this.charSpacing&&(a=this._getWidthOfCharSpacing(),u+=a,l+=a);var h={width:u,left:0,height:o.fontSize,kernedWidth:l,deltaY:o.deltaY};if(n>0&&!i){var f=this.__charBounds[t][n-1];h.left=f.left+f.width+c.kernedWidth-c.width}return h},getHeightOfLine:function(e){if(this.__lineHeights[e])return this.__lineHeights[e];for(var t=this._textLines[e],n=this.getHeightOfChar(e,0),r=1,i=t.length;r0?(l&&g&&e.fillRect(b+a+h,u+this.offsets[t]*r+o,f,this.fontSize/15),h=d.left,f=d.width,l=p,g=v,r=i,o=s):f+=d.kernedWidth;e.fillStyle=v,p&&v&&e.fillRect(b+a+h,u+this.offsets[t]*r+o,f-w,this.fontSize/15),y+=n}else y+=n;this._removeShadow(e)}},_getFontDeclaration:function(e,n){var r=e||this,i=this.fontFamily,a=t.Text.genericFonts.indexOf(i.toLowerCase())>-1,o=void 0===i||i.indexOf("'")>-1||i.indexOf(",")>-1||i.indexOf('"')>-1||a?r.fontFamily:'"'+r.fontFamily+'"';return[t.isLikelyNode?r.fontWeight:r.fontStyle,t.isLikelyNode?r.fontStyle:r.fontWeight,n?this.CACHE_FONT_SIZE+"px":r.fontSize+"px",o].join(" ")},render:function(e){this.visible&&(this.canvas&&this.canvas.skipOffscreen&&!this.group&&!this.isOnScreen()||(this._shouldClearDimensionCache()&&this.initDimensions(),this.callSuper("render",e)))},_splitTextIntoLines:function(e){for(var n=e.split(this._reNewline),r=new Array(n.length),i=["\n"],a=[],o=0;o-1&&(e.underline=!0),e.textDecoration.indexOf("line-through")>-1&&(e.linethrough=!0),e.textDecoration.indexOf("overline")>-1&&(e.overline=!0),delete e.textDecoration)}r.IText=r.util.createClass(r.Text,r.Observable,{type:"i-text",selectionStart:0,selectionEnd:0,selectionColor:"rgba(17,119,255,0.3)",isEditing:!1,editable:!0,editingBorderColor:"rgba(102,153,255,0.25)",cursorWidth:2,cursorColor:"",cursorDelay:1e3,cursorDuration:600,caching:!0,_reSpace:/\s|\n/,_currentCursorOpacity:0,_selectionDirection:null,_abortCursorAnimation:!1,__widthOfSpace:[],inCompositionMode:!1,initialize:function(e,t){this.callSuper("initialize",e,t),this.initBehavior()},setSelectionStart:function(e){e=Math.max(e,0),this._updateAndFire("selectionStart",e)},setSelectionEnd:function(e){e=Math.min(e,this.text.length),this._updateAndFire("selectionEnd",e)},_updateAndFire:function(e,t){this[e]!==t&&(this._fireSelectionChanged(),this[e]=t),this._updateTextarea()},_fireSelectionChanged:function(){this.fire("selection:changed"),this.canvas&&this.canvas.fire("text:selection:changed",{target:this})},initDimensions:function(){this.isEditing&&this.initDelayedCursor(),this.clearContextTop(),this.callSuper("initDimensions")},render:function(e){this.clearContextTop(),this.callSuper("render",e),this.cursorOffsetCache={},this.renderCursorOrSelection()},_render:function(e){this.callSuper("_render",e)},clearContextTop:function(e){if(this.isEditing&&this.canvas&&this.canvas.contextTop){var t=this.canvas.contextTop,n=this.canvas.viewportTransform;t.save(),t.transform(n[0],n[1],n[2],n[3],n[4],n[5]),this.transform(t),this._clearTextArea(t),e||t.restore()}},renderCursorOrSelection:function(){if(this.isEditing&&this.canvas&&this.canvas.contextTop){var e=this._getCursorBoundaries(),t=this.canvas.contextTop;this.clearContextTop(!0),this.selectionStart===this.selectionEnd?this.renderCursor(e,t):this.renderSelection(e,t),t.restore()}},_clearTextArea:function(e){var t=this.width+4,n=this.height+4;e.clearRect(-t/2,-n/2,t,n)},_getCursorBoundaries:function(e){"undefined"===typeof e&&(e=this.selectionStart);var t=this._getLeftOffset(),n=this._getTopOffset(),r=this._getCursorBoundariesOffsets(e);return{left:t,top:n,leftOffset:r.left,topOffset:r.top}},_getCursorBoundariesOffsets:function(e){if(this.cursorOffsetCache&&"top"in this.cursorOffsetCache)return this.cursorOffsetCache;var t,n,r,i,a=0,o=0,s=this.get2DCursorLocation(e);r=s.charIndex,n=s.lineIndex;for(var c=0;c0?o:0)},this.cursorOffsetCache=i,this.cursorOffsetCache},renderCursor:function(e,t){var n=this.get2DCursorLocation(),r=n.lineIndex,i=n.charIndex>0?n.charIndex-1:0,a=this.getValueOfPropertyAt(r,i,"fontSize"),o=this.scaleX*this.canvas.getZoom(),s=this.cursorWidth/o,c=e.topOffset,l=this.getValueOfPropertyAt(r,i,"deltaY");c+=(1-this._fontSizeFraction)*this.getHeightOfLine(r)/this.lineHeight-a*(1-this._fontSizeFraction),this.inCompositionMode&&this.renderSelection(e,t),t.fillStyle=this.cursorColor||this.getValueOfPropertyAt(r,i,"fill"),t.globalAlpha=this.__isMousedown?1:this._currentCursorOpacity,t.fillRect(e.left+e.leftOffset-s/2,c+e.top+l,s,a)},renderSelection:function(e,t){for(var n=this.inCompositionMode?this.hiddenTextarea.selectionStart:this.selectionStart,r=this.inCompositionMode?this.hiddenTextarea.selectionEnd:this.selectionEnd,i=-1!==this.textAlign.indexOf("justify"),a=this.get2DCursorLocation(n),o=this.get2DCursorLocation(r),s=a.lineIndex,c=o.lineIndex,l=a.charIndex<0?0:a.charIndex,u=o.charIndex<0?0:o.charIndex,h=s;h<=c;h++){var f=this._getLineLeftOffset(h)||0,d=this.getHeightOfLine(h),p=0,m=0,v=0;if(h===s&&(m=this.__charBounds[s][l].left),h>=s&&h1)&&(d/=this.lineHeight),this.inCompositionMode?(t.fillStyle=this.compositionColor||"black",t.fillRect(e.left+f+m,e.top+e.topOffset+d,v-m,1)):(t.fillStyle=this.selectionColor,t.fillRect(e.left+f+m,e.top+e.topOffset,v-m,d)),e.topOffset+=p}},getCurrentCharFontSize:function(){var e=this._getCurrentCharIndex();return this.getValueOfPropertyAt(e.l,e.c,"fontSize")},getCurrentCharColor:function(){var e=this._getCurrentCharIndex();return this.getValueOfPropertyAt(e.l,e.c,"fill")},_getCurrentCharIndex:function(){var e=this.get2DCursorLocation(this.selectionStart,!0),t=e.charIndex>0?e.charIndex-1:0;return{l:e.lineIndex,c:t}}}),r.IText.fromObject=function(t,n){if(e(t),t.styles)for(var i in t.styles)for(var a in t.styles[i])e(t.styles[i][a]);r.Object._fromObject("IText",t,n,"text")}}(),function(){var e=r.util.object.clone;r.util.object.extend(r.IText.prototype,{initBehavior:function(){this.initAddedHandler(),this.initRemovedHandler(),this.initCursorSelectionHandlers(),this.initDoubleClickSimulation(),this.mouseMoveHandler=this.mouseMoveHandler.bind(this)},onDeselect:function(){this.isEditing&&this.exitEditing(),this.selected=!1},initAddedHandler:function(){var e=this;this.on("added",(function(){var t=e.canvas;t&&(t._hasITextHandlers||(t._hasITextHandlers=!0,e._initCanvasHandlers(t)),t._iTextInstances=t._iTextInstances||[],t._iTextInstances.push(e))}))},initRemovedHandler:function(){var e=this;this.on("removed",(function(){var t=e.canvas;t&&(t._iTextInstances=t._iTextInstances||[],r.util.removeFromArray(t._iTextInstances,e),0===t._iTextInstances.length&&(t._hasITextHandlers=!1,e._removeCanvasHandlers(t)))}))},_initCanvasHandlers:function(e){e._mouseUpITextHandler=function(){e._iTextInstances&&e._iTextInstances.forEach((function(e){e.__isMousedown=!1}))},e.on("mouse:up",e._mouseUpITextHandler)},_removeCanvasHandlers:function(e){e.off("mouse:up",e._mouseUpITextHandler)},_tick:function(){this._currentTickState=this._animateCursor(this,1,this.cursorDuration,"_onTickComplete")},_animateCursor:function(e,t,n,r){var i;return i={isAborted:!1,abort:function(){this.isAborted=!0}},e.animate("_currentCursorOpacity",t,{duration:n,onComplete:function(){i.isAborted||e[r]()},onChange:function(){e.canvas&&e.selectionStart===e.selectionEnd&&e.renderCursorOrSelection()},abort:function(){return i.isAborted}}),i},_onTickComplete:function(){var e=this;this._cursorTimeout1&&clearTimeout(this._cursorTimeout1),this._cursorTimeout1=setTimeout((function(){e._currentTickCompleteState=e._animateCursor(e,0,this.cursorDuration/2,"_tick")}),100)},initDelayedCursor:function(e){var t=this,n=e?0:this.cursorDelay;this.abortCursorAnimation(),this._currentCursorOpacity=1,this._cursorTimeout2=setTimeout((function(){t._tick()}),n)},abortCursorAnimation:function(){var e=this._currentTickState||this._currentTickCompleteState,t=this.canvas;this._currentTickState&&this._currentTickState.abort(),this._currentTickCompleteState&&this._currentTickCompleteState.abort(),clearTimeout(this._cursorTimeout1),clearTimeout(this._cursorTimeout2),this._currentCursorOpacity=0,e&&t&&t.clearContext(t.contextTop||t.contextContainer)},selectAll:function(){return this.selectionStart=0,this.selectionEnd=this._text.length,this._fireSelectionChanged(),this._updateTextarea(),this},getSelectedText:function(){return this._text.slice(this.selectionStart,this.selectionEnd).join("")},findWordBoundaryLeft:function(e){var t=0,n=e-1;if(this._reSpace.test(this._text[n]))while(this._reSpace.test(this._text[n]))t++,n--;while(/\S/.test(this._text[n])&&n>-1)t++,n--;return e-t},findWordBoundaryRight:function(e){var t=0,n=e;if(this._reSpace.test(this._text[n]))while(this._reSpace.test(this._text[n]))t++,n++;while(/\S/.test(this._text[n])&&n-1)t++,n--;return e-t},findLineBoundaryRight:function(e){var t=0,n=e;while(!/\n/.test(this._text[n])&&n0&&ithis.__selectionStartOnMouseDown?(this.selectionStart=this.__selectionStartOnMouseDown,this.selectionEnd=t):(this.selectionStart=t,this.selectionEnd=this.__selectionStartOnMouseDown),this.selectionStart===n&&this.selectionEnd===r||(this.restartCursorIfNeeded(),this._fireSelectionChanged(),this._updateTextarea(),this.renderCursorOrSelection()))}},_setEditingProps:function(){this.hoverCursor="text",this.canvas&&(this.canvas.defaultCursor=this.canvas.moveCursor="text"),this.borderColor=this.editingBorderColor,this.hasControls=this.selectable=!1,this.lockMovementX=this.lockMovementY=!0},fromStringToGraphemeSelection:function(e,t,n){var i=n.slice(0,e),a=r.util.string.graphemeSplit(i).length;if(e===t)return{selectionStart:a,selectionEnd:a};var o=n.slice(e,t),s=r.util.string.graphemeSplit(o).length;return{selectionStart:a,selectionEnd:a+s}},fromGraphemeToStringSelection:function(e,t,n){var r=n.slice(0,e),i=r.join("").length;if(e===t)return{selectionStart:i,selectionEnd:i};var a=n.slice(e,t),o=a.join("").length;return{selectionStart:i,selectionEnd:i+o}},_updateTextarea:function(){if(this.cursorOffsetCache={},this.hiddenTextarea){if(!this.inCompositionMode){var e=this.fromGraphemeToStringSelection(this.selectionStart,this.selectionEnd,this._text);this.hiddenTextarea.selectionStart=e.selectionStart,this.hiddenTextarea.selectionEnd=e.selectionEnd}this.updateTextareaPosition()}},updateFromTextArea:function(){if(this.hiddenTextarea){this.cursorOffsetCache={},this.text=this.hiddenTextarea.value,this._shouldClearDimensionCache()&&(this.initDimensions(),this.setCoords());var e=this.fromStringToGraphemeSelection(this.hiddenTextarea.selectionStart,this.hiddenTextarea.selectionEnd,this.hiddenTextarea.value);this.selectionEnd=this.selectionStart=e.selectionEnd,this.inCompositionMode||(this.selectionStart=e.selectionStart),this.updateTextareaPosition()}},updateTextareaPosition:function(){if(this.selectionStart===this.selectionEnd){var e=this._calcTextareaPosition();this.hiddenTextarea.style.left=e.left,this.hiddenTextarea.style.top=e.top}},_calcTextareaPosition:function(){if(!this.canvas)return{x:1,y:1};var e=this.inCompositionMode?this.compositionStart:this.selectionStart,t=this._getCursorBoundaries(e),n=this.get2DCursorLocation(e),i=n.lineIndex,a=n.charIndex,o=this.getValueOfPropertyAt(i,a,"fontSize")*this.lineHeight,s=t.leftOffset,c=this.calcTransformMatrix(),l={x:t.left+s,y:t.top+t.topOffset+o},u=this.canvas.getRetinaScaling(),h=this.canvas.upperCanvasEl,f=h.width/u,d=h.height/u,p=f-o,m=d-o,v=h.clientWidth/f,g=h.clientHeight/d;return l=r.util.transformPoint(l,c),l=r.util.transformPoint(l,this.canvas.viewportTransform),l.x*=v,l.y*=g,l.x<0&&(l.x=0),l.x>p&&(l.x=p),l.y<0&&(l.y=0),l.y>m&&(l.y=m),l.x+=this.canvas._offset.left,l.y+=this.canvas._offset.top,{left:l.x+"px",top:l.y+"px",fontSize:o+"px",charHeight:o}},_saveEditingProps:function(){this._savedProps={hasControls:this.hasControls,borderColor:this.borderColor,lockMovementX:this.lockMovementX,lockMovementY:this.lockMovementY,hoverCursor:this.hoverCursor,selectable:this.selectable,defaultCursor:this.canvas&&this.canvas.defaultCursor,moveCursor:this.canvas&&this.canvas.moveCursor}},_restoreEditingProps:function(){this._savedProps&&(this.hoverCursor=this._savedProps.hoverCursor,this.hasControls=this._savedProps.hasControls,this.borderColor=this._savedProps.borderColor,this.selectable=this._savedProps.selectable,this.lockMovementX=this._savedProps.lockMovementX,this.lockMovementY=this._savedProps.lockMovementY,this.canvas&&(this.canvas.defaultCursor=this._savedProps.defaultCursor,this.canvas.moveCursor=this._savedProps.moveCursor))},exitEditing:function(){var e=this._textBeforeEdit!==this.text,t=this.hiddenTextarea;return this.selected=!1,this.isEditing=!1,this.selectionEnd=this.selectionStart,t&&(t.blur&&t.blur(),t.parentNode&&t.parentNode.removeChild(t)),this.hiddenTextarea=null,this.abortCursorAnimation(),this._restoreEditingProps(),this._currentCursorOpacity=0,this._shouldClearDimensionCache()&&(this.initDimensions(),this.setCoords()),this.fire("editing:exited"),e&&this.fire("modified"),this.canvas&&(this.canvas.off("mouse:move",this.mouseMoveHandler),this.canvas.fire("text:editing:exited",{target:this}),e&&this.canvas.fire("object:modified",{target:this})),this},_removeExtraneousStyles:function(){for(var e in this.styles)this._textLines[e]||delete this.styles[e]},removeStyleFromTo:function(e,t){var n,r,i=this.get2DCursorLocation(e,!0),a=this.get2DCursorLocation(t,!0),o=i.lineIndex,s=i.charIndex,c=a.lineIndex,l=a.charIndex;if(o!==c){if(this.styles[o])for(n=s;n=l&&(r[u-f]=r[h],delete r[h])}},shiftLineStyles:function(t,n){var r=e(this.styles);for(var i in this.styles){var a=parseInt(i,10);a>t&&(this.styles[a+n]=r[a],r[a-n]||delete this.styles[a])}},restartCursorIfNeeded:function(){this._currentTickState&&!this._currentTickState.isAborted&&this._currentTickCompleteState&&!this._currentTickCompleteState.isAborted||this.initDelayedCursor()},insertNewlineStyleObject:function(t,n,r,i){var a,o={},s=!1,c=this._unwrappedTextLines[t].length===n;for(var l in r||(r=1),this.shiftLineStyles(t,r),this.styles[t]&&(a=this.styles[t][0===n?n:n-1]),this.styles[t]){var u=parseInt(l,10);u>=n&&(s=!0,o[u-n]=this.styles[t][l],c&&0===n||delete this.styles[t][l])}var h=!1;s&&!c&&(this.styles[t+r]=o,h=!0),h&&r--;while(r>0)i&&i[r-1]?this.styles[t+r]={0:e(i[r-1])}:a?this.styles[t+r]={0:e(a)}:delete this.styles[t+r],r--;this._forceClearCache=!0},insertCharStyleObject:function(t,n,r,i){this.styles||(this.styles={});var a=this.styles[t],o=a?e(a):{};for(var s in r||(r=1),o){var c=parseInt(s,10);c>=n&&(a[c+r]=o[c],o[c-r]||delete a[c])}if(this._forceClearCache=!0,i)while(r--)Object.keys(i[r]).length&&(this.styles[t]||(this.styles[t]={}),this.styles[t][n+r]=e(i[r]));else if(a){var l=a[n?n-1:1];while(l&&r--)this.styles[t][n+r]=e(l)}},insertNewStyleBlock:function(e,t,n){for(var r=this.get2DCursorLocation(t,!0),i=[0],a=0,o=0;o0&&(this.insertCharStyleObject(r.lineIndex,r.charIndex,i[0],n),n=n&&n.slice(i[0]+1)),a&&this.insertNewlineStyleObject(r.lineIndex,r.charIndex+i[0],a);for(o=1;o0?this.insertCharStyleObject(r.lineIndex+o,0,i[o],n):n&&(this.styles[r.lineIndex+o][0]=n[0]),n=n&&n.slice(i[o]+1);i[o]>0&&this.insertCharStyleObject(r.lineIndex+o,0,i[o],n)},setSelectionStartEndWithShift:function(e,t,n){n<=e?(t===e?this._selectionDirection="left":"right"===this._selectionDirection&&(this._selectionDirection="left",this.selectionEnd=e),this.selectionStart=n):n>e&&ne?this.selectionStart=e:this.selectionStart<0&&(this.selectionStart=0),this.selectionEnd>e?this.selectionEnd=e:this.selectionEnd<0&&(this.selectionEnd=0)}})}(),r.util.object.extend(r.IText.prototype,{initDoubleClickSimulation:function(){this.__lastClickTime=+new Date,this.__lastLastClickTime=+new Date,this.__lastPointer={},this.on("mousedown",this.onMouseDown)},onMouseDown:function(e){if(this.canvas){this.__newClickTime=+new Date;var t=e.pointer;this.isTripleClick(t)&&(this.fire("tripleclick",e),this._stopEvent(e.e)),this.__lastLastClickTime=this.__lastClickTime,this.__lastClickTime=this.__newClickTime,this.__lastPointer=t,this.__lastIsEditing=this.isEditing,this.__lastSelected=this.selected}},isTripleClick:function(e){return this.__newClickTime-this.__lastClickTime<500&&this.__lastClickTime-this.__lastLastClickTime<500&&this.__lastPointer.x===e.x&&this.__lastPointer.y===e.y},_stopEvent:function(e){e.preventDefault&&e.preventDefault(),e.stopPropagation&&e.stopPropagation()},initCursorSelectionHandlers:function(){this.initMousedownHandler(),this.initMouseupHandler(),this.initClicks()},doubleClickHandler:function(e){this.isEditing&&this.selectWord(this.getSelectionStartFromPointer(e.e))},tripleClickHandler:function(e){this.isEditing&&this.selectLine(this.getSelectionStartFromPointer(e.e))},initClicks:function(){this.on("mousedblclick",this.doubleClickHandler),this.on("tripleclick",this.tripleClickHandler)},_mouseDownHandler:function(e){!this.canvas||!this.editable||e.e.button&&1!==e.e.button||(this.__isMousedown=!0,this.selected&&(this.inCompositionMode=!1,this.setCursorByClick(e.e)),this.isEditing&&(this.__selectionStartOnMouseDown=this.selectionStart,this.selectionStart===this.selectionEnd&&this.abortCursorAnimation(),this.renderCursorOrSelection()))},_mouseDownHandlerBefore:function(e){!this.canvas||!this.editable||e.e.button&&1!==e.e.button||(this.selected=this===this.canvas._activeObject)},initMousedownHandler:function(){this.on("mousedown",this._mouseDownHandler),this.on("mousedown:before",this._mouseDownHandlerBefore)},initMouseupHandler:function(){this.on("mouseup",this.mouseUpHandler)},mouseUpHandler:function(e){if(this.__isMousedown=!1,!(!this.editable||this.group||e.transform&&e.transform.actionPerformed||e.e.button&&1!==e.e.button)){if(this.canvas){var t=this.canvas._activeObject;if(t&&t!==this)return}this.__lastSelected&&!this.__corner?(this.selected=!1,this.__lastSelected=!1,this.enterEditing(e.e),this.selectionStart===this.selectionEnd?this.initDelayedCursor(!0):this.renderCursorOrSelection()):this.selected=!0}},setCursorByClick:function(e){var t=this.getSelectionStartFromPointer(e),n=this.selectionStart,r=this.selectionEnd;e.shiftKey?this.setSelectionStartEndWithShift(n,r,t):(this.selectionStart=t,this.selectionEnd=t),this.isEditing&&(this._fireSelectionChanged(),this._updateTextarea())},getSelectionStartFromPointer:function(e){for(var t,n,r=this.getLocalPointer(e),i=0,a=0,o=0,s=0,c=0,l=0,u=this._textLines.length;l0&&(s+=this._textLines[l-1].length+this.missingNewlineOffset(l-1))}t=this._getLineLeftOffset(c),a=t*this.scaleX,n=this._textLines[c];for(var h=0,f=n.length;ha||o<0?0:1,c=r+s;return this.flipX&&(c=i-c),c>this._text.length&&(c=this._text.length),c}}),r.util.object.extend(r.IText.prototype,{initHiddenTextarea:function(){this.hiddenTextarea=r.document.createElement("textarea"),this.hiddenTextarea.setAttribute("autocapitalize","off"),this.hiddenTextarea.setAttribute("autocorrect","off"),this.hiddenTextarea.setAttribute("autocomplete","off"),this.hiddenTextarea.setAttribute("spellcheck","false"),this.hiddenTextarea.setAttribute("data-fabric-hiddentextarea",""),this.hiddenTextarea.setAttribute("wrap","off");var e=this._calcTextareaPosition();this.hiddenTextarea.style.cssText="position: absolute; top: "+e.top+"; left: "+e.left+"; z-index: -999; opacity: 0; width: 1px; height: 1px; font-size: 1px; paddingｰtop: "+e.fontSize+";",r.document.body.appendChild(this.hiddenTextarea),r.util.addListener(this.hiddenTextarea,"keydown",this.onKeyDown.bind(this)),r.util.addListener(this.hiddenTextarea,"keyup",this.onKeyUp.bind(this)),r.util.addListener(this.hiddenTextarea,"input",this.onInput.bind(this)),r.util.addListener(this.hiddenTextarea,"copy",this.copy.bind(this)),r.util.addListener(this.hiddenTextarea,"cut",this.copy.bind(this)),r.util.addListener(this.hiddenTextarea,"paste",this.paste.bind(this)),r.util.addListener(this.hiddenTextarea,"compositionstart",this.onCompositionStart.bind(this)),r.util.addListener(this.hiddenTextarea,"compositionupdate",this.onCompositionUpdate.bind(this)),r.util.addListener(this.hiddenTextarea,"compositionend",this.onCompositionEnd.bind(this)),!this._clickHandlerInitialized&&this.canvas&&(r.util.addListener(this.canvas.upperCanvasEl,"click",this.onClick.bind(this)),this._clickHandlerInitialized=!0)},keysMap:{9:"exitEditing",27:"exitEditing",33:"moveCursorUp",34:"moveCursorDown",35:"moveCursorRight",36:"moveCursorLeft",37:"moveCursorLeft",38:"moveCursorUp",39:"moveCursorRight",40:"moveCursorDown"},ctrlKeysMapUp:{67:"copy",88:"cut"},ctrlKeysMapDown:{65:"selectAll"},onClick:function(){this.hiddenTextarea&&this.hiddenTextarea.focus()},onKeyDown:function(e){if(this.isEditing){if(e.keyCode in this.keysMap)this[this.keysMap[e.keyCode]](e);else{if(!(e.keyCode in this.ctrlKeysMapDown)||!e.ctrlKey&&!e.metaKey)return;this[this.ctrlKeysMapDown[e.keyCode]](e)}e.stopImmediatePropagation(),e.preventDefault(),e.keyCode>=33&&e.keyCode<=40?(this.inCompositionMode=!1,this.clearContextTop(),this.renderCursorOrSelection()):this.canvas&&this.canvas.requestRenderAll()}},onKeyUp:function(e){!this.isEditing||this._copyDone||this.inCompositionMode?this._copyDone=!1:e.keyCode in this.ctrlKeysMapUp&&(e.ctrlKey||e.metaKey)&&(this[this.ctrlKeysMapUp[e.keyCode]](e),e.stopImmediatePropagation(),e.preventDefault(),this.canvas&&this.canvas.requestRenderAll())},onInput:function(e){var t=this.fromPaste;if(this.fromPaste=!1,e&&e.stopPropagation(),this.isEditing){var n,i,a,o,s,c=this._splitTextIntoLines(this.hiddenTextarea.value).graphemeText,l=this._text.length,u=c.length,h=u-l,f=this.selectionStart,d=this.selectionEnd,p=f!==d;if(""===this.hiddenTextarea.value)return this.styles={},this.updateFromTextArea(),this.fire("changed"),void(this.canvas&&(this.canvas.fire("text:changed",{target:this}),this.canvas.requestRenderAll()));var m=this.fromStringToGraphemeSelection(this.hiddenTextarea.selectionStart,this.hiddenTextarea.selectionEnd,this.hiddenTextarea.value),v=f>m.selectionStart;p?(n=this._text.slice(f,d),h+=d-f):u0&&(n=this.__charBounds[e][t-1],r+=n.left+n.width),r},getDownCursorOffset:function(e,t){var n=this._getSelectionForOffset(e,t),r=this.get2DCursorLocation(n),i=r.lineIndex;if(i===this._textLines.length-1||e.metaKey||34===e.keyCode)return this._text.length-n;var a=r.charIndex,o=this._getWidthBeforeCursor(i,a),s=this._getIndexOnLine(i+1,o),c=this._textLines[i].slice(a);return c.length+s+1+this.missingNewlineOffset(i)},_getSelectionForOffset:function(e,t){return e.shiftKey&&this.selectionStart!==this.selectionEnd&&t?this.selectionEnd:this.selectionStart},getUpCursorOffset:function(e,t){var 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n=void 0;this.clickOutsideHandler||!this.isClickToHide()&&!this.isContextmenuToShow()||(n=e.getDocument(),this.clickOutsideHandler=Object(d["a"])(n,"mousedown",this.onDocumentClick)),this.touchOutsideHandler||(n=n||e.getDocument(),this.touchOutsideHandler=Object(d["a"])(n,"touchstart",this.onDocumentClick)),!this.contextmenuOutsideHandler1&&this.isContextmenuToShow()&&(n=n||e.getDocument(),this.contextmenuOutsideHandler1=Object(d["a"])(n,"scroll",this.onContextmenuClose)),!this.contextmenuOutsideHandler2&&this.isContextmenuToShow()&&(this.contextmenuOutsideHandler2=Object(d["a"])(window,"blur",this.onContextmenuClose))}else this.clearOutsideHandler()},onMouseenter:function(e){var t=this.$props.mouseEnterDelay;this.fireEvents("mouseenter",e),this.delaySetPopupVisible(!0,t,t?null:e)},onMouseMove:function(e){this.fireEvents("mousemove",e),this.setPoint(e)},onMouseleave:function(e){this.fireEvents("mouseleave",e),this.delaySetPopupVisible(!1,this.$props.mouseLeaveDelay)},onPopupMouseenter:function(){this.clearDelayTimer()},onPopupMouseleave:function(e){e&&e.relatedTarget&&!e.relatedTarget.setTimeout&&this._component&&this._component.getPopupDomNode&&Object(u["a"])(this._component.getPopupDomNode(),e.relatedTarget)||this.delaySetPopupVisible(!1,this.$props.mouseLeaveDelay)},onFocus:function(e){this.fireEvents("focus",e),this.clearDelayTimer(),this.isFocusToShow()&&(this.focusTime=Date.now(),this.delaySetPopupVisible(!0,this.$props.focusDelay))},onMousedown:function(e){this.fireEvents("mousedown",e),this.preClickTime=Date.now()},onTouchstart:function(e){this.fireEvents("touchstart",e),this.preTouchTime=Date.now()},onBlur:function(e){Object(u["a"])(e.target,e.relatedTarget||document.activeElement)||(this.fireEvents("blur",e),this.clearDelayTimer(),this.isBlurToHide()&&this.delaySetPopupVisible(!1,this.$props.blurDelay))},onContextmenu:function(e){e.preventDefault(),this.fireEvents("contextmenu",e),this.setPopupVisible(!0,e)},onContextmenuClose:function(){this.isContextmenuToShow()&&this.close()},onClick:function(e){if(this.fireEvents("click",e),this.focusTime){var 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r=t.handleGetPopupClassFromAlign,i=t.getRootDomNode,o=t.getContainer,s=t.$props,c=s.prefixCls,l=s.destroyPopupOnHide,u=s.popupClassName,f=s.action,d=s.popupAnimation,p=s.popupTransitionName,m=s.popupStyle,v=s.mask,g=s.maskAnimation,b=s.maskTransitionName,y=s.zIndex,_=s.stretch,w=s.alignPoint,M=this.$data,x=M.sPopupVisible,S=M.point,k=this.getPopupAlign(),C={props:{prefixCls:c,destroyPopupOnHide:l,visible:x,point:w&&S,action:f,align:k,animation:d,getClassNameFromAlign:r,stretch:_,getRootDomNode:i,mask:v,zIndex:y,transitionName:p,maskAnimation:g,maskTransitionName:b,getContainer:o,popupClassName:u,popupStyle:m},on:a()({align:Object(h["j"])(this).popupAlign||ot},n),directives:[{name:"ant-ref",value:this.savePopup}]};return e(nt,C,[Object(h["f"])(t,"popup")])},getContainer:function(){var e=this.$props,t=this.dialogContext,n=document.createElement("div");n.style.position="absolute",n.style.top="0",n.style.left="0",n.style.width="100%";var r=e.getPopupContainer?e.getPopupContainer(this.$el,t):e.getDocument().body;return r.appendChild(n),this.popupContainer=n,n},setPopupVisible:function(e,t){var n=this.alignPoint,r=this.sPopupVisible;if(this.clearDelayTimer(),r!==e){Object(h["r"])(this,"popupVisible")||this.setState({sPopupVisible:e,prevPopupVisible:r});var i=Object(h["j"])(this);i.popupVisibleChange&&i.popupVisibleChange(e)}n&&t&&this.setPoint(t)},setPoint:function(e){var t=this.$props.alignPoint;t&&e&&this.setState({point:{pageX:e.pageX,pageY:e.pageY}})},delaySetPopupVisible:function(e,t,n){var r=this,i=1e3*t;if(this.clearDelayTimer(),i){var a=n?{pageX:n.pageX,pageY:n.pageY}:null;this.delayTimer=Object(f["b"])((function(){r.setPopupVisible(e,a),r.clearDelayTimer()}),i)}else 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e=this.$props,t=e.action,n=e.showAction;return-1!==t.indexOf("contextmenu")||-1!==n.indexOf("contextmenu")},isClickToHide:function(){var e=this.$props,t=e.action,n=e.hideAction;return-1!==t.indexOf("click")||-1!==n.indexOf("click")},isMouseEnterToShow:function(){var e=this.$props,t=e.action,n=e.showAction;return-1!==t.indexOf("hover")||-1!==n.indexOf("mouseenter")},isMouseLeaveToHide:function(){var e=this.$props,t=e.action,n=e.hideAction;return-1!==t.indexOf("hover")||-1!==n.indexOf("mouseleave")},isFocusToShow:function(){var e=this.$props,t=e.action,n=e.showAction;return-1!==t.indexOf("focus")||-1!==n.indexOf("focus")},isBlurToHide:function(){var 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t=this,n=e.relatedTarget||document.activeElement;if((pe["c"]||pe["b"])&&(e.relatedTarget===this.$refs.arrow||n&&this.selectTriggerRef&&this.selectTriggerRef.getInnerMenu()&&this.selectTriggerRef.getInnerMenu().$el===n||Object(de["a"])(e.target,n)))return e.target.focus(),void e.preventDefault();this.clearBlurTime(),this.disabled?e.preventDefault():this.blurTimer=setTimeout((function(){t._focused=!1,t.updateFocusClassName();var e=t.$props,n=t.$data._value,r=t.$data._inputValue;if(q(e)&&e.showSearch&&r&&e.defaultActiveFirstOption){var i=t._options||[];if(i.length){var a=ne(i);a&&(n=[R(a)],t.fireChange(n))}}else if(W(e)&&r){t._mouseDown?t.setInputValue(""):(t.$data._inputValue="",t.getInputDOMNode&&t.getInputDOMNode()&&(t.getInputDOMNode().value=""));var o=t.getValueByInput(r);void 0!==o&&(n=o,t.fireChange(n))}if(W(e)&&t._mouseDown)return 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l.data=l.data||{},e("div",{class:t.prefixCls+"-search__field__wrap",on:{click:this.inputClick}},[Object(T["a"])(l,{props:{disabled:t.disabled,value:r},attrs:u()({},l.data.attrs||{},{disabled:t.disabled,value:r}),domProps:{value:r},class:h,directives:[{name:"ant-ref",value:this.saveInputRef},{name:"ant-input"}],on:{input:this.onInputChange,keydown:be(this.onInputKeydown,f.keydown,Object(C["j"])(this).inputKeydown),focus:be(this.inputFocus,f.focus),blur:be(this.inputBlur,f.blur)}}),e("span",i()([{directives:[{name:"ant-ref",value:this.saveInputMirrorRef}]},{class:t.prefixCls+"-search__field__mirror"}]),[a," "])])},getInputDOMNode:function(){return this.topCtrlRef?this.topCtrlRef.querySelector("input,textarea,div[contentEditable]"):this.inputRef},getInputMirrorDOMNode:function(){return this.inputMirrorRef},getPopupDOMNode:function(){if(this.selectTriggerRef)return this.selectTriggerRef.getPopupDOMNode()},getPopupMenuComponent:function(){if(this.selectTriggerRef)return this.selectTriggerRef.getInnerMenu()},setOpenState:function(e){var t=this,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},r=this.$props,i=this.$data,a=n.needFocus,o=n.fireSearch;if(i._open!==e){this.__emit("dropdownVisibleChange",e);var s={_open:e,_backfillValue:""};!e&&q(r)&&r.showSearch&&this.setInputValue("",o),e||this.maybeFocus(e,!!a),this.setState(s,(function(){e&&t.maybeFocus(e,!!a)}))}else this.maybeFocus(e,!!a)},setInputValue:function(e){var t=!(arguments.length>1&&void 0!==arguments[1])||arguments[1];e!==this.$data._inputValue&&(this.setState({_inputValue:e},this.forcePopupAlign),t&&this.$emit("search",e))},getValueByInput:function(e){var t=this,n=this.$props,r=n.multiple,i=n.tokenSeparators,a=this.$data._value,o=!1;return ie(e,i).forEach((function(e){var n=[e];if(r){var i=t.getValueByLabel(e);i&&-1===J(a,i)&&(a=a.concat(i),o=!0,t.fireSelect(i))}else-1===J(a,e)&&(a=a.concat(n),o=!0,t.fireSelect(e))})),o?a:void 0},getRealOpenState:function(e){var t=this.$props.open;if("boolean"===typeof t)return t;var n=(e||this.$data)._open,r=this._options||[];return!U(this.$props)&&this.$props.showSearch||n&&!r.length&&(n=!1),n},focus:function(){q(this.$props)&&this.selectionRef?this.selectionRef.focus():this.getInputDOMNode()&&this.getInputDOMNode().focus()},blur:function(){q(this.$props)&&this.selectionRef?this.selectionRef.blur():this.getInputDOMNode()&&this.getInputDOMNode().blur()},markMouseDown:function(){this._mouseDown=!0},markMouseLeave:function(){this._mouseDown=!1},handleBackfill:function(e){if(this.backfill&&(q(this.$props)||$(this.$props))){var t=R(e);$(this.$props)&&this.setInputValue(t,!1),this.setState({_value:[t],_backfillValue:t})}},_filterOption:function(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:ae,r=this.$data,i=r._value,a=r._backfillValue,o=i[i.length-1];if(!e||o&&o===a)return!0;var s=this.$props.filterOption;return Object(C["r"])(this,"filterOption")?!0===s&&(s=n.bind(this)):s=n.bind(this),!s||("function"===typeof s?s.call(this,e,t):!Object(C["q"])(t,"disabled"))},timeoutFocus:function(){var e=this;this.focusTimer&&this.clearFocusTime(),this.focusTimer=window.setTimeout((function(){e.$emit("focus")}),10)},clearFocusTime:function(){this.focusTimer&&(clearTimeout(this.focusTimer),this.focusTimer=null)},clearBlurTime:function(){this.blurTimer&&(clearTimeout(this.blurTimer),this.blurTimer=null)},clearComboboxTime:function(){this.comboboxTimer&&(clearTimeout(this.comboboxTimer),this.comboboxTimer=null)},updateFocusClassName:function(){var e=this.rootRef,t=this.prefixCls;this._focused?_()(e).add(t+"-focused"):_()(e).remove(t+"-focused")},maybeFocus:function(e,t){if(t||e){var n=this.getInputDOMNode(),r=document,i=r.activeElement;n&&(e||U(this.$props))?i!==n&&(n.focus(),this._focused=!0):i!==this.selectionRef&&this.selectionRef&&(this.selectionRef.focus(),this._focused=!0)}},removeSelected:function(e,t){var n=this.$props;if(!n.disabled&&!this.isChildDisabled(e)){t&&t.stopPropagation&&t.stopPropagation();var r=this.$data._value,i=r.filter((function(t){return t!==e})),a=W(n);if(a){var o=e;n.labelInValue&&(o={key:e,label:this.getLabelBySingleValue(e)}),this.$emit("deselect",o,this.getOptionBySingleValue(e))}this.fireChange(i)}},openIfHasChildren:function(){var e=this.$props;(e.children&&e.children.length||q(e))&&this.setOpenState(!0)},fireSelect:function(e){this.$emit("select",this.getVLBySingleValue(e),this.getOptionBySingleValue(e))},fireChange:function(e){Object(C["r"])(this,"value")||this.setState({_value:e},this.forcePopupAlign);var t=this.getVLForOnChange(e),n=this.getOptionsBySingleValue(e);this._valueOptions=n,this.$emit("change",t,W(this.$props)?n:n[0])},isChildDisabled:function(e){return(this.$props.children||[]).some((function(t){var n=R(t);return n===e&&Object(C["q"])(t,"disabled")}))},forcePopupAlign:function(){this.$data._open&&this.selectTriggerRef&&this.selectTriggerRef.triggerRef&&this.selectTriggerRef.triggerRef.forcePopupAlign()},renderFilterOptions:function(){var e=this.$createElement,t=this.$data._inputValue,n=this.$props,r=n.children,a=n.tags,o=n.notFoundContent,s=[],c=[],l=!1,h=this.renderFilterOptionsFromChildren(r,c,s);if(a){var f=this.$data._value;if(f=f.filter((function(e){return-1===c.indexOf(e)&&(!t||String(e).indexOf(String(t))>-1)})),f.sort((function(e,t){return e.length-t.length})),f.forEach((function(t){var n=t,r=u()({},te,{role:"option"}),a=e(w["a"],i()([{style:ee},{attrs:r},{attrs:{value:n},key:n}]),[n]);h.push(a),s.push(a)})),t&&s.every((function(e){return R(e)!==t}))){var d={attrs:te,key:t,props:{value:t,role:"option"},style:ee};h.unshift(e(w["a"],d,[t]))}}if(!h.length&&o){l=!0;var p={attrs:te,key:"NOT_FOUND",props:{value:"NOT_FOUND",disabled:!0,role:"option"},style:ee};h=[e(w["a"],p,[o])]}return{empty:l,options:h}},renderFilterOptionsFromChildren:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=this,n=arguments[1],r=arguments[2],a=this.$createElement,o=[],s=this.$props,c=this.$data._inputValue,l=s.tags;return e.forEach((function(e){if(e.data&&void 0===e.data.slot)if(Object(C["n"])(e).isSelectOptGroup){var s=Object(C["f"])(e,"label"),h=e.key;h||"string"!==typeof s?!s&&h&&(s=h):h=s;var f=Object(C["o"])(e)["default"];if(f="function"===typeof f?f():f,c&&t._filterOption(c,e)){var d=f.map((function(e){var t=R(e)||e.key;return a(w["a"],i()([{key:t,attrs:{value:t}},e.data]),[e.componentOptions.children])}));o.push(a(M["a"],{key:h,attrs:{title:s},class:Object(C["e"])(e)},[d]))}else{var p=t.renderFilterOptionsFromChildren(f,n,r);p.length&&o.push(a(M["a"],i()([{key:h,attrs:{title:s}},e.data]),[p]))}}else{S()(Object(C["n"])(e).isSelectOption,"the children of `Select` should be `Select.Option` or `Select.OptGroup`, instead of `"+(Object(C["n"])(e).name||Object(C["n"])(e))+"`.");var m=R(e);if(oe(m,t.$props),t._filterOption(c,e)){var v={attrs:u()({},te,Object(C["d"])(e)),key:m,props:u()({value:m},Object(C["l"])(e),{role:"option"}),style:ee,on:Object(C["h"])(e),class:Object(C["e"])(e)},g=a(w["a"],v,[e.componentOptions.children]);o.push(g),r.push(g)}l&&n.push(m)}})),o},renderTopControlNode:function(){var e=this,t=this.$createElement,n=this.$props,r=this.$data,a=r._value,o=r._inputValue,s=r._open,c=n.choiceTransitionName,l=n.prefixCls,h=n.maxTagTextLength,f=n.maxTagCount,d=n.maxTagPlaceholder,p=n.showSearch,m=Object(C["f"])(this,"removeIcon"),v=l+"-selection__rendered",g=null;if(q(n)){var b=null;if(a.length){var y=!1,_=1;p&&s?(y=!o,y&&(_=.4)):y=!0;var w=a[0],M=this.getOptionInfoBySingleValue(w),x=M.label,S=M.title;b=t("div",{key:"value",class:l+"-selection-selected-value",attrs:{title:F(S||x)},style:{display:y?"block":"none",opacity:_}},[x])}g=p?[b,t("div",{class:l+"-search "+l+"-search--inline",key:"input",style:{display:s?"block":"none"}},[this._getInputElement()])]:[b]}else{var k=[],T=a,O=void 0;if(void 0!==f&&a.length>f){T=T.slice(0,f);var z=this.getVLForOnChange(a.slice(f,a.length)),D="+ "+(a.length-f)+" ...";d&&(D="function"===typeof d?d(z):d);var j=u()({},te,{role:"presentation",title:F(D)});O=t("li",i()([{style:ee},{attrs:j},{on:{mousedown:X},class:l+"-selection__choice "+l+"-selection__choice__disabled",key:"maxTagPlaceholder"}]),[t("div",{class:l+"-selection__choice__content"},[D])])}if(W(n)&&(k=T.map((function(n){var r=e.getOptionInfoBySingleValue(n),a=r.label,o=r.title||a;h&&"string"===typeof a&&a.length>h&&(a=a.slice(0,h)+"...");var s=e.isChildDisabled(n),c=s?l+"-selection__choice "+l+"-selection__choice__disabled":l+"-selection__choice",f=u()({},te,{role:"presentation",title:F(o)});return t("li",i()([{style:ee},{attrs:f},{on:{mousedown:X},class:c,key:n||me}]),[t("div",{class:l+"-selection__choice__content"},[a]),s?null:t("span",{on:{click:function(t){e.removeSelected(n,t)}},class:l+"-selection__choice__remove"},[m||t("i",{class:l+"-selection__choice__remove-icon"},["×"])])])}))),O&&k.push(O),k.push(t("li",{class:l+"-search "+l+"-search--inline",key:"__input"},[this._getInputElement()])),W(n)&&c){var H=Object(L["a"])(c,{tag:"ul",afterLeave:this.onChoiceAnimationLeave});g=t("transition-group",H,[k])}else g=t("ul",[k])}return t("div",i()([{class:v},{directives:[{name:"ant-ref",value:this.saveTopCtrlRef}]},{on:{click:this.topCtrlContainerClick}}]),[this.getPlaceholderElement(),g])},renderArrow:function(e){var t=this.$createElement,n=this.$props,r=n.showArrow,a=void 0===r?!e:r,o=n.loading,s=n.prefixCls,c=Object(C["f"])(this,"inputIcon");if(!a&&!o)return null;var l=t("i",o?{class:s+"-arrow-loading"}:{class:s+"-arrow-icon"});return t("span",i()([{key:"arrow",class:s+"-arrow",style:ee},{attrs:te},{on:{click:this.onArrowClick},ref:"arrow"}]),[c||l])},topCtrlContainerClick:function(e){this.$data._open&&!q(this.$props)&&e.stopPropagation()},renderClear:function(){var e=this.$createElement,t=this.$props,n=t.prefixCls,r=t.allowClear,a=this.$data,o=a._value,s=a._inputValue,c=Object(C["f"])(this,"clearIcon"),l=e("span",i()([{key:"clear",class:n+"-selection__clear",on:{mousedown:X},style:ee},{attrs:te},{on:{click:this.onClearSelection}}]),[c||e("i",{class:n+"-selection__clear-icon"},["×"])]);return r?$(this.$props)?s?l:null:s||o.length?l:null:null},selectionRefClick:function(){if(!this.disabled){var e=this.getInputDOMNode();this._focused&&this.$data._open?(this.setOpenState(!1,!1),e&&e.blur()):(this.clearBlurTime(),this.setOpenState(!0,!0),e&&e.focus())}},selectionRefFocus:function(e){this._focused||this.disabled||U(this.$props)?e.preventDefault():(this._focused=!0,this.updateFocusClassName(),this.$emit("focus"))},selectionRefBlur:function(e){U(this.$props)?e.preventDefault():this.inputBlur(e)}},render:function(){var e,t=arguments[0],n=this.$props,r=W(n),a=n.showArrow,s=void 0===a||a,c=this.$data,l=n.disabled,u=n.prefixCls,h=n.loading,f=this.renderTopControlNode(),d=this.$data,p=d._open,m=d._inputValue,v=d._value;if(p){var g=this.renderFilterOptions();this._empty=g.empty,this._options=g.options}var y=this.getRealOpenState(),_=this._empty,w=this._options||[],M=Object(C["j"])(this),x=M.mouseenter,S=void 0===x?ve:x,k=M.mouseleave,L=void 0===k?ve:k,T=M.popupScroll,O=void 0===T?ve:T,z={props:{},attrs:{role:"combobox","aria-autocomplete":"list","aria-haspopup":"true","aria-expanded":y,"aria-controls":this.$data._ariaId},on:{},class:u+"-selection "+u+"-selection--"+(r?"multiple":"single"),key:"selection"},D={attrs:{tabIndex:-1}};U(n)||(D.attrs.tabIndex=n.disabled?-1:n.tabIndex);var j=(e={},o()(e,u,!0),o()(e,u+"-open",p),o()(e,u+"-focused",p||!!this._focused),o()(e,u+"-combobox",$(n)),o()(e,u+"-disabled",l),o()(e,u+"-enabled",!l),o()(e,u+"-allow-clear",!!n.allowClear),o()(e,u+"-no-arrow",!s),o()(e,u+"-loading",!!h),e);return t(he,i()([{attrs:{dropdownAlign:n.dropdownAlign,dropdownClassName:n.dropdownClassName,dropdownMatchSelectWidth:n.dropdownMatchSelectWidth,defaultActiveFirstOption:n.defaultActiveFirstOption,dropdownMenuStyle:n.dropdownMenuStyle,transitionName:n.transitionName,animation:n.animation,prefixCls:n.prefixCls,dropdownStyle:n.dropdownStyle,combobox:n.combobox,showSearch:n.showSearch,options:w,empty:_,multiple:r,disabled:l,visible:y,inputValue:m,value:v,backfillValue:c._backfillValue,firstActiveValue:n.firstActiveValue,getPopupContainer:n.getPopupContainer,showAction:n.showAction,menuItemSelectedIcon:Object(C["f"])(this,"menuItemSelectedIcon")},on:{dropdownVisibleChange:this.onDropdownVisibleChange,menuSelect:this.onMenuSelect,menuDeselect:this.onMenuDeselect,popupScroll:O,popupFocus:this.onPopupFocus,mouseenter:S,mouseleave:L}},{directives:[{name:"ant-ref",value:this.saveSelectTriggerRef}]},{attrs:{dropdownRender:n.dropdownRender,ariaId:this.$data._ariaId}}]),[t("div",i()([{directives:[{name:"ant-ref",value:be(this.saveRootRef,this.saveSelectionRef)}]},{style:Object(C["p"])(this),class:b()(j),on:{mousedown:this.markMouseDown,mouseup:this.markMouseLeave,mouseout:this.markMouseLeave}},D,{on:{blur:this.selectionRefBlur,focus:this.selectionRefFocus,click:this.selectionRefClick,keydown:U(n)?ve:this.onKeyDown}}]),[t("div",z,[f,this.renderClear(),this.renderArrow(!!r)])])])}},_e=(Object(z["a"])(ye),n("4df5")),we=n("0c63"),Me=n("db14"),xe=function(){return{prefixCls:d["a"].string,size:d["a"].oneOf(["small","large","default"]),showAction:d["a"].oneOfType([d["a"].string,d["a"].arrayOf(String)]),notFoundContent:d["a"].any,transitionName:d["a"].string,choiceTransitionName:d["a"].string,showSearch:d["a"].bool,allowClear:d["a"].bool,disabled:d["a"].bool,tabIndex:d["a"].number,placeholder:d["a"].any,defaultActiveFirstOption:d["a"].bool,dropdownClassName:d["a"].string,dropdownStyle:d["a"].any,dropdownMenuStyle:d["a"].any,dropdownMatchSelectWidth:d["a"].bool,filterOption:d["a"].oneOfType([d["a"].bool,d["a"].func]),autoFocus:d["a"].bool,backfill:d["a"].bool,showArrow:d["a"].bool,getPopupContainer:d["a"].func,open:d["a"].bool,defaultOpen:d["a"].bool,autoClearSearchValue:d["a"].bool,dropdownRender:d["a"].func,loading:d["a"].bool}},Se=d["a"].shape({key:d["a"].oneOfType([d["a"].string,d["a"].number])}).loose,ke=d["a"].oneOfType([d["a"].string,d["a"].number,d["a"].arrayOf(d["a"].oneOfType([Se,d["a"].string,d["a"].number])),Se]),Ce=u()({},xe(),{value:ke,defaultValue:ke,mode:d["a"].string,optionLabelProp:d["a"].string,firstActiveValue:d["a"].oneOfType([String,d["a"].arrayOf(String)]),maxTagCount:d["a"].number,maxTagPlaceholder:d["a"].any,maxTagTextLength:d["a"].number,dropdownMatchSelectWidth:d["a"].bool,optionFilterProp:d["a"].string,labelInValue:d["a"].boolean,getPopupContainer:d["a"].func,tokenSeparators:d["a"].arrayOf(d["a"].string),getInputElement:d["a"].func,options:d["a"].array,suffixIcon:d["a"].any,removeIcon:d["a"].any,clearIcon:d["a"].any,menuItemSelectedIcon:d["a"].any}),Le={prefixCls:d["a"].string,size:d["a"].oneOf(["default","large","small"]),notFoundContent:d["a"].any,showSearch:d["a"].bool,optionLabelProp:d["a"].string,transitionName:d["a"].string,choiceTransitionName:d["a"].string},Te="SECRET_COMBOBOX_MODE_DO_NOT_USE",Oe={SECRET_COMBOBOX_MODE_DO_NOT_USE:Te,Option:u()({},p,{name:"ASelectOption"}),OptGroup:u()({},m,{name:"ASelectOptGroup"}),name:"ASelect",props:u()({},Ce,{showSearch:d["a"].bool.def(!1),transitionName:d["a"].string.def("slide-up"),choiceTransitionName:d["a"].string.def("zoom")}),propTypes:Le,model:{prop:"value",event:"change"},provide:function(){return{savePopupRef:this.savePopupRef}},inject:{configProvider:{default:function(){return 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e,t=arguments[0],n=Object(C["k"])(this),r=n.prefixCls,a=n.size,s=n.mode,l=n.options,h=n.getPopupContainer,d=n.showArrow,m=c()(n,["prefixCls","size","mode","options","getPopupContainer","showArrow"]),v=this.configProvider.getPrefixCls,g=this.configProvider.renderEmpty,b=v("select",r),y=this.configProvider.getPopupContainer,_=Object(C["f"])(this,"removeIcon");_=Array.isArray(_)?_[0]:_;var w=Object(C["f"])(this,"clearIcon");w=Array.isArray(w)?w[0]:w;var M=Object(C["f"])(this,"menuItemSelectedIcon");M=Array.isArray(M)?M[0]:M;var x=Object(f["a"])(m,["inputIcon","removeIcon","clearIcon","suffixIcon","menuItemSelectedIcon"]),S=(e={},o()(e,b+"-lg","large"===a),o()(e,b+"-sm","small"===a),o()(e,b+"-show-arrow",d),e),k=this.$props.optionLabelProp;this.isCombobox()&&(k=k||"value");var 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e=this._key,t=e.words,n=[],r=0;r<56;r++){var i=o[r]-1;n[r]=t[i>>>5]>>>31-i%32&1}for(var a=this._subKeys=[],l=0;l<16;l++){var u=a[l]=[],h=c[l];for(r=0;r<24;r++)u[r/6|0]|=n[(s[r]-1+h)%28]<<31-r%6,u[4+(r/6|0)]|=n[28+(s[r+24]-1+h)%28]<<31-r%6;u[0]=u[0]<<1|u[0]>>>31;for(r=1;r<7;r++)u[r]=u[r]>>>4*(r-1)+3;u[7]=u[7]<<5|u[7]>>>27}var f=this._invSubKeys=[];for(r=0;r<16;r++)f[r]=a[15-r]},encryptBlock:function(e,t){this._doCryptBlock(e,t,this._subKeys)},decryptBlock:function(e,t){this._doCryptBlock(e,t,this._invSubKeys)},_doCryptBlock:function(e,t,n){this._lBlock=e[t],this._rBlock=e[t+1],f.call(this,4,252645135),f.call(this,16,65535),d.call(this,2,858993459),d.call(this,8,16711935),f.call(this,1,1431655765);for(var r=0;r<16;r++){for(var i=n[r],a=this._lBlock,o=this._rBlock,s=0,c=0;c<8;c++)s|=l[c][((o^i[c])&u[c])>>>0];this._lBlock=o,this._rBlock=a^s}var h=this._lBlock;this._lBlock=this._rBlock,this._rBlock=h,f.call(this,1,1431655765),d.call(this,8,16711935),d.call(this,2,858993459),f.call(this,16,65535),f.call(this,4,252645135),e[t]=this._lBlock,e[t+1]=this._rBlock},keySize:2,ivSize:2,blockSize:2});function f(e,t){var n=(this._lBlock>>>e^this._rBlock)&t;this._rBlock^=n,this._lBlock^=n<>>e^this._lBlock)&t;this._lBlock^=n,this._rBlock^=n<192.");var n=t.slice(0,2),i=t.length<4?t.slice(0,2):t.slice(2,4),a=t.length<6?t.slice(0,2):t.slice(4,6);this._des1=h.createEncryptor(r.create(n)),this._des2=h.createEncryptor(r.create(i)),this._des3=h.createEncryptor(r.create(a))},encryptBlock:function(e,t){this._des1.encryptBlock(e,t),this._des2.decryptBlock(e,t),this._des3.encryptBlock(e,t)},decryptBlock:function(e,t){this._des3.decryptBlock(e,t),this._des2.encryptBlock(e,t),this._des1.decryptBlock(e,t)},keySize:6,ivSize:2,blockSize:2});t.TripleDES=i._createHelper(p)}(),e.TripleDES}))},a434:function(e,t,n){"use strict";var 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h=e.slice(n.place,u+n.place);if(0===l[0]){if(!(128&l[1]))return!1;l=l.slice(1)}if(0===h[0]){if(!(128&h[1]))return!1;h=h.slice(1)}return this.r=new r(l),this.s=new r(h),this.recoveryParam=null,!0},o.prototype.toDER=function(e){var t=this.r.toArray(),n=this.s.toArray();128&t[0]&&(t=[0].concat(t)),128&n[0]&&(n=[0].concat(n)),t=l(t),n=l(n);while(!n[0]&&!(128&n[1]))n=n.slice(1);var r=[2];u(r,t.length),r=r.concat(t),r.push(2),u(r,n.length);var a=r.concat(n),o=[48];return u(o,a.length),o=o.concat(a),i.encode(o,e)}},b7d1:function(e,t,n){(function(t){function n(e,t){if(r("noDeprecation"))return e;var n=!1;function i(){if(!n){if(r("throwDeprecation"))throw new Error(t);r("traceDeprecation")?console.trace(t):console.warn(t),n=!0}return e.apply(this,arguments)}return i}function r(e){try{if(!t.localStorage)return!1}catch(r){return!1}var n=t.localStorage[e];return 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t(this._ah,this._al,0),t(this._bh,this._bl,8),t(this._ch,this._cl,16),t(this._dh,this._dl,24),t(this._eh,this._el,32),t(this._fh,this._fl,40),e},e.exports=c},b84c:function(e,t,n){(function(e,t){t(n("c1df"))})(0,(function(e){"use strict"; -//! moment.js locale configuration -var t=e.defineLocale("nn",{months:"januar_februar_mars_april_mai_juni_juli_august_september_oktober_november_desember".split("_"),monthsShort:"jan._feb._mars_apr._mai_juni_juli_aug._sep._okt._nov._des.".split("_"),monthsParseExact:!0,weekdays:"sundag_måndag_tysdag_onsdag_torsdag_fredag_laurdag".split("_"),weekdaysShort:"su._må._ty._on._to._fr._lau.".split("_"),weekdaysMin:"su_må_ty_on_to_fr_la".split("_"),weekdaysParseExact:!0,longDateFormat:{LT:"HH:mm",LTS:"HH:mm:ss",L:"DD.MM.YYYY",LL:"D. MMMM YYYY",LLL:"D. MMMM YYYY [kl.] H:mm",LLLL:"dddd D. MMMM YYYY [kl.] HH:mm"},calendar:{sameDay:"[I dag klokka] LT",nextDay:"[I morgon klokka] LT",nextWeek:"dddd [klokka] LT",lastDay:"[I går klokka] LT",lastWeek:"[Føregåande] dddd [klokka] LT",sameElse:"L"},relativeTime:{future:"om %s",past:"%s sidan",s:"nokre sekund",ss:"%d sekund",m:"eit minutt",mm:"%d minutt",h:"ein time",hh:"%d timar",d:"ein dag",dd:"%d dagar",M:"ein månad",MM:"%d månader",y:"eit år",yy:"%d år"},dayOfMonthOrdinalParse:/\d{1,2}\./,ordinal:"%d.",week:{dow:1,doy:4}});return t}))},b86b:function(e,t,n){(function(t,r,i){e.exports=r(n("21bf"),n("3252"),n("d6e6"))})(0,(function(e){return function(){var t=e,n=t.x64,r=n.Word,i=n.WordArray,a=t.algo,o=a.SHA512,s=a.SHA384=o.extend({_doReset:function(){this._hash=new i.init([new r.init(3418070365,3238371032),new r.init(1654270250,914150663),new r.init(2438529370,812702999),new r.init(355462360,4144912697),new r.init(1731405415,4290775857),new r.init(2394180231,1750603025),new r.init(3675008525,1694076839),new r.init(1203062813,3204075428)])},_doFinalize:function(){var e=o._doFinalize.call(this);return e.sigBytes-=16,e}});t.SHA384=o._createHelper(s),t.HmacSHA384=o._createHmacHelper(s)}(),e.SHA384}))},b86c:function(e,t,n){(function(t,r,i){e.exports=r(n("21bf"),n("38ba"))})(0,(function(e){return e.pad.NoPadding={pad:function(){},unpad:function(){}},e.pad.NoPadding}))},b8e7:function(e,t,n){},b904:function(e,t,n){function r(){return n("81a5"),{}}e.exports=r},b92b:function(e,t,n){"use strict";var r=n("4d91");t["a"]=function(){return{prefixCls:r["a"].string,type:r["a"].string,htmlType:r["a"].oneOf(["button","submit","reset"]).def("button"),icon:r["a"].any,shape:r["a"].oneOf(["circle","circle-outline","round"]),size:r["a"].oneOf(["small","large","default"]).def("default"),loading:r["a"].oneOfType([r["a"].bool,r["a"].object]),disabled:r["a"].bool,ghost:r["a"].bool,block:r["a"].bool}}},b97c:function(e,t,n){(function(e,t){t(n("c1df"))})(0,(function(e){"use strict"; -//! moment.js locale configuration -var t={ss:"sekundes_sekundēm_sekunde_sekundes".split("_"),m:"minūtes_minūtēm_minūte_minūtes".split("_"),mm:"minūtes_minūtēm_minūte_minūtes".split("_"),h:"stundas_stundām_stunda_stundas".split("_"),hh:"stundas_stundām_stunda_stundas".split("_"),d:"dienas_dienām_diena_dienas".split("_"),dd:"dienas_dienām_diena_dienas".split("_"),M:"mēneša_mēnešiem_mēnesis_mēneši".split("_"),MM:"mēneša_mēnešiem_mēnesis_mēneši".split("_"),y:"gada_gadiem_gads_gadi".split("_"),yy:"gada_gadiem_gads_gadi".split("_")};function n(e,t,n){return n?t%10===1&&t%100!==11?e[2]:e[3]:t%10===1&&t%100!==11?e[0]:e[1]}function r(e,r,i){return e+" "+n(t[i],e,r)}function i(e,r,i){return n(t[i],e,r)}function a(e,t){return t?"dažas sekundes":"dažām sekundēm"}var o=e.defineLocale("lv",{months:"janvāris_februāris_marts_aprīlis_maijs_jūnijs_jūlijs_augusts_septembris_oktobris_novembris_decembris".split("_"),monthsShort:"jan_feb_mar_apr_mai_jūn_jūl_aug_sep_okt_nov_dec".split("_"),weekdays:"svētdiena_pirmdiena_otrdiena_trešdiena_ceturtdiena_piektdiena_sestdiena".split("_"),weekdaysShort:"Sv_P_O_T_C_Pk_S".split("_"),weekdaysMin:"Sv_P_O_T_C_Pk_S".split("_"),weekdaysParseExact:!0,longDateFormat:{LT:"HH:mm",LTS:"HH:mm:ss",L:"DD.MM.YYYY.",LL:"YYYY. [gada] D. MMMM",LLL:"YYYY. [gada] D. MMMM, HH:mm",LLLL:"YYYY. [gada] D. MMMM, dddd, HH:mm"},calendar:{sameDay:"[Šodien pulksten] LT",nextDay:"[Rīt pulksten] LT",nextWeek:"dddd [pulksten] LT",lastDay:"[Vakar pulksten] LT",lastWeek:"[Pagājušā] dddd [pulksten] LT",sameElse:"L"},relativeTime:{future:"pēc %s",past:"pirms %s",s:a,ss:r,m:i,mm:r,h:i,hh:r,d:i,dd:r,M:i,MM:r,y:i,yy:r},dayOfMonthOrdinalParse:/\d{1,2}\./,ordinal:"%d.",week:{dow:1,doy:4}});return o}))},b97c3:function(e,t,n){"use strict";n("b2a3"),n("a54e")},b9a8:function(e,t,n){"use strict";var r=n("399f"),i=n("6aa2"),a=n("f3a3"),o=n("0cbb"),s=n("fdac"),c=a.assert,l=n("bb34"),u=n("b73f");function h(e){if(!(this instanceof h))return new h(e);"string"===typeof e&&(c(o.hasOwnProperty(e),"Unknown curve "+e),e=o[e]),e instanceof o.PresetCurve&&(e={curve:e}),this.curve=e.curve.curve,this.n=this.curve.n,this.nh=this.n.ushrn(1),this.g=this.curve.g,this.g=e.curve.g,this.g.precompute(e.curve.n.bitLength()+1),this.hash=e.hash||e.curve.hash}e.exports=h,h.prototype.keyPair=function(e){return new l(this,e)},h.prototype.keyFromPrivate=function(e,t){return l.fromPrivate(this,e,t)},h.prototype.keyFromPublic=function(e,t){return l.fromPublic(this,e,t)},h.prototype.genKeyPair=function(e){e||(e={});var t=new i({hash:this.hash,pers:e.pers,persEnc:e.persEnc||"utf8",entropy:e.entropy||s(this.hash.hmacStrength),entropyEnc:e.entropy&&e.entropyEnc||"utf8",nonce:this.n.toArray()}),n=this.n.byteLength(),a=this.n.sub(new r(2));do{var o=new r(t.generate(n));if(!(o.cmp(a)>0))return o.iaddn(1),this.keyFromPrivate(o)}while(1)},h.prototype._truncateToN=function(e,t){var n=8*e.byteLength()-this.n.bitLength();return n>0&&(e=e.ushrn(n)),!t&&e.cmp(this.n)>=0?e.sub(this.n):e},h.prototype.sign=function(e,t,n,a){"object"===typeof n&&(a=n,n=null),a||(a={}),t=this.keyFromPrivate(t,n),e=this._truncateToN(new r(e,16));for(var o=this.n.byteLength(),s=t.getPrivate().toArray("be",o),c=e.toArray("be",o),l=new i({hash:this.hash,entropy:s,nonce:c,pers:a.pers,persEnc:a.persEnc||"utf8"}),h=this.n.sub(new r(1)),f=0;1;f++){var d=a.k?a.k(f):new r(l.generate(this.n.byteLength()));if(d=this._truncateToN(d,!0),!(d.cmpn(1)<=0||d.cmp(h)>=0)){var p=this.g.mul(d);if(!p.isInfinity()){var m=p.getX(),v=m.umod(this.n);if(0!==v.cmpn(0)){var g=d.invm(this.n).mul(v.mul(t.getPrivate()).iadd(e));if(g=g.umod(this.n),0!==g.cmpn(0)){var b=(p.getY().isOdd()?1:0)|(0!==m.cmp(v)?2:0);return a.canonical&&g.cmp(this.nh)>0&&(g=this.n.sub(g),b^=1),new u({r:v,s:g,recoveryParam:b})}}}}}},h.prototype.verify=function(e,t,n,i){e=this._truncateToN(new r(e,16)),n=this.keyFromPublic(n,i),t=new u(t,"hex");var a=t.r,o=t.s;if(a.cmpn(1)<0||a.cmp(this.n)>=0)return!1;if(o.cmpn(1)<0||o.cmp(this.n)>=0)return!1;var s=o.invm(this.n),c=s.mul(e).umod(this.n),l=s.mul(a).umod(this.n);if(!this.curve._maxwellTrick){var h=this.g.mulAdd(c,n.getPublic(),l);return!h.isInfinity()&&0===h.getX().umod(this.n).cmp(a)}h=this.g.jmulAdd(c,n.getPublic(),l);return!h.isInfinity()&&h.eqXToP(a)},h.prototype.recoverPubKey=function(e,t,n,i){c((3&n)===n,"The recovery param is more than two bits"),t=new u(t,i);var a=this.n,o=new r(e),s=t.r,l=t.s,h=1&n,f=n>>1;if(s.cmp(this.curve.p.umod(this.curve.n))>=0&&f)throw new Error("Unable to find sencond key candinate");s=f?this.curve.pointFromX(s.add(this.curve.n),h):this.curve.pointFromX(s,h);var d=t.r.invm(a),p=a.sub(o).mul(d).umod(a),m=l.mul(d).umod(a);return this.g.mulAdd(p,s,m)},h.prototype.getKeyRecoveryParam=function(e,t,n,r){if(t=new u(t,r),null!==t.recoveryParam)return t.recoveryParam;for(var i=0;i<4;i++){var a;try{a=this.recoverPubKey(e,t,i)}catch(e){continue}if(a.eq(n))return i}throw new Error("Unable to find valid recovery factor")}},b9b5:function(e,t,n){"use strict";var r=n("9d8a").codes.ERR_INVALID_OPT_VALUE;function i(e,t,n){return null!=e.highWaterMark?e.highWaterMark:t?e[n]:null}function a(e,t,n,a){var o=i(t,a,n);if(null!=o){if(!isFinite(o)||Math.floor(o)!==o||o<0){var s=a?n:"highWaterMark";throw new r(s,o)}return Math.floor(o)}return 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this.$store.state.labels.currentProId}},watch:{isImage:function(t,e){this.isPreservation=!t}},data:function(){return{colorPicker:null,isSubmit:!1,hasSelectDrawType:"0",isPreservation:!0,rectActiveStyle:!1,polygonActiveStyle:!1,editActiveStyle:!1,preDrawType:null}},mounted:function(){this.hasSelectDrawType="0",this.initFillColor(),this.initBorderColor()},methods:{uploadImg:function(){this.$emit("uploadImg",{selectTabs:"1",selectItem:0,fileAttribute:"fileImage"}),this.isSubmit=!0},echoImg:function(t){this.$emit("echoImg",{selectTabs:"1",selectItem:0,fileAttribute:"file",selectType:t}),this.isSubmit=!0},initFillColor:function(){var t=this,e=this;this.$nextTick((function(){t.colorPicker=new ut({el:"#color-picker",size:{width:200,height:32},isLog:!1,alpha:!0,defaultColor:t.fillColor,predefineColor:["#fff","#2396ef"],sure:e.handler,clear:e.clearColor,openPickerAni:"opacity"})}))},initBorderColor:function(){var t=this,e=this;this.$nextTick((function(){t.borderColorPicker=new ut({el:"#color-border",size:{width:200,height:32},isLog:!1,alpha:!1,defaultColor:t.borderColor,predefineColor:["#fff","#2396ef"],sure:e.sureBorder,clear:e.clearColorBorder,openPickerAni:"opacity"})}))},handler:function(t){this.$store.commit(pt["e"],t),this.changeSelectedColorVal.id&&(this.changeSelectedColorVal.fill=t,this.$emit("chanageSelectedColor",this.changeSelectedColorVal))},clearColor:function(){this.$store.commit(pt["e"],"rgba(255, 255, 255, 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t=this,e=t.$createElement,a=t._self._c||e;return a("a-drawer",{attrs:{title:t.$t("drawerTool.drawerTool55"),placement:"right",visible:t.visible,width:370,"after-visible-change":t.afterVisibleChange},on:{close:t.onClose}},[a("div",{staticClass:"handle-term"},[a("div",{staticClass:"tagname"},[a("div",{staticClass:"item-title"},[t._v(t._s(t.$t("aDrawer.labelName")))]),a("a-input",{staticClass:"container marLeft10",attrs:{placeholder:t.$t("drawerTool.drawerTool56")},on:{change:t.changeFn},model:{value:t.tagName,callback:function(e){t.tagName=e},expression:"tagName"}})],1),a("div",{staticClass:"boderColor flexBasi adrawer",staticStyle:{marginTop:"6px"}},[a("div",{staticClass:"item-title"},[t._v(t._s(t.$t("aDrawer.borderColor")))]),a("div",{staticClass:"container"},[a("div",{attrs:{id:"colorBorder"}})])]),a("div",{staticClass:"fill flexBasi adrawer"},[a("div",{staticClass:"item-title"},[t._v(t._s(t.$t("aDrawer.fillColor")))]),a("div",{staticClass:"container"},[a("div",{attrs:{id:"colorPicker"}})])])]),a("div",{staticClass:"handle-explain"},[a("div",{staticClass:"title"},[t._v(t._s(t.$t("aDrawer.operatingInstructions")))]),a("div",{staticClass:"content"},[a("div",{staticClass:"explain-item"},[a("b",[t._v("1、")]),a("span",{staticStyle:{color:"#6DD451","font-weight":"600"}},[t._v(t._s(t.$t("aDrawer.labelName2")))]),t._v(t._s(t.$t("aDrawer.inputLabelImediately"))+" ")]),a("div",{staticClass:"explain-item"},[a("b",[t._v("2、")]),a("span",{staticStyle:{color:"#406DCF","font-weight":"600"}},[t._v(t._s(t.$t("aDrawer.borderColor2")))]),t._v(t._s(t.$t("aDrawer.relevantValueChange"))+" ")]),a("div",{staticClass:"explain-item"},[a("b",[t._v("3、")]),a("span",{staticStyle:{color:"#C42B51","font-weight":"600"}},[t._v(t._s(t.$t("aDrawer.fillColor2")))]),t._v(t._s(t.$t("aDrawer.relevantValueChange2"))+" ")])])]),a("div",{staticClass:"handle-explain"},[a("div",{staticClass:"title"},[t._v(t._s(t.$t("aDrawer.mattersNeedingAttention")))]),a("div",{staticClass:"content"},[a("div",{staticClass:"explain-item"},[a("b",[t._v("1、")]),a("span",{staticClass:"care"},[t._v(t._s(t.$t("aDrawer.clearOperation")))]),t._v(t._s(t.$t("aDrawer.selectedDefaultColor"))+" ")]),a("div",{staticClass:"explain-item"},[a("b",[t._v("2、")]),a("span",{staticClass:"care"},[t._v(t._s(t.$t("aDrawer.transparency")))]),t._v(t._s(t.$t("aDrawer.transparencyDefaultSelecting"))+" ")]),a("div",{staticClass:"explain-item"},[a("b",[t._v("3、")]),a("span",{staticClass:"care"},[t._v(t._s(t.$t("aDrawer.quickColors")))]),t._v(t._s(t.$t("aDrawer.selectorOperation"))+" ")])])])])},yt=[],xt={props:{isVisibleaDrawer:{type:Boolean,default:!1},changeSelectedColorVal:{type:Object,default:function(){}}},watch:{isVisibleaDrawer:function(t){this.visible=t,t&&(this.initFillColor(),this.initBorderColor(),this.tagName=this.changeSelectedColorVal.descibe)}},computed:{fillColor:function(){return this.$store.state.labels.fillColor},borderColor:function(){return this.$store.state.labels.borderColor}},data:function(){return{visible:this.isVisibleaDrawer,colorPicker:null,borderColorPicker:null,tagName:""}},methods:{initFillColor:function(){var t=this;t.$nextTick((function(){t.colorPicker=new ut({el:"#colorPicker",size:{width:200,height:32},isLog:!1,alpha:!0,defaultColor:t.fillColor,predefineColor:["#fff","#2396ef"],openPickerAni:"opacity",sure:t.handler,clear:t.clearColor})}))},initBorderColor:function(){var t=this;t.$nextTick((function(){t.borderColorPicker=new ut({el:"#colorBorder",size:{width:200,height:32},isLog:!1,alpha:!1,defaultColor:t.borderColor,predefineColor:["#fff","#2396ef"],openPickerAni:"opacity",sure:t.sureBorder,clear:t.clearColorBorder})}))},sureBorder:function(t){this.$store.commit(pt["b"],t),this.changeSelectedColorVal.id&&(this.changeSelectedColorVal.stroke=t,this.$emit("chanageSelectedColor",this.changeSelectedColorVal))},clearColorBorder:function(t){this.$store.state.labels.borderColor="",this.$store.commit(pt["b"],"#ff0000")},handler:function(t){this.$store.commit(pt["e"],t),this.changeSelectedColorVal.id&&(this.changeSelectedColorVal.fill=t,this.$emit("chanageSelectedColor",this.changeSelectedColorVal))},clearColor:function(t){this.$store.state.labels.fillColor="",this.$store.commit(pt["e"],"rgba(255, 0, 0, 0)")},afterVisibleChange:function(t){},onClose:function(){this.$emit("update:isVisibleaDrawer",!1)},changeFn:function(t){this.changeSelectedColorVal.descibe=this.tagName,this.$emit("changeTagName",this.changeSelectedColorVal)}},components:{}},_t=xt,Ct=(a("4716"),Object(x["a"])(_t,wt,yt,!1,null,"1d1bf8f4",null)),kt=Ct.exports;function It(t,e,a){var i=a.points[this.pointIndex].x-a.pathOffset.x,s=a.points[this.pointIndex].y-a.pathOffset.y;return fabric.util.transformPoint({x:i,y:s},fabric.util.multiplyTransformMatrices(a.canvas.viewportTransform,a.calcTransformMatrix()))}function Tt(t,e,a,i){var s=e.target,n=s.controls[s.__corner],o=s.toLocalPoint(new fabric.Point(a,i),"center","center"),r=s._getNonTransformedDimensions(),l=s._getTransformedDimensions(0,0),c={x:o.x*r.x/l.x+s.pathOffset.x,y:o.y*r.y/l.y+s.pathOffset.y};return s.points[n.pointIndex]=c,!0}function St(t,e){return function(a,i,s,n){var o=i.target,r=fabric.util.transformPoint({x:o.points[t].x-o.pathOffset.x,y:o.points[t].y-o.pathOffset.y},o.calcTransformMatrix()),l=e(a,i,s,n),c=(o._setPositionDimensions({}),o._getNonTransformedDimensions()),d=(o.points[t].x-o.pathOffset.x)/c.x,h=(o.points[t].y-o.pathOffset.y)/c.y;return o.setPositionByOrigin(r,d+.5,h+.5),l}}var Lt,At,Ot,Dt=a("2875"),Pt=a.n(Dt),jt=a("5d3d"),Et=a.n(jt),Mt={x:0,y:0},Bt=(new fabric.Image.filters.Resize({scaleX:1,scaleY:1,resizeType:"lanczos",lanczosLobes:3}),{props:{queryProId:{type:String,default:""}},data:function(){return{width:1248,height:689,outWidth:1250,drawType:null,moveCount:1,doDrawing:!1,mouseTo:{},mouseFrom:{},drawWidth:1,drawingObject:null,isVisibleDialog:!1,jsonData:{},isVisiblea:!1,paddingLft:"200px",marTop:"100px",canvasBg:"",drawCanDia:!0,isEdit:!0,curentDataSetId:"",currentImgId:"",currentAction:"项目操作",inspectInfor:"",inspectTimes:0,inspectTagId:"",polygonMode:!1,pointArray:[],lineArray:[],activeShape:!1,activeLine:"",line:{},scaleNum:100,isVisibleaDrawer:!1,moveCanvasInfo:{isDragging:!1,lastX:0,lastY:0},zoom:window.zoom?window.zoom:1,beginzoomPoint:new fabric.Point(0,0),zoomPoint:new fabric.Point(0,0),lastzoomPoint:{x:0,y:0},lastmousePoint:{x:0,y:0},lastzoom:1,relativeMouseX:0,relativeMouseY:0,copycanvas:{},copycontext:null,square:null,squaredata:{},canvasSource:null,boxSource:null,changeSelectedColorVal:{},bgImgX:0,bgImgY:0,bgImgLeftTopX:0,bgImgLeftBottomY:0,bgWidth:0,bgHeight:0,imgFile:{},imgSrc:"",dataList:[],isSelectDrawType:!0,isImage:!1,isDrawEdit:!1,isCanCtrlFlag:!1,CoefficientPosi:0,preEdit:!1,SelectFileList:[],SelectFile:{},preDrawType:"",isMoveScal:!1}},computed:{imgList:function(){return this.$store.state.labels.selectImgList},currentProId:function(){return this.$store.state.labels.currentProId},fromCenterVal:function(){return this.$store.state.labels.fromCenterVal},currentSelectedImg:function(){return this.$store.state.labels.currentSelectedImg},fillColor:function(){return this.$store.state.labels.fillColor},color:function(){return this.$store.state.labels.borderColor}},watch:{drawType:function(){this.canvas.selection=!this.drawType},width:function(t){this.canvas.setWidth(t)},height:function(){this.canvas.setHeight(this.height)},currentSelectedImg:function(t){this.bgChangeFn(t)}},mounted:function(){var t=this;this.init(),this.keyboardFn(),this.widthChange(),window.onresize=function(){t.widthChange()},this.width=document.getElementById("top").clientWidth-2,this.height=document.getElementById("top").clientHeight-2},methods:{uploadImg:function(t){this.$emit("handleSwitch",l({},t)),this.clearBgAll(),document.getElementById("imgInput").click()},handleEchoImg:function(t){this.SelectFile={},"file"===t.selectType?(t.selectTabs="2",t.selectItem="",document.getElementById("imgDataInputFile").click()):document.getElementById("imgDataInputFolder").click(),this.$emit("handleSwitch",l({},t)),this.clearBgAll()},getImgDataFile:function(t){var e=this,a=this;a.isImage=!1;var i=document.getElementById("imgDataInputFile"),s=new Image,n=t.target.files[0];if(n){var o=n,r=o.name;a.imgTitle=r;var l=new FileReader;l.readAsText(o),l.onload=function(){var t=JSON.parse(this.result);t.imgUrl||this.$message.warning($t("draw.selectJsonFile")),s.src=t.imgUrl,a.canvasBg=t.imgUrl,a.clearAllDraw(),a.clearBgAll(),t.desiList?(a.dataList=p(t.desiList),a.dataList.forEach((function(t){"polygon"===t.type?a.showPolygon(t):a.showRectangle(t)})),i.value=""):a.$message.error($t("draw.dataFormatIncorrect"))},s.onload=function(){new fabric.Image(s,{zIndex:-1});a.isImage=!0,a.setCanvasBg(e.canvasBg),a.isSelectDrawType=!1,a.$emit("allDesiListFn",a.dataList)}}else this.$message.warning($t("draw.selectAgain"))},getImgDataFolder:function(t){var e=this,a=this,i=[];a.isImage=!1;var s=l({},t.target.files),n=[];for(var o in s)s[o].name.includes(".json")&&(i.push(s[o]),n.push(s[o].webkitRelativePath));localStorage.setItem("LocalStragePaths",JSON.stringify(n)),this.$emit("handleGetFile",{list:i,type:"file"});var r=l({title:"",type:""},JSON.parse(localStorage.getItem("selcetFileIndex"))),c=r.title,d=r.type;i.forEach((function(t,a){return t.name===c?i.length>=a&&"file"===d?(e.loadEchoImage(i[a]),!1):(e.loadEchoImage(i[0]),!1):a===i.length-1&&t.name!==c&&"file"!==d?(e.loadEchoImage(i[0]),!1):void 0}))},loadEchoImage:function(t){var e=this;if(t){var a=this,i=document.getElementById("imgDataInputFolder"),s=new Image,n=t,o=n.name;a.imgTitle=o;var r=new FileReader;r.readAsText(n),r.onload=function(){var t=JSON.parse(this.result);t.imgUrl||this.$message.warning($t("draw.selectJsonFile")),s.src=t.imgUrl,a.canvasBg=t.imgUrl,a.clearAllDraw(),a.clearBgAll(),t.desiList?(a.dataList=p(t.desiList),a.dataList.forEach((function(t){"polygon"===t.type?a.showPolygon(t):a.showRectangle(t)})),i.value=""):a.$message.error($t("draw.dataFormatIncorrect"))},s.onload=function(){new fabric.Image(s,{zIndex:-1});a.isImage=!0,a.setCanvasBg(e.canvasBg),a.isSelectDrawType=!1,a.$emit("allDesiListFn",a.dataList)}}else this.$message.warning($t("draw.selectAgain"))},getFileImage:function(t){var e=this,a=this,i=[];a.isImage=!1;var s=t.target.files;for(var n in this.SelectFileList=[],s)/\.(jpe?g|png|bmp)$/i.test(s[n].name)&&(i.push(s[n]),this.SelectFileList.push(s[n]));this.$emit("handleGetFileImage",{list:i,type:"fileImage"});var o=l({title:"",type:""},JSON.parse(localStorage.getItem("selcetFileIndex"))),r=o.title,c=o.type;i.forEach((function(t,a){return t.name===r?i.length>=a&&"fileImage"===c?(e.loadUploadImage(i[a]),!1):(e.loadUploadImage(i[0]),!1):a===i.length-1&&t.name!==r&&"fileImage"!==c?(e.loadUploadImage(i[0]),!1):void 0}))},loadUploadImage:function(t){var e=this;if(t){var a=this,i=document.getElementById("imgInput");a.SelectFile=t;var s=new Image;if(/\.(jpe?g|png|bmp)$/i.test(t.name)){var n=new FileReader;a.clearAllDraw(),a.clearBgAll(),n.onload=function(){a.imgTitle=t.name,s.src=this.result,a.canvasBg=this.result},n.readAsDataURL(t),i.value="",s.onload=function(){new fabric.Image(s,{zIndex:-1});e.setCanvasBg(e.canvasBg),e.isSelectDrawType=!1,e.isImage=!0}}}else this.$message.warning($t("draw.unmarkedSelectedFolder"))},handleUpImage:function(){var t=this,e=[],a=p(this.SelectFileList);a.map((function(t){/\.(jpe?g|png|bmp)$/i.test(t.name)&&e.push(t)})),this.$emit("handleGetFileImage",{list:e,type:"fileImage"});var i=l({title:"",type:""},JSON.parse(localStorage.getItem("selcetFileIndex"))),s=i.title,n=i.type;e.forEach((function(a,i){return a.name===s?e.length>=i&&"fileImage"===n?(t.loadUploadImage(e[i]),!1):(t.loadUploadImage(e[0]),!1):i===e.length-1&&a.name!==s&&"fileImage"!==n?(t.loadUploadImage(e[0]),!1):void 0}))},initClickBig:function(){At=document.getElementById("copycanvas"),Ot=At.getContext("2d"),Ot.imageSmoothingEnabled=!1},initCopyCanvas:function(){var t=this;this.canvasSource=document.getElementById("canvas"),this.copycanvas=document.getElementById("copycanvas"),this.copycontext=this.copycanvas.getContext("2d"),this.square=document.getElementById("square"),this.boxSource=this.canvasSource.getBoundingClientRect(),window.onmousemove=function(e){var a=e.clientX,i=e.clientY;a>=t.canvasSource.offsetLeft&&a<=t.canvasSource.offsetLeft+t.canvasSource.width&&i>=t.canvasSource.offsetTop&&i<=t.canvasSource.offsetTop+t.canvasSource.height?t.createSquare(a,i):(t.hideSquare(),t.hideCanvas())}},createSquare:function(t,e){var a=t-45a&&(s=a-o),n+r>i&&(n=i-r),Ot.drawImage(Lt._originalElement,s/t,n/e,550,400,0,0,550*t,400*e),Ot.drawImage(At,0,0,o,r,0,0,550,400)},chooseTools:function(t){this.isVisibleaDrawer=!0,this.changeSelectedColorVal={},t.id&&(this.changeSelectedColorVal=t),this.isVisibleaDrawer=!0,this.changeSelectedColorVal={},t.id&&(this.changeSelectedColorVal=t)},canvasChangeAdd:function(t){this.scaleNum=2,this.canvas.setZoom(2)},canvasChangeReduce:function(t){this.scaleNum=1,this.canvas.setZoom(1)},init:function(){var t=this;this.canvas=new fabric.Canvas("canvas",{}),this.canvas.selectionColor="rgba(0,0,0,0.05)",this.canvas.defaultCursor="crosshair",this.canvas.hoverCursor="pointer",this.canvas.preserveObjectStacking=!0,this.canvas.on("mouse:down",this.mousedown),this.canvas.on("mouse:move",this.mousemove),this.canvas.on("mouse:up",this.mouseup),this.canvas.on("object:modified",this.modified),this.canvas.on("object:selected",this.objectSelected),this.canvas.on("object:scaling",this.objectScaleing),this.canvas.on("mouse:wheel",(function(e){e.e.preventDefault(),e.e.altKey&&!t.isSelectDrawType?(document.querySelector("body").setAttribute("style","overflow: hidden"),t.zoom=(e.e.deltaY>0?-.1:.1)+t.canvas.getZoom(),t.zoom=Math.max(.1,t.zoom),t.zoom=Math.min(3,t.zoom),t.zoomPoint=new fabric.Point(e.pointer.x,e.pointer.y),t.canvas.zoomToPoint(t.zoomPoint,t.zoom),t.resetOriginAfterZoom(),t.setCanvasBg(t.canvasBg)):document.querySelector("body").setAttribute("style","overflow: auto")}))},initialZoom:function(){this.zoom=.8,this.zoomPoint=new fabric.Point(this.canvas.width/2,this.canvas.height/2),this.canvas.zoomToPoint(this.zoomPoint,this.zoom),this.resetOriginAfterZoom()},resetOriginAfterZoom:function(){this.lastzoomPoint.x=this.lastzoomPoint.x+(this.zoomPoint.x-this.lastmousePoint.x-this.relativeMouseX)/this.lastzoom,this.lastzoomPoint.y=this.lastzoomPoint.y+(this.zoomPoint.y-this.lastmousePoint.y-this.relativeMouseY)/this.lastzoom,this.lastmousePoint.x=this.zoomPoint.x,this.lastmousePoint.y=this.zoomPoint.y,this.lastzoom=this.zoom,this.tempmouseX=this.relativeMouseX,this.tempmouseY=this.relativeMouseY,this.relativeMouseX=0,this.relativeMouseY=0},resizeCanvas:function(){this.canvas.width=.72*document.getElementById("draw").offsetWidth,this.canvas.height=.72*document.getElementById("draw").offsetHeight,this.canvas.setWidth(this.canvas.width),this.canvas.setHeight(this.canvas.height),this.initialZoom()},zoomToFitCanvas:function(){this.canvas.setZoom(1),this.canvas.absolutePan({x:0,y:0});var t=this.canvas.getObjects();if(t.length>0)for(var e=t[0].getBoundingRect(),a=e.left,i=e.top,s=e.left+e.width,n=e.top+e.height,o=1;o1&&t.target&&t.target.id===this.pointArray[0].id&&this.generatePolygon(),this.polygonMode&&this.addPoint(t)}catch(a){}}else this.drawCanDia=!1},mousemove:function(t){if(!(this.moveCount%2)||this.doDrawing){if(this.moveCount++,"polygon"!==this.drawType&&this.drawing(t),"polygon"===this.drawType){if(this.activeLine&&"line"===this.activeLine.class){var e=this.canvas.getPointer(t.e);this.activeLine.set({x2:e.x,y2:e.y});var a=this.activeShape.get("points");a[this.pointArray.length]={x:e.x,y:e.y,zIndex:1},this.activeShape.set({points:a}),this.canvas.renderAll()}this.canvas.renderAll()}if(this.moveCanvasInfo.isDragging){var i=new fabric.Point(t.e.movementX,t.e.movementY);return this.canvas.relativePan(i),this.relativeMouseX+=t.e.movementX,void(this.relativeMouseY+=t.e.movementY)}this.mouseTo.x=this.getX(t),this.mouseTo.y=this.getY(t)}},mouseup:function(t){"rectangle"===this.preDrawType&&this.isMoveScal?(this.drawType=this.preDrawType,this.isMoveScal=!1):"polygon"===this.preDrawType&&this.isMoveScal&&(this.isMoveScal=!1,this.drawType=this.preDrawType),this.mouseFrom.x=this.getX(t),this.mouseFrom.y=this.getY(t),this.drawingObject=null,this.moveCount=1,"polygon"!==this.drawType&&(this.doDrawing=!1),this.drawCanDia&&this.drawEnd(),this.moveCanvasInfo.isDragging=!1,this.moveCanvasInfo.selection=!0},drawEnd:function(){this.isVisiblea=!0,this.isEdit=!1,this.submitDraw("名称")},modified:function(t){this.$emit("listenModified",t.target)},objectScaleing:function(t){var e=t.target;!e.strokeWidthUnscaled&&e.strokeWidth&&(e.strokeWidthUnscaled=e.strokeWidth),e.strokeWidthUnscaled&&(e.strokeWidth=e.strokeWidthUnscaled/e.scaleX)},objectMoving:function(t){var e=t.pointer.x-this.bgImgX>0||t.pointer.y-this.bgImgY>0;e&&(this.$message.warning($t("draw.exceedingMarked")),this.canvas.skipTargetFind=!0,this.canvas.remove(t.target),this.$emit("deleteItem",t.target),this.canvas.renderAll())},objectSelected:function(t){},drawing:function(t){this.drawingObject&&this.canvas.remove(this.drawingObject);var e=null,a=this.mouseFrom.x,i=this.mouseFrom.y,s=this.mouseFrom,n=this.mouseTo;switch(this.drawType){case"rectangle":t.e.shiftKey&&(n.x-a>n.y-i?n.y=i+n.x-a:n.x=a+n.y-i);var o="M "+s.x+" "+s.y+" L "+n.x+" "+s.y+" L "+n.x+" "+n.y+" L "+s.x+" "+n.y+" L "+s.x+" "+s.y+" z";e=new fabric.Path(o,{left:a,top:i,stroke:this.color,strokeWidth:this.drawWidth,fill:this.fillColor,hasControls:!0,id:this.currentKey(),scaleX:1,scaleY:1,opacity:.4,objectCaching:!1,selectable:!1,type:"rectangular"});break;default:break}e&&(this.canvas.add(e),this.drawingObject=e,this.drawCanDia=!0)},drawPolygon:function(){this.canvasBg?(this.isDrawEdit&&(this.isDrawPolygon(),this.isDrawEdit=!1),this.drawType="polygon",this.preDrawType=this.drawType,this.polygonMode=!0,this.pointArray=new Array,this.lineArray=new Array,this.canvas.isDrawingMode=!1):(this.$message.warning($t("draw.selelctPictureClicking")),this.drawCanDia=!1)},drawEdit:function(){this.drawType=null,this.doDrawing=!1,this.polygonMode=!1,this.isDrawPolygon()},isDrawPolygon:function(t){var e=this;this.isDrawEdit=!this.isDrawEdit,this.canvas.getObjects().map((function(a){"polygon"!==a.type&&"rectangular"!==a.type||(a.set({selectable:!t&&e.isDrawEdit}),e.canvas.renderAll())})),this.canvas.skipTargetFind=!t&&!this.isDrawEdit},isCanCtrlPolygon:function(){var t=this;this.canvas.getObjects().map((function(e){"polygon"===e.type&&(e.set({hasControls:!t.isCanCtrlFlag}),t.canvas.renderAll())}));var e=this.canvas.getActiveObject();e.hasControls=!0,"polygon"===e.type&&this.changeDrawPolygon(e)},changeDrawPolygon:function(t){var e=this.canvas,a=e.getObjects().length,i=t||e.getObjects()[a-1];if(e.setActiveObject(i),i.edit=!i.edit,i.edit){var s=i.points.length-1;i.cornerStyle="circle",i.cornerColor="rgba(0,0,255,0.5)",i.controls=i.points.reduce((function(t,e,a){return t["p"+a]=new fabric.Control({positionHandler:It,actionHandler:St(a>0?a-1:s,Tt),actionName:"modifyPolygon",pointIndex:a}),t}),{})}else i.cornerStyle="rect",i.controls=fabric.Object.prototype.controls;i.hasBorders=!i.edit,e.requestRenderAll()},generatePolygon:function(){var t=this,e=new Array;this.pointArray.map((function(a,i){e.push({x:a.left,y:a.top}),t.canvas.remove(a)})),this.lineArray.map((function(e,a){t.canvas.remove(e)})),this.canvas.remove(this.activeShape).remove(this.activeLine);var a=new fabric.Polygon(e,{stroke:this.color,strokeWidth:this.drawWidth,fill:this.fillColor,cornerColor:"blue",objectCaching:!1,hasControls:!0,id:this.currentKey(),type:"polygon",selectable:!1});this.canvas.add(a),this.activeLine=null,this.activeShape=null,this.polygonMode=!1,this.doDrawing=!1,this.drawCanDia=!0},canEditPolygon:function(t){var e=new fabric.Polygon(t,{fill:this.fillColor,strokeWidth:this.drawWidth,stroke:this.color,objectCaching:!1,transparentCorners:!1,cornerColor:"blue",id:this.currentKey()});return e},currentKey:function(){var t=Math.floor(1e4*Math.random());return(new Date).getTime()+t},addPoint:function(t){var e=Math.floor(1e4*Math.random()),a=(new Date).getTime()+e,i=this.mouseFrom.x,s=this.mouseFrom.y,n=new fabric.Circle({radius:6,fill:"#ffffff",stroke:"#333333",strokeWidth:.5,left:i,top:s,selectable:!1,hasBorders:!1,hasControls:!1,originX:"center",originY:"center",id:a,objectCaching:!1});0===this.pointArray.length&&(n.set({fill:"red"}),this.canvas.skipTargetFind=!1);var o=[i,s,i,s];if(this.line=new fabric.Line(o,{strokeWidth:2,fill:"#999999",stroke:"#999999",class:"line",originX:"center",originY:"center",selectable:!1,hasBorders:!1,hasControls:!1,evented:!1,objectCaching:!1}),this.activeShape){var r=this.canvas.getPointer(t.e);o=this.activeShape.get("points");o.push({x:r.x,y:r.y});var l=new fabric.Polygon(o,{stroke:"#333333",strokeWidth:1,fill:"#cccccc",opacity:.4,selectable:!1,hasBorders:!1,hasControls:!1,evented:!1,objectCaching:!1});this.canvas.remove(this.activeShape),this.canvas.add(l),this.activeShape=l,this.canvas.renderAll()}else{var c=[{x:i,y:s}];l=new 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MessageService.showWarning("选定数据存在重复")},objectFun:function(e){var a=!1,i=!1;if(t.dataList[e.index].markInfo&&t.dataList[e.index].markInfo.forEach((function(t){var s=t.t1_start,n=t.t1_end,o=t.t2_start,r=t.t2_end,l=t.aspect_term_start,c=t.aspect_term_end;e.text.anchorOffse>s&&e.text.anchorOffses&&e.text.focusOffseto&&e.text.anchorOffseo&&e.text.focusOffsetl&&e.text.anchorOffsel&&e.text.focusOffset').concat(t.dataList[e.index].corpus1[m],""):(u[m]='').concat(t.dataList[e.index].corpus1[m]),u[g-1]="".concat(t.dataList[e.index].corpus1[g-1],""));var v=t.dataList[e.index].markInfo[p].t2_start,b=t.dataList[e.index].markInfo[p].t2_end;v===b-1?u[v]='').concat(t.dataList[e.index].corpus1[v],""):(u[v]='').concat(t.dataList[e.index].corpus1[v]),u[b-1]="".concat(t.dataList[e.index].corpus1[b-1],""));var 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v=t.dataList[e.index].markInfo[p].t2_start,b=t.dataList[e.index].markInfo[p].t2_end;v===b-1?u[v]='').concat(t.dataList[e.index].corpus1[v],""):(u[v]='').concat(t.dataList[e.index].corpus1[v]),u[b-1]="".concat(t.dataList[e.index].corpus1[b-1],""));var w=t.dataList[e.index].markInfo[p].aspect_term_start,y=t.dataList[e.index].markInfo[p].aspect_term_end;w===y-1?u[w]='').concat(t.dataList[e.index].corpus1[w],""):(u[w]='').concat(t.dataList[e.index].corpus1[w]),u[y-1]="".concat(t.dataList[e.index].corpus1[y-1],"")),t.dataList[e.index].markInfo[p].index=p}t.dataList[e.index].corpus=u.join("")}else MessageService.showWarning("选定数据存在重复")}}}}}},d=c,h=(a("0a1d"),a("2877")),f=Object(h["a"])(d,i,s,!1,null,"0b3d3f98",null);e["default"]=f.exports},a3b5:function(t,e,a){},b2b7:function(t,e,a){"use strict";a.r(e),a.d(e,"isElementNode",(function(){return s})),a.d(e,"svgComponent",(function(){return o}));var i=Object.assign||function(t){for(var e,a=1,i=arguments.length;a63#;^s7T^zu7k%=XXez_)_y(D`+k%1u4mu-z3-mG@A*B?`)rDGj6NwT3cXJ% zhU1EILQ#}cv`9r7si#d*$aYjf@|#~m691f&THe{hela@nBShU32^sf&Ed+0^2`#2< zxF27~n>rKv9Oak^bYgdX8L?=T!eme~XL4WqH~^bI1D5=BtjvyrF}GKP@KoP~)p!Oy zmQs8Rjw7z-O5!k&gY9)R>a`*^vb{+h$*THotuCU!1Zp?!Q4QH2$eX@C4o^_ zJ(?(Qcg=08k#tN-n&=>9XZ;f##plteJB<(SdTD1TdR)Yj^LIZrVvo2;h`#~knPFRt z&IvA|5x-1B$`_?qVbP_+Q*{H2BR-*5K@dII2lq9hHp+Sb68UQ2uDB++GT-)xmtoau z(OG>9KJz`qentdowng`U;PXU3{N}rG6rM%XoecEa%McptQEPz6%YA=M`P&{`#E`2_ z7EQEe+62B%55iWMg=dCrtSw9&#UEDR7AcgBGJ<`P)- zslvXuyP;wOp~+q}boTnP$H<}QRsg!{!itzYpt!sHVg?}M|L`ayMhD)X=~=#^ZAzj{B( c;h<1~N47&;hE+kOP6i6jWI6hO@W0>x03 1] - all_regex_urls = list(map(lambda x: 'http:' + x, all_regex_urls_temp)) + list(map(lambda x: 'https:' + x, all_regex_urls_temp)) + list(map(lambda x: 'ftp:' + x, all_regex_urls_temp)) + \ - list(map(lambda x: 'HTTP:' + x, all_regex_urls_temp)) + list(map(lambda x: 'HTTPS:' + x, all_regex_urls_temp)) + list(map(lambda x: 'FTP:' + x, all_regex_urls_temp)) + all_regex_urls = list(map(lambda x: 'http:' + x, all_regex_urls_temp)) + list( + map(lambda x: 'https:' + x, all_regex_urls_temp)) + list(map(lambda x: 'ftp:' + x, all_regex_urls_temp)) + \ + list(map(lambda x: 'HTTP:' + x, all_regex_urls_temp)) + list( + map(lambda x: 'HTTPS:' + x, all_regex_urls_temp)) + list(map(lambda x: 'FTP:' + x, all_regex_urls_temp)) patterns_regex = '|'.join(all_regex_urls).replace(',', '').replace(':', '\:').replace('?', '\?').replace( '=', '\=').replace('&', '\&').replace('.', '\.').replace('#', '\#').replace('/', '\/') cleaned_article = re.sub( @@ -177,12 +179,19 @@ def remove_link(self, article): if 'http' in cleaned_article: urls_further = re.findall( self.url_regex_set.URL_REGEX_FURTHER, cleaned_article) - all_regex_urls_temp_further = [y for y in list(set(sum([re.split(r'http:|https:|ftp:|HTTP:|HTTPS:|FTP:', ''.join( - x for x in url if x in string.printable)) for url in urls_further], []))) if len(y) > 1] - all_regex_urls_further = list(map(lambda x: 'http:' + x, all_regex_urls_temp_further)) + list(map(lambda x: 'https:' + x, all_regex_urls_temp_further)) + list(map(lambda x: 'ftp:' + x, all_regex_urls_temp_further)) + list( - map(lambda x: 'HTTP:' + x, all_regex_urls_temp_further)) + list(map(lambda x: 'HTTPS:' + x, all_regex_urls_temp_further)) + list(map(lambda x: 'FTP:' + x, all_regex_urls_temp_further)) + all_regex_urls_temp_further = [y for y in + list(set(sum([re.split(r'http:|https:|ftp:|HTTP:|HTTPS:|FTP:', ''.join( + x for x in url if x in string.printable)) for url in urls_further], []))) + if len(y) > 1] + all_regex_urls_further = list(map(lambda x: 'http:' + x, all_regex_urls_temp_further)) + list( + map(lambda x: 'https:' + x, all_regex_urls_temp_further)) + list( + map(lambda x: 'ftp:' + x, all_regex_urls_temp_further)) + list( + map(lambda x: 'HTTP:' + x, all_regex_urls_temp_further)) + list( + map(lambda x: 'HTTPS:' + x, all_regex_urls_temp_further)) + list( + map(lambda x: 'FTP:' + x, all_regex_urls_temp_further)) patterns_regex_further = '|'.join(all_regex_urls_further).replace(',', '').replace(':', '\:').replace( - '?', '\?').replace('=', '\=').replace('&', '\&').replace('.', '\.').replace('#', '\#').replace('/', '\/') + '?', '\?').replace('=', '\=').replace('&', '\&').replace('.', '\.').replace('#', '\#').replace('/', + '\/') cleaned_article = re.sub( patterns_regex_further, '', cleaned_article, 1000, flags=re.I).strip() return cleaned_article @@ -245,7 +254,7 @@ def _contain_double_mark(self, article): double-mark validation ''' new_article = [x for x in self.rules.ALL_LANGUAGE_WHOLE_SENTENCE_MARK if ( - (x[0] in article) and (x[1] in article))] + (x[0] in article) and (x[1] in article))] if len(new_article) >= 1: return article else: @@ -265,3 +274,19 @@ def clean_article(self, article): if not article: break return article.strip() + + +class ReprintDeclarationCleaning(BasicCleaner): + def __init__(self): + self.patterns = [ + re.compile(r'版权声明：.*?原文链接：.*?$', re.DOTALL | re.MULTILINE), + re.compile(r'作者：.*?链接：.*?来源：知乎.*?著作权归作者所有。.*?$', re.DOTALL | re.MULTILINE), + re.compile(r'@著作权归作者所有，转载或内容合作请联系作者.*?禁止转载，如需转载请通过简信或评论联系作者。', + re.DOTALL), + re.compile(r'本文来自博客园，作者：.*?，转载请注明原文链接：.*?$', re.MULTILINE) + ] + + def clean_article(self, article): + for pattern in self.patterns: + article = pattern.sub('', article) + return article diff --git a/flagdata/condensation/data_distillation.py b/flagdata/condensation/data_distillation.py deleted file mode 100644 index 149c298..0000000 --- a/flagdata/condensation/data_distillation.py +++ /dev/null @@ -1,76 +0,0 @@ -# Copyright © 2023 BAAI. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License") -import os -import torch -from loguru import logger -from transformers import DistilBertTokenizer -from torch.utils.data import DataLoader -from .utils.model import DistilBertModel, BertClassifier -from .utils.data import SeqCLSDataset -from .utils.datadistill import DistilledData -from .utils.common_utils import read_config, set_random_seed -from datetime import datetime -import tempfile - - -class DataDistillationTrainer(object): - def __init__(self, config_path): - self.config = read_config(config_path) - self.model_cache_dir = tempfile.TemporaryDirectory() - timestamp = datetime.now().strftime("%d-%m-%Y-%H-%M-%S") - logger.add(f'log/log_{timestamp}.log') - self.data_loader = {} - set_random_seed(self.config["basic"]["seed"]) - self.model, self.tokenizer, self.init_model_path = self.setup_model_and_tokenizer() - self.distilled_load_path = self.config["distill"]["distilled_load_path"] - self.distilled_save_path = self.config["distill"]["distilled_save_path"] - self.best_loss = float("inf") - - def setup_model_and_tokenizer(self): - distilbert_model = DistilBertModel.from_pretrained( - self.config["model"]["model_name"]) - tokenizer = DistilBertTokenizer.from_pretrained( - self.config["model"]["model_name"]) - model = BertClassifier(bert_model=distilbert_model, - num_classes=self.config["data"]["num_classes"], - drop_p=self.config["basic"]["drop_p"]).to(self.config["basic"]["device"]) - init_model_path = os.path.join( - self.model_cache_dir.name, "model_init_states.pt") - torch.save(model.state_dict(), init_model_path) - return model, tokenizer, init_model_path - - def load_data(self, dataset_class=SeqCLSDataset): - for type in ['train', 'test']: - data_path = self.config["data"][f"{type}_data_path"] - dataset = dataset_class( - data_path, self.tokenizer, self.config["data"]["max_seq_len"]) - batch_size = self.config["data"][f"{type}_batch_size"] - if type == "train": - shuffle = True - else: - shuffle = False - self.data_loader[type] = DataLoader( - dataset, batch_size=batch_size, shuffle=shuffle, num_workers=4) - - def fit(self): - distilled_data = DistilledData( - config=self.config, - model=self.model, - train_loader=self.data_loader['train'], - initial_model_path=self.init_model_path - ) - if self.distilled_load_path is not None and os.path.exists(self.distilled_load_path): - distilled_data.load_distilled_data(self.distilled_load_path) - for epoch in range(self.config["distill"]["n_distill_epochs"]): - logger.info(f"Epoch[{epoch+1}]: Train distilled data " + "-" * 70) - distilled_data.train_distilled_data(epoch) - logger.info(f"Epoch[{epoch+1}]: Test distilled data" + "-" * 70) - test_loss, test_acc = distilled_data.test_distilled_data( - self.data_loader['test']) - if test_loss < self.best_loss: - self.best_loss = test_loss - distilled_data.save_distilled_data(self.distilled_save_path) - - def __del__(self): - self.model_cache_dir.cleanup() diff --git a/flagdata/condensation/utils/common_utils.py b/flagdata/condensation/utils/common_utils.py deleted file mode 100644 index 8c5cd68..0000000 --- a/flagdata/condensation/utils/common_utils.py +++ /dev/null @@ -1,34 +0,0 @@ -# Copyright © 2023 BAAI. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License") -import random -import torch -import numpy as np -import pathlib -import yaml - - -def make_dir(d): - pathlib.Path(d).mkdir(parents=True, exist_ok=True) - - -def set_random_seed(seed): - random.seed(seed) - np.random.seed(seed) - torch.manual_seed(seed) - torch.cuda.manual_seed(seed) - torch.cuda.manual_seed_all(seed) - torch.backends.cudnn.deterministic = True - torch.cuda.empty_cache() - - -def read_config(config_path): - with open(config_path, "r", encoding="utf8") as fr: - config = yaml.safe_load(fr) - device_id = config["basic"]["cuda_device_id"] - if device_id != -1: - config["basic"]["device"] = torch.device(f"cuda:{device_id}") - else: - config["basic"]["device"] = torch.device("cpu") - config["basic"]["dtype"] = torch.bfloat16 if config["basic"]["fp16"] else torch.float16 - return config diff --git a/flagdata/condensation/utils/data.py b/flagdata/condensation/utils/data.py deleted file mode 100644 index b172e2b..0000000 --- a/flagdata/condensation/utils/data.py +++ /dev/null @@ -1,45 +0,0 @@ -# Copyright © 2023 BAAI. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License") -import json -import torch -from torch.utils.data import Dataset -import pandas as pd - - -class SeqCLSDataset(Dataset): - def __init__(self, data_path, tokenizer, max_length): - self.tokenizer = tokenizer - self.data = self.read_data(data_path) - self.sentence1 = self.data['text'].values - self.targets = self.data['label'].values - self.max_length = max_length - - def read_data(self, path): - data, label = [], [] - with open(path, "r", encoding="utf8") as f: - for line in f.readlines(): - line = json.loads(line) - data.append(line['text']) - label.append(line['label'] - 1) - return pd.DataFrame(data={'text': data, 'label': label}) - - def __len__(self): - return len(self.sentence1) - - def __getitem__(self, index): - sent1 = str(self.sentence1[index]) - - inputs = self.tokenizer.encode_plus( - sent1, - add_special_tokens=True, - max_length=self.max_length, - padding="max_length", - return_token_type_ids=True, - return_tensors="pt", truncation=True - ) - - input_ids = inputs["input_ids"] - attention_mask = inputs["attention_mask"] - - return input_ids.squeeze(), attention_mask.squeeze(), torch.tensor(self.targets[index]) diff --git a/flagdata/condensation/utils/datadistill.py b/flagdata/condensation/utils/datadistill.py deleted file mode 100644 index 3173c36..0000000 --- a/flagdata/condensation/utils/datadistill.py +++ /dev/null @@ -1,245 +0,0 @@ -# Copyright © 2023 BAAI. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License") -import torch -import time -from loguru import logger -from .evaluate import evaluate -from tqdm import tqdm -from torch.optim import SGD, Adam -from transformers import get_linear_schedule_with_warmup -from torch.cuda import amp -from collections import OrderedDict - - -class DistilledData: - def __init__(self, config, model, train_loader, initial_model_path): - self.num_classes = config["data"]["num_classes"] - self.data_size = config["distill"]["data_size"] - self.device = config["basic"]["device"] - self.use_amp = config["basic"]["use_amp"] - self.dtype = config["basic"]["dtype"] - self.random_init = config["distill"]["random_init"] - self.n_inner_steps = config["distill"]["n_inner_steps"] - self.accum_loss = config["distill"]["accum_loss"] - self.distill_max_grad_norm = config["distill"]["distill_max_grad_norm"] - self.logging_steps = config["basic"]["logging_steps"] - self.initial_model_path = initial_model_path - self.model = model - self.inputs_embeds = torch.randn( - self.num_classes * self.data_size, - *(model.bert_config.max_position_embeddings, model.bert_config.dim,) - ) - label_classes = torch.tensor( - [[c] * self.data_size for c in range(self.num_classes)] - ).view(-1) - self._labels = torch.eye(self.num_classes)[label_classes] - self.model_lr, self.step_lr_gamma = None, None - if self.model_lr is None: - self.model_lr = torch.tensor(config["distill"]["distill_model_lr"]) - if self.step_lr_gamma is None: - self.step_lr_gamma = torch.tensor( - config["distill"]["distill_step_lr_gamma"]) - - # set on device - self.inputs_embeds = self.inputs_embeds.to(self.device) - self._labels = self._labels.to(self.device) - - self.model_lr = self.model_lr.to(self.device) - self.step_lr_gamma = self.step_lr_gamma.to(self.device) - - # train data loader - self.train_loader = train_loader - # number of traning steps - num_tot_train_steps = len(train_loader) * \ - config["distill"]["n_distill_epochs"] - # set optimizer of distilled data - self.optimize_param_list = [self.inputs_embeds] - - if config["distill"]["optimize_lr"]: - self.optimize_param_list += [self.model_lr, self.step_lr_gamma] - for param in self.optimize_param_list: - param.requires_grad = True - self.d_optimizer = Adam(self.optimize_param_list, - lr=config["distill"]["distill_lr"]) - # scheduler (linear decay with linear warmup) - self.d_scheduler = get_linear_schedule_with_warmup( - self.d_optimizer, - int(num_tot_train_steps * config["distill"]["distill_warmup_ratio"]), - num_tot_train_steps, - ) - # gradient scaler for mixed precision - self.scaler = amp.GradScaler(enabled=self.use_amp) - # initial model parameters - self.initial_state_dict = torch.load(self.initial_model_path) - - def train_distilled_data(self, epoch): - # init model with original params each epoch - self.model.load_state_dict(self.initial_state_dict) - # training loop - cur_num, cur_before_loss, cur_after_loss = 0, 0, 0 - cur_before_correct, cur_after_correct = 0, 0 - with tqdm(self.train_loader, ncols=140, desc=f"Epoch[{epoch+1}]") as pbar: - for outer_step, (input_ids, attention_mask, labels) in enumerate(pbar): - # initialize model parameters - if self.random_init: - self.model.reset_additional_parameters() - # model parameters - weights = OrderedDict(self.model.named_parameters()) - - batch_size = len(input_ids) - cur_num += batch_size - - # acc & loss of initial parameters (before updating with distilled data) - with torch.no_grad(): - with amp.autocast(dtype=self.dtype, enabled=self.use_amp): - before_losses, before_logits, _ = self.model.forward_with_params( - input_ids=input_ids.to(self.device), - attention_mask=attention_mask.to(self.device), - labels=labels.to(self.device), - weights=weights, - ) - cur_before_loss += before_losses.mean().item() * batch_size - cur_before_correct += ( - before_logits.cpu().argmax(1).eq(labels).sum().item() - ) - - # update model parameters with distilled data - loss = 0 - for inner_step in range(self.n_inner_steps): - # forward - d_losses, _, bert_outputs = self.model.forward_with_params( - inputs_embeds=self.inputs_embeds, - labels=self._labels, - weights=weights, - output_attentions=True, - ) - d_loss = d_losses.mean() - d_loss = d_loss * self.model_lr * \ - (self.step_lr_gamma**inner_step) - - # backward - grads = torch.autograd.grad( - d_loss, weights.values(), create_graph=True, allow_unused=True - ) - # update parameters (SGD) - weights = OrderedDict( - (name, param - grad) if grad is not None else (name, param) - for ((name, param), grad) in zip(weights.items(), grads) - ) - - if self.accum_loss or (inner_step + 1) == self.n_inner_steps: - # loss of updated parameters (after each gradient step) - with amp.autocast(dtype=self.dtype, enabled=self.use_amp): - after_losses, after_logits, _ = self.model.forward_with_params( - input_ids=input_ids.to(self.device), - attention_mask=attention_mask.to(self.device), - labels=labels.to(self.device), - weights=weights, - ) - after_loss = after_losses.mean() - loss += after_loss - - cur_after_loss += after_loss.item() * batch_size - cur_after_correct += ( - after_logits.cpu().argmax(1).eq(labels).sum().item() - ) - - self.d_optimizer.zero_grad() - # backward - self.scaler.scale(loss).backward() - # unscale gradients (for gradient clipping) - self.scaler.unscale_(self.d_optimizer) - # gradient cliping - torch.nn.utils.clip_grad_norm_( - self.optimize_param_list, self.distill_max_grad_norm - ) - self.scaler.step(self.d_optimizer) - self.scaler.update() - self.d_scheduler.step() - - # logging - if (outer_step + 1) % self.logging_steps == 0: - logger.info( - "Epoch[{:.2f}] | (before) loss: {:>6.4f}, acc: {:5.2%}" - " -> (after) loss: {:>6.4f}, acc: {:5.2%}" - " | lr={:.2E}, gamma={:.2f}".format( - epoch + (outer_step + 1) / len(pbar), - cur_before_loss / cur_num, - cur_before_correct / cur_num, - cur_after_loss / cur_num, - cur_after_correct / cur_num, - self.model_lr.item(), - self.step_lr_gamma.item(), - ) - ) - cur_num, cur_before_loss, cur_after_loss = 0, 0, 0 - cur_before_correct, cur_after_correct = 0, 0 - - # update infomation of progress bar - pbar.set_postfix( - { - "loss": f"{after_loss.item():.4}", - "lr": f"{self.d_scheduler.get_last_lr()[0]:.1E}", - "gd_scale": f"{self.scaler.get_scale()}", - } - ) - - def train_model_on_distilled_data(self): - # optimizer - model_opt = SGD(self.model.parameters(), lr=1.0) - # gradient updating with distilled data - start_time = time.time() - for inner_step in range(self.n_inner_steps): - losses, _, bert_outputs = self.model( - inputs_embeds=self.inputs_embeds, - labels=self._labels, - output_attentions=True, - ) - loss = losses.mean() - loss = loss * self.model_lr * (self.step_lr_gamma**inner_step) - # backward - model_opt.zero_grad() - loss.backward() - model_opt.step() - end_time = time.time() - start_time - logger.info(f"Time for model traning : {end_time:.2f}s") - - @property - def data_dict(self): - return { - "config": { - "input_embeds_shape": self.inputs_embeds.shape[1:], - "num_classes": self.num_classes, - "data_size": self.data_size, - }, - "inputs_embeds": self.inputs_embeds.cpu().data, - "labels": self._labels.cpu().data, - "lr": self.model_lr.cpu().data, - "gamma": self.step_lr_gamma.cpu().data, - } - - def save_distilled_data(self, path): - # save data as dict - torch.save(self.data_dict, path) - - def load_distilled_data(self, distill_data_path): - data_dict = torch.load(distill_data_path) - self.inputs_embeds = data_dict["inputs_embeds"].to(self.device) - self._labels = data_dict["labels"].to(self.device) - self.model_lr = data_dict["lr"].to(self.device) - self.step_lr_gamma = data_dict["gamma"].to(self.device) - - def test_distilled_data(self, test_loader): - # init model with original params - self.model.load_state_dict(self.initial_state_dict) - self.train_model_on_distilled_data() - test_acc, test_loss = evaluate( - self.model, test_loader, self.dtype, self.use_amp, self.device) - logger.info( - "Evaluate on Test dataset | loss: {:>6.4f}, acc: {:5.2%}".format( - test_loss, - test_acc, - ) - ) - return test_loss, test_acc diff --git a/flagdata/condensation/utils/evaluate.py b/flagdata/condensation/utils/evaluate.py deleted file mode 100644 index 9ff257a..0000000 --- a/flagdata/condensation/utils/evaluate.py +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright © 2023 BAAI. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License") -import torch -from torch.cuda import amp - - -def evaluate(model, test_loader, dtype, use_amp, device): - # model to eval mode - model.eval() - - # compute accuracy and loss - total_num, total_loss, total_correct = 0, 0, 0 - with torch.no_grad(): - for (input_ids, attention_mask, labels) in test_loader: - # forward with mixed precision - with amp.autocast(dtype=dtype, enabled=use_amp): - losses, logits, _ = model( - input_ids=input_ids.to(device), - attention_mask=attention_mask.to(device), - labels=labels.to(device), - ) - loss = losses.mean() - - total_num += len(input_ids) - total_loss += loss.item() * len(input_ids) - total_correct += logits.cpu().argmax(1).eq(labels).sum().item() - - # model to train mode - model.train() - - return total_correct / total_num, total_loss / total_num diff --git a/flagdata/condensation/utils/model.py b/flagdata/condensation/utils/model.py deleted file mode 100644 index 3841ef5..0000000 --- a/flagdata/condensation/utils/model.py +++ /dev/null @@ -1,1076 +0,0 @@ -# Copyright © 2023 BAAI. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License") -from collections import OrderedDict -from torch import nn -from torch.nn import functional as F -import math -import numpy as np -import torch -from packaging import version -from transformers.activations import gelu -from transformers.deepspeed import is_deepspeed_zero3_enabled -from transformers.modeling_outputs import BaseModelOutput -from transformers.modeling_utils import PreTrainedModel, apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer -from transformers import DistilBertConfig - - -def cross_entropy_with_soft_labels(logits, labels): - return (-labels * F.log_softmax(logits, dim=-1)).sum(-1) - - -class BertClassifier(nn.Module): - def __init__(self, bert_model, num_classes, drop_p=0.1): - super().__init__() - - # config - self.bert_config = bert_model.config - self.num_classes = num_classes - - # pretrained bert model - self.bert_model = bert_model - - # pooler - if not hasattr(bert_model, "pooler"): - self.pooler = nn.Sequential( - nn.Linear(bert_model.config.dim, - bert_model.config.dim), nn.Tanh() - ) - else: - self.pooler is None - - # dropout layer - self.dropout = nn.Dropout(p=drop_p) - # single linear layer for classification - self.classifier = nn.Linear(self.bert_config.hidden_size, num_classes) - - # loss function - self.cross_entropy = nn.CrossEntropyLoss(reduction="none") - - def forward( - self, - input_ids=None, - inputs_embeds=None, - attention_mask=None, - labels=None, - output_attentions=False, - ): - # encode input sequences with bert model - bert_outputs = self.bert_model( - input_ids=input_ids, - inputs_embeds=inputs_embeds, - attention_mask=attention_mask, - output_attentions=output_attentions, - ) - - # hidden state of [CLS] token - if "pooler_output" in bert_outputs.keys(): - pooler_output = bert_outputs["pooler_output"] - else: - pooler_output = self.pooler( - bert_outputs["last_hidden_state"][:, 0]) - - # dropout - pooler_output = self.dropout(pooler_output) - - # classifier layer - logits = self.classifier(pooler_output) - - # calculate losses - if labels is not None: - if logits.shape == labels.shape: - losses = cross_entropy_with_soft_labels(logits, labels) - else: - losses = self.cross_entropy(logits, labels) - return losses, logits, bert_outputs - - return logits, bert_outputs - - def forward_with_params( - self, - input_ids=None, - inputs_embeds=None, - attention_mask=None, - labels=None, - weights=None, - output_attentions=False, - ): - assert weights is not None - module_name_list = ["bert_model", "classifier"] - if self.pooler is not None: - module_name_list.append("pooler") - weights_dict = {module_name: OrderedDict() - for module_name in module_name_list} - for name, param in weights.items(): - module_name, param_name = name.split(".", maxsplit=1) - weights_dict[module_name][param_name] = param - - # encode input sequences with bert model - bert_outputs = self.bert_model.forward_with_params( - input_ids=input_ids, - inputs_embeds=inputs_embeds, - attention_mask=attention_mask, - weights=weights_dict["bert_model"], - output_attentions=output_attentions, - ) - - # hidden state of [CLS] token - if "pooler_output" in bert_outputs.keys(): - pooler_output = bert_outputs["pooler_output"] - else: - pooler_output = F.linear( - bert_outputs["last_hidden_state"][:, 0], - weight=weights_dict["pooler"]["0.weight"], - bias=weights_dict["pooler"]["0.bias"], - ) - pooler_output = self.pooler[1](pooler_output) - - # dropout - pooler_output = self.dropout(pooler_output) - - # classifier layer - logits = F.linear( - pooler_output, - weight=weights_dict["classifier"]["weight"], - bias=weights_dict["classifier"]["bias"], - ) - - # calculate losses - if labels is not None: - if logits.shape == labels.shape: - losses = cross_entropy_with_soft_labels(logits, labels) - else: - losses = self.cross_entropy(logits, labels) - return losses, logits, bert_outputs - - return logits, bert_outputs - - def reset_additional_parameters(self): - if self.pooler is not None: - self.pooler[0].reset_parameters() - self.classifier.reset_parameters() - - -DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [ - "distilbert-base-uncased", - "distilbert-base-uncased-distilled-squad", - "distilbert-base-cased", - "distilbert-base-cased-distilled-squad", - "distilbert-base-german-cased", - "distilbert-base-multilingual-cased", - "distilbert-base-uncased-finetuned-sst-2-english", - # See all DistilBERT models at https://huggingface.co/models?filter=distilbert -] - - -# UTILS AND BUILDING BLOCKS OF THE ARCHITECTURE # - - -def create_sinusoidal_embeddings(n_pos, dim, out): - if is_deepspeed_zero3_enabled(): - from transformers import deepspeed - - with deepspeed.zero.GatheredParameters(out, modifier_rank=0): - if torch.distributed.get_rank() == 0: - _create_sinusoidal_embeddings(n_pos=n_pos, dim=dim, out=out) - else: - _create_sinusoidal_embeddings(n_pos=n_pos, dim=dim, out=out) - - -def _create_sinusoidal_embeddings(n_pos, dim, out): - position_enc = np.array( - [ - [pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] - for pos in range(n_pos) - ] - ) - out.requires_grad = False - out[:, 0::2] = torch.FloatTensor(np.sin(position_enc[:, 0::2])) - out[:, 1::2] = torch.FloatTensor(np.cos(position_enc[:, 1::2])) - out.detach_() - - -class Embeddings(nn.Module): - def __init__(self, config): - super().__init__() - self.word_embeddings = nn.Embedding( - config.vocab_size, config.dim, padding_idx=config.pad_token_id - ) - self.position_embeddings = nn.Embedding( - config.max_position_embeddings, config.dim - ) - if config.sinusoidal_pos_embds: - create_sinusoidal_embeddings( - n_pos=config.max_position_embeddings, - dim=config.dim, - out=self.position_embeddings.weight, - ) - - self.LayerNorm = nn.LayerNorm(config.dim, eps=1e-12) - self.dropout = nn.Dropout(config.dropout) - if version.parse(torch.__version__) > version.parse("1.6.0"): - self.register_buffer( - "position_ids", - torch.arange(config.max_position_embeddings).expand((1, -1)), - persistent=False, - ) - - def forward(self, input_ids): - """ - Parameters: - input_ids: torch.tensor(bs, max_seq_length) The token ids to embed. - - Returns: torch.tensor(bs, max_seq_length, dim) The embedded tokens (plus position embeddings, no token_type - embeddings) - """ - seq_length = input_ids.size(1) - - # Setting the position-ids to the registered buffer in constructor, it helps - # when tracing the model without passing position-ids, solves - # isues similar to issue #5664 - if hasattr(self, "position_ids"): - position_ids = self.position_ids[:, :seq_length] - else: - position_ids = torch.arange( - seq_length, dtype=torch.long, device=input_ids.device - ) # (max_seq_length) - position_ids = position_ids.unsqueeze(0).expand_as( - input_ids - ) # (bs, max_seq_length) - - word_embeddings = self.word_embeddings( - input_ids) # (bs, max_seq_length, dim) - position_embeddings = self.position_embeddings( - position_ids - ) # (bs, max_seq_length, dim) - - embeddings = word_embeddings + \ - position_embeddings # (bs, max_seq_length, dim) - embeddings = self.LayerNorm(embeddings) # (bs, max_seq_length, dim) - embeddings = self.dropout(embeddings) # (bs, max_seq_length, dim) - return embeddings - - def forward_with_params(self, input_ids, weights=None): - """ - Parameters: - input_ids: torch.tensor(bs, max_seq_length) The token ids to embed. - - Returns: torch.tensor(bs, max_seq_length, dim) The embedded tokens (plus position embeddings, no token_type - embeddings) - """ - assert weights is not None - module_name_list = ["word_embeddings", - "position_embeddings", "LayerNorm"] - weights_dict = {module_name: OrderedDict() - for module_name in module_name_list} - for name, param in weights.items(): - module_name, param_name = name.split(".", maxsplit=1) - weights_dict[module_name][param_name] = param - - seq_length = input_ids.size(1) - - # Setting the position-ids to the registered buffer in constructor, it helps - # when tracing the model without passing position-ids, solves - # issues similar to issue #5664 - if hasattr(self, "position_ids"): - position_ids = self.position_ids[:, :seq_length] - else: - position_ids = torch.arange( - seq_length, dtype=torch.long, device=input_ids.device - ) # (max_seq_length) - position_ids = position_ids.unsqueeze(0).expand_as( - input_ids - ) # (bs, max_seq_length) - - word_embeddings = F.embedding( - input_ids, weights_dict["word_embeddings"]["weight"] - ) # (bs, max_seq_length, dim) - position_embeddings = F.embedding( - position_ids, weights_dict["position_embeddings"]["weight"] - ) # (bs, max_seq_length, dim) - - embeddings = word_embeddings + \ - position_embeddings # (bs, max_seq_length, dim) - embeddings = F.layer_norm( - embeddings, - (embeddings.size(-1),), - weight=weights_dict["LayerNorm"]["weight"], - bias=weights_dict["LayerNorm"]["bias"], - eps=1e-12, - ) # (bs, max_seq_length, dim) - embeddings = self.dropout(embeddings) # (bs, max_seq_length, dim) - return embeddings - - -class MultiHeadSelfAttention(nn.Module): - def __init__(self, config): - super().__init__() - - self.n_heads = config.n_heads - self.dim = config.dim - self.dropout = nn.Dropout(p=config.attention_dropout) - - assert self.dim % self.n_heads == 0 - - self.q_lin = nn.Linear(in_features=config.dim, out_features=config.dim) - self.k_lin = nn.Linear(in_features=config.dim, out_features=config.dim) - self.v_lin = nn.Linear(in_features=config.dim, out_features=config.dim) - self.out_lin = nn.Linear( - in_features=config.dim, out_features=config.dim) - - self.pruned_heads = set() - - def prune_heads(self, heads): - attention_head_size = self.dim // self.n_heads - if len(heads) == 0: - return - heads, index = find_pruneable_heads_and_indices( - heads, self.n_heads, attention_head_size, self.pruned_heads - ) - # Prune linear layers - self.q_lin = prune_linear_layer(self.q_lin, index) - self.k_lin = prune_linear_layer(self.k_lin, index) - self.v_lin = prune_linear_layer(self.v_lin, index) - self.out_lin = prune_linear_layer(self.out_lin, index, dim=1) - # Update hyper params - self.n_heads = self.n_heads - len(heads) - self.dim = attention_head_size * self.n_heads - self.pruned_heads = self.pruned_heads.union(heads) - - def forward( - self, - query, - key, - value, - mask, - head_mask=None, - output_attentions=False, - ): - """ - Parameters: - query: torch.tensor(bs, seq_length, dim) - key: torch.tensor(bs, seq_length, dim) - value: torch.tensor(bs, seq_length, dim) - mask: torch.tensor(bs, seq_length) - - Returns: - weights: torch.tensor(bs, n_heads, seq_length, seq_length) Attention weights context: torch.tensor(bs, - seq_length, dim) Contextualized layer. Optional: only if `output_attentions=True` - """ - bs, q_length, dim = query.size() - k_length = key.size(1) - # assert dim == self.dim, f'Dimensions do not match: {dim} input vs {self.dim} configured' - # assert key.size() == value.size() - - dim_per_head = self.dim // self.n_heads - - mask_reshp = (bs, 1, 1, k_length) - - def shape(x): - """separate heads""" - return x.view(bs, -1, self.n_heads, dim_per_head).transpose(1, 2) - - def unshape(x): - """group heads""" - return ( - x.transpose(1, 2).contiguous().view( - bs, -1, self.n_heads * dim_per_head) - ) - - q = shape(self.q_lin(query)) # (bs, n_heads, q_length, dim_per_head) - k = shape(self.k_lin(key)) # (bs, n_heads, k_length, dim_per_head) - v = shape(self.v_lin(value)) # (bs, n_heads, k_length, dim_per_head) - - # (bs, n_heads, q_length, dim_per_head) - q = q / math.sqrt(dim_per_head) - # (bs, n_heads, q_length, k_length) - scores = torch.matmul(q, k.transpose(2, 3)) - mask = ( - (mask == 0).view(mask_reshp).expand_as(scores) - ) # (bs, n_heads, q_length, k_length) - # (bs, n_heads, q_length, k_length) - scores.masked_fill_(mask, -float("inf")) - - # (bs, n_heads, q_length, k_length) - weights = nn.Softmax(dim=-1)(scores) - weights = self.dropout(weights) # (bs, n_heads, q_length, k_length) - - # Mask heads if we want to - if head_mask is not None: - weights = weights * head_mask - - # (bs, n_heads, q_length, dim_per_head) - context = torch.matmul(weights, v) - context = unshape(context) # (bs, q_length, dim) - context = self.out_lin(context) # (bs, q_length, dim) - - if output_attentions: - return (context, weights) - else: - return (context,) - - def forward_with_params( - self, - query, - key, - value, - mask, - head_mask=None, - output_attentions=False, - weights=None, - ): - """ - Parameters: - query: torch.tensor(bs, seq_length, dim) - key: torch.tensor(bs, seq_length, dim) - value: torch.tensor(bs, seq_length, dim) - mask: torch.tensor(bs, seq_length) - - Returns: - weights: torch.tensor(bs, n_heads, seq_length, seq_length) Attention weights context: torch.tensor(bs, - seq_length, dim) Contextualized layer. Optional: only if `output_attentions=True` - """ - assert weights is not None - module_name_list = ["q_lin", "k_lin", "v_lin", "out_lin"] - weights_dict = {module_name: OrderedDict() - for module_name in module_name_list} - for name, param in weights.items(): - module_name, param_name = name.split(".", maxsplit=1) - weights_dict[module_name][param_name] = param - - bs, q_length, dim = query.size() - k_length = key.size(1) - # assert dim == self.dim, f'Dimensions do not match: {dim} input vs {self.dim} configured' - # assert key.size() == value.size() - - dim_per_head = self.dim // self.n_heads - - mask_reshp = (bs, 1, 1, k_length) - - def shape(x): - """separate heads""" - return x.view(bs, -1, self.n_heads, dim_per_head).transpose(1, 2) - - def unshape(x): - """group heads""" - return ( - x.transpose(1, 2).contiguous().view( - bs, -1, self.n_heads * dim_per_head) - ) - - # q_lin - q = shape( - F.linear( - query, - weight=weights_dict["q_lin"]["weight"], - bias=weights_dict["q_lin"]["bias"], - ) - ) # (bs, n_heads, q_length, dim_per_head) - # k_lin - k = shape( - F.linear( - key, - weight=weights_dict["k_lin"]["weight"], - bias=weights_dict["k_lin"]["bias"], - ) - ) # (bs, n_heads, k_length, dim_per_head) - # v_lin - v = shape( - F.linear( - value, - weight=weights_dict["v_lin"]["weight"], - bias=weights_dict["v_lin"]["bias"], - ) - ) # (bs, n_heads, k_length, dim_per_head) - - # (bs, n_heads, q_length, dim_per_head) - q = q / math.sqrt(dim_per_head) - # (bs, n_heads, q_length, k_length) - scores = torch.matmul(q, k.transpose(2, 3)) - mask = ( - (mask == 0).view(mask_reshp).expand_as(scores) - ) # (bs, n_heads, q_length, k_length) - # (bs, n_heads, q_length, k_length) - scores.masked_fill_(mask, -float("inf")) - - # (bs, n_heads, q_length, k_length) - weights = nn.Softmax(dim=-1)(scores) - weights = self.dropout(weights) # (bs, n_heads, q_length, k_length) - - # Mask heads if we want to - if head_mask is not None: - weights = weights * head_mask - - # (bs, n_heads, q_length, dim_per_head) - context = torch.matmul(weights, v) - context = unshape(context) # (bs, q_length, dim) - # out_lin - context = F.linear( - context, - weight=weights_dict["out_lin"]["weight"], - bias=weights_dict["out_lin"]["bias"], - ) # (bs, q_length, dim) - - if output_attentions: - return (context, weights) - else: - return (context,) - - -class FFN(nn.Module): - def __init__(self, config): - super().__init__() - self.dropout = nn.Dropout(p=config.dropout) - self.chunk_size_feed_forward = config.chunk_size_feed_forward - self.seq_len_dim = 1 - self.lin1 = nn.Linear(in_features=config.dim, - out_features=config.hidden_dim) - self.lin2 = nn.Linear( - in_features=config.hidden_dim, out_features=config.dim) - assert config.activation in [ - "relu", - "gelu", - ], f"activation ({config.activation}) must be in ['relu', 'gelu']" - self.activation = gelu if config.activation == "gelu" else nn.ReLU() - - def forward(self, input): - - return apply_chunking_to_forward( - self.ff_chunk, self.chunk_size_feed_forward, self.seq_len_dim, input - ) - - def forward_with_params(self, input, weights=None): - assert weights is not None - module_name_list = ["lin1", "lin2"] - weights_dict = {module_name: OrderedDict() - for module_name in module_name_list} - for name, param in weights.items(): - module_name, param_name = name.split(".", maxsplit=1) - weights_dict[module_name][param_name] = param - - def ff_chunk_with_params(input): - x = F.linear( - input, - weight=weights_dict["lin1"]["weight"], - bias=weights_dict["lin1"]["bias"], - ) - x = self.activation(x) - x = F.linear( - x, - weight=weights_dict["lin2"]["weight"], - bias=weights_dict["lin2"]["bias"], - ) - x = self.dropout(x) - return x - - return apply_chunking_to_forward( - ff_chunk_with_params, self.chunk_size_feed_forward, self.seq_len_dim, input - ) - - def ff_chunk(self, input): - x = self.lin1(input) - x = self.activation(x) - x = self.lin2(x) - x = self.dropout(x) - return x - - -class TransformerBlock(nn.Module): - def __init__(self, config): - super().__init__() - - assert config.dim % config.n_heads == 0 - - self.attention = MultiHeadSelfAttention(config) - self.sa_layer_norm = nn.LayerNorm( - normalized_shape=config.dim, eps=1e-12) - - self.ffn = FFN(config) - self.output_layer_norm = nn.LayerNorm( - normalized_shape=config.dim, eps=1e-12) - - def forward(self, x, attn_mask=None, head_mask=None, output_attentions=False): - """ - Parameters: - x: torch.tensor(bs, seq_length, dim) - attn_mask: torch.tensor(bs, seq_length) - - Returns: - sa_weights: torch.tensor(bs, n_heads, seq_length, seq_length) The attention weights ffn_output: - torch.tensor(bs, seq_length, dim) The output of the transformer block contextualization. - """ - # Self-Attention - sa_output = self.attention( - query=x, - key=x, - value=x, - mask=attn_mask, - head_mask=head_mask, - output_attentions=output_attentions, - ) - if output_attentions: - ( - sa_output, - sa_weights, - ) = sa_output # (bs, seq_length, dim), (bs, n_heads, seq_length, seq_length) - else: # To handle these `output_attentions` or `output_hidden_states` cases returning tuples - assert type(sa_output) == tuple - sa_output = sa_output[0] - sa_output = self.sa_layer_norm(sa_output + x) # (bs, seq_length, dim) - - # Feed Forward Network - ffn_output = self.ffn(sa_output) # (bs, seq_length, dim) - ffn_output = self.output_layer_norm( - ffn_output + sa_output - ) # (bs, seq_length, dim) - - output = (ffn_output,) - if output_attentions: - output = (sa_weights,) + output - return output - - def forward_with_params( - self, x, attn_mask=None, head_mask=None, output_attentions=False, weights=None - ): - """ - Parameters: - x: torch.tensor(bs, seq_length, dim) - attn_mask: torch.tensor(bs, seq_length) - - Returns: - sa_weights: torch.tensor(bs, n_heads, seq_length, seq_length) The attention weights ffn_output: - torch.tensor(bs, seq_length, dim) The output of the transformer block contextualization. - """ - assert weights is not None - module_name_list = ["attention", - "sa_layer_norm", "ffn", "output_layer_norm"] - weights_dict = {module_name: OrderedDict() - for module_name in module_name_list} - for name, param in weights.items(): - module_name, param_name = name.split(".", maxsplit=1) - weights_dict[module_name][param_name] = param - - # Self-Attention - sa_output = self.attention.forward_with_params( - query=x, - key=x, - value=x, - mask=attn_mask, - head_mask=head_mask, - output_attentions=output_attentions, - weights=weights_dict["attention"], - ) - if output_attentions: - ( - sa_output, - sa_weights, - ) = sa_output # (bs, seq_length, dim), (bs, n_heads, seq_length, seq_length) - else: # To handle these `output_attentions` or `output_hidden_states` cases returning tuples - assert type(sa_output) == tuple - sa_output = sa_output[0] - # sa_layer_norm - sa_output = F.layer_norm( - sa_output + x, - (sa_output.size(-1),), - weight=weights_dict["sa_layer_norm"]["weight"], - bias=weights_dict["sa_layer_norm"]["bias"], - eps=1e-12, - ) # (bs, seq_length, dim) - - # Feed Forward Network - ffn_output = self.ffn.forward_with_params( - sa_output, weights=weights_dict["ffn"] - ) # (bs, seq_length, dim) - ffn_output = F.layer_norm( - ffn_output + sa_output, - (ffn_output.size(-1),), - weight=weights_dict["output_layer_norm"]["weight"], - bias=weights_dict["output_layer_norm"]["bias"], - eps=1e-12, - ) # (bs, seq_length, dim) - - output = (ffn_output,) - if output_attentions: - output = (sa_weights,) + output - return output - - -class Transformer(nn.Module): - def __init__(self, config): - super().__init__() - self.n_layers = config.n_layers - self.layer = nn.ModuleList( - [TransformerBlock(config) for _ in range(config.n_layers)] - ) - - def forward( - self, - x, - attn_mask=None, - head_mask=None, - output_attentions=False, - output_hidden_states=False, - return_dict=None, - ): # docstyle-ignore - """ - Parameters: - x: torch.tensor(bs, seq_length, dim) Input sequence embedded. - attn_mask: torch.tensor(bs, seq_length) Attention mask on the sequence. - - Returns: - hidden_state: torch.tensor(bs, seq_length, dim) Sequence of hidden states in the last (top) - layer all_hidden_states: Tuple[torch.tensor(bs, seq_length, dim)] - Tuple of length n_layers with the hidden states from each layer. - Optional: only if output_hidden_states=True - all_attentions: Tuple[torch.tensor(bs, n_heads, seq_length, seq_length)] - Tuple of length n_layers with the attention weights from each layer - Optional: only if output_attentions=True - """ - all_hidden_states = () if output_hidden_states else None - all_attentions = () if output_attentions else None - - hidden_state = x - for i, layer_module in enumerate(self.layer): - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_state,) - - layer_outputs = layer_module( - x=hidden_state, - attn_mask=attn_mask, - head_mask=head_mask[i], - output_attentions=output_attentions, - ) - hidden_state = layer_outputs[-1] - - if output_attentions: - assert len(layer_outputs) == 2 - attentions = layer_outputs[0] - all_attentions = all_attentions + (attentions,) - else: - assert len(layer_outputs) == 1 - - # Add last layer - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_state,) - - if not return_dict: - return tuple( - v - for v in [hidden_state, all_hidden_states, all_attentions] - if v is not None - ) - return BaseModelOutput( - last_hidden_state=hidden_state, - hidden_states=all_hidden_states, - attentions=all_attentions, - ) - - def forward_with_params( - self, - x, - attn_mask=None, - head_mask=None, - output_attentions=False, - output_hidden_states=False, - return_dict=None, - weights=None, - ): # docstyle-ignore - """ - Parameters: - x: torch.tensor(bs, seq_length, dim) Input sequence embedded. - attn_mask: torch.tensor(bs, seq_length) Attention mask on the sequence. - - Returns: - hidden_state: torch.tensor(bs, seq_length, dim) Sequence of hidden states in the last (top) - layer all_hidden_states: Tuple[torch.tensor(bs, seq_length, dim)] - Tuple of length n_layers with the hidden states from each layer. - Optional: only if output_hidden_states=True - all_attentions: Tuple[torch.tensor(bs, n_heads, seq_length, seq_length)] - Tuple of length n_layers with the attention weights from each layer - Optional: only if output_attentions=True - """ - assert weights is not None - layer_weights = OrderedDict({i: OrderedDict() - for i in range(self.n_layers)}) - for name, param in weights.items(): - module_name, layer_id, param_name = name.split(".", maxsplit=2) - assert module_name == "layer" - layer_weights[int(layer_id)][param_name] = param - - all_hidden_states = () if output_hidden_states else None - all_attentions = () if output_attentions else None - - hidden_state = x - for i, layer_module in enumerate(self.layer): - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_state,) - - layer_outputs = layer_module.forward_with_params( - x=hidden_state, - attn_mask=attn_mask, - head_mask=head_mask[i], - output_attentions=output_attentions, - weights=layer_weights[i], - ) - hidden_state = layer_outputs[-1] - - if output_attentions: - assert len(layer_outputs) == 2 - attentions = layer_outputs[0] - all_attentions = all_attentions + (attentions,) - else: - assert len(layer_outputs) == 1 - - # Add last layer - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_state,) - - if not return_dict: - return tuple( - v - for v in [hidden_state, all_hidden_states, all_attentions] - if v is not None - ) - return BaseModelOutput( - last_hidden_state=hidden_state, - hidden_states=all_hidden_states, - attentions=all_attentions, - ) - -# INTERFACE FOR ENCODER AND TASK SPECIFIC MODEL # - - -class DistilBertPreTrainedModel(PreTrainedModel): - """ - An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained - models. - """ - - config_class = DistilBertConfig - load_tf_weights = None - base_model_prefix = "distilbert" - - def _init_weights(self, module): - """Initialize the weights.""" - if isinstance(module, nn.Linear): - # Slightly different from the TF version which uses truncated_normal for initialization - # cf https://github.com/pytorch/pytorch/pull/5617 - module.weight.data.normal_( - mean=0.0, std=self.config.initializer_range) - if module.bias is not None: - module.bias.data.zero_() - elif isinstance(module, nn.Embedding): - module.weight.data.normal_( - mean=0.0, std=self.config.initializer_range) - if module.padding_idx is not None: - module.weight.data[module.padding_idx].zero_() - elif isinstance(module, nn.LayerNorm): - module.bias.data.zero_() - module.weight.data.fill_(1.0) - - -class DistilBertModel(DistilBertPreTrainedModel): - def __init__(self, config): - super().__init__(config) - - self.embeddings = Embeddings(config) # Embeddings - self.transformer = Transformer(config) # Encoder - - self.init_weights() - - def get_position_embeddings(self) -> nn.Embedding: - """ - Returns the position embeddings - """ - return self.embeddings.position_embeddings - - def resize_position_embeddings(self, new_num_position_embeddings: int): - """ - Resizes position embeddings of the model if :obj:`new_num_position_embeddings != - config.max_position_embeddings`. - - Arguments: - new_num_position_embeddings (:obj:`int`): - The number of new position embedding matrix. If position embeddings are learned, increasing the size - will add newly initialized vectors at the end, whereas reducing the size will remove vectors from the - end. If position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the - size will add correct vectors at the end following the position encoding algorithm, whereas reducing - the size will remove vectors from the end. - """ - num_position_embeds_diff = ( - new_num_position_embeddings - self.config.max_position_embeddings - ) - - # no resizing needs to be done if the length stays the same - if num_position_embeds_diff == 0: - return - - self.config.max_position_embeddings = new_num_position_embeddings - - old_position_embeddings_weight = ( - self.embeddings.position_embeddings.weight.clone() - ) - - self.embeddings.position_embeddings = nn.Embedding( - self.config.max_position_embeddings, self.config.dim - ) - - if self.config.sinusoidal_pos_embds: - create_sinusoidal_embeddings( - n_pos=self.config.max_position_embeddings, - dim=self.config.dim, - out=self.position_embeddings.weight, - ) - else: - with torch.no_grad(): - if num_position_embeds_diff > 0: - self.embeddings.position_embeddings.weight[ - :-num_position_embeds_diff - ] = nn.Parameter(old_position_embeddings_weight) - else: - self.embeddings.position_embeddings.weight = nn.Parameter( - old_position_embeddings_weight[:num_position_embeds_diff] - ) - # move position_embeddings to correct device - self.embeddings.position_embeddings.to(self.device) - - def get_input_embeddings(self): - return self.embeddings.word_embeddings - - def set_input_embeddings(self, new_embeddings): - self.embeddings.word_embeddings = new_embeddings - - def _prune_heads(self, heads_to_prune): - """ - Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base - class PreTrainedModel - """ - for layer, heads in heads_to_prune.items(): - self.transformer.layer[layer].attention.prune_heads(heads) - - def forward( - self, - input_ids=None, - attention_mask=None, - head_mask=None, - inputs_embeds=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - ): - output_attentions = ( - output_attentions - if output_attentions is not None - else self.config.output_attentions - ) - output_hidden_states = ( - output_hidden_states - if output_hidden_states is not None - else self.config.output_hidden_states - ) - return_dict = ( - return_dict if return_dict is not None else self.config.use_return_dict - ) - - if input_ids is not None and inputs_embeds is not None: - raise ValueError( - "You cannot specify both input_ids and inputs_embeds at the same time" - ) - elif input_ids is not None: - input_shape = input_ids.size() - elif inputs_embeds is not None: - input_shape = inputs_embeds.size()[:-1] - else: - raise ValueError( - "You have to specify either input_ids or inputs_embeds") - - device = input_ids.device if input_ids is not None else inputs_embeds.device - - if attention_mask is None: - attention_mask = torch.ones( - input_shape, device=device) # (bs, seq_length) - - # Prepare head mask if needed - head_mask = self.get_head_mask( - head_mask, self.config.num_hidden_layers) - - if inputs_embeds is None: - inputs_embeds = self.embeddings(input_ids) # (bs, seq_length, dim) - return self.transformer( - x=inputs_embeds, - attn_mask=attention_mask, - head_mask=head_mask, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - def forward_with_params( - self, - input_ids=None, - attention_mask=None, - head_mask=None, - inputs_embeds=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - weights=None, - ): - - assert weights is not None - module_name_list = ["embeddings", "transformer"] - weights_dict = {module_name: OrderedDict() - for module_name in module_name_list} - for name, param in weights.items(): - module_name, param_name = name.split(".", maxsplit=1) - weights_dict[module_name][param_name] = param - - output_attentions = ( - output_attentions - if output_attentions is not None - else self.config.output_attentions - ) - output_hidden_states = ( - output_hidden_states - if output_hidden_states is not None - else self.config.output_hidden_states - ) - return_dict = ( - return_dict if return_dict is not None else self.config.use_return_dict - ) - - if input_ids is not None and inputs_embeds is not None: - raise ValueError( - "You cannot specify both input_ids and inputs_embeds at the same time" - ) - elif input_ids is not None: - input_shape = input_ids.size() - elif inputs_embeds is not None: - input_shape = inputs_embeds.size()[:-1] - else: - raise ValueError( - "You have to specify either input_ids or inputs_embeds") - - device = input_ids.device if input_ids is not None else inputs_embeds.device - - if attention_mask is None: - attention_mask = torch.ones( - input_shape, device=device) # (bs, seq_length) - - # Prepare head mask if needed - head_mask = self.get_head_mask( - head_mask, self.config.num_hidden_layers) - - if inputs_embeds is None: - inputs_embeds = self.embeddings.forward_with_params( - input_ids, weights=weights_dict["embeddings"] - ) # (bs, seq_length, dim) - return self.transformer.forward_with_params( - x=inputs_embeds, - attn_mask=attention_mask, - head_mask=head_mask, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - weights=weights_dict["transformer"], - ) diff --git a/gpt4_data_gen/README.md b/flagdata/data_gen/README.md similarity index 100% rename from gpt4_data_gen/README.md rename to flagdata/data_gen/README.md diff --git a/flagdata/data_gen/__init__.py b/flagdata/data_gen/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/gpt4_data_gen/example.py b/flagdata/data_gen/example.py similarity index 100% rename from gpt4_data_gen/example.py rename to flagdata/data_gen/example.py diff --git a/gpt4_data_gen/prompt_template.py b/flagdata/data_gen/prompt_template.py similarity index 100% rename from gpt4_data_gen/prompt_template.py rename to flagdata/data_gen/prompt_template.py diff --git a/gpt4_data_gen/strategy.py b/flagdata/data_gen/strategy.py similarity index 100% rename from gpt4_data_gen/strategy.py rename to flagdata/data_gen/strategy.py diff --git a/gpt4_data_gen/utils.py b/flagdata/data_gen/utils.py similarity index 100% rename from gpt4_data_gen/utils.py rename to flagdata/data_gen/utils.py diff --git a/deduplication/README.md b/flagdata/deduplication/README.md similarity index 100% rename from deduplication/README.md rename to flagdata/deduplication/README.md diff --git a/flagdata/deduplication/__init__.py b/flagdata/deduplication/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/deduplication/addTwoDigits.py b/flagdata/deduplication/addTwoDigits.py similarity index 100% rename from deduplication/addTwoDigits.py rename to flagdata/deduplication/addTwoDigits.py diff --git a/deduplication/minhash.py b/flagdata/deduplication/minhash.py similarity index 100% rename from deduplication/minhash.py rename to flagdata/deduplication/minhash.py diff --git a/flagdata/deduplication/stringMatching.py b/flagdata/deduplication/stringMatching.py new file mode 100644 index 0000000..8b16b73 --- /dev/null +++ b/flagdata/deduplication/stringMatching.py @@ -0,0 +1,24 @@ +from typing import List + +""" +https://leetcode.cn/problems/string-matching-in-an-array/solutions/1723228/shu-zu-zhong-de-zi-fu-chuan-pi-pei-by-le-rpmt/ +""" + + +def stringMatching(words: List[str]) -> List[str]: + ans = [] + for i, x in enumerate(words): + for j, y in enumerate(words): + if j != i and x in y: + ans.append(x) + break + return ans + + +if __name__ == '__main__': + list = ["mass", "as", "hero", "superhero"] + list2 = ["leetcode", "et", "code"] + list3 = ["blue", "green", "bu"] + print(stringMatching(list)) + print(stringMatching(list2)) + print(stringMatching(list3)) diff --git a/deduplication/udf_spark_addTwoDigits.py b/flagdata/deduplication/udf_spark_addTwoDigits.py similarity index 100% rename from deduplication/udf_spark_addTwoDigits.py rename to flagdata/deduplication/udf_spark_addTwoDigits.py diff --git a/flagdata/deduplication/udf_spark_stringMatching.py b/flagdata/deduplication/udf_spark_stringMatching.py new file mode 100644 index 0000000..58efb92 --- /dev/null +++ b/flagdata/deduplication/udf_spark_stringMatching.py @@ -0,0 +1,54 @@ +from pyspark.sql import SparkSession +from pyspark.sql.functions import udf, array +from pyspark.sql.types import ArrayType, StringType + +# Initialize Spark session +spark = SparkSession.builder.appName("StringMatchingUDF").getOrCreate() + + +# Function to string matching +def stringMatching(words): + ans = [] + for i, x in enumerate(words): + for j, y in enumerate(words): + if j != i and x in y: + ans.append(x) + break + return ans + + +# Register the function as a UDF +string_matching_udf = udf(stringMatching, ArrayType(StringType())) + +# Sample input data +data = [("blue", "green", "bu")] + +# Create a DataFrame +df = spark.createDataFrame(data, ["word1", "word2", "word3"]) +# Dynamically build arrays without hard-coding the number of columns +# df = spark.createDataFrame(data, ["words"]) + +# Apply the UDF to the DataFrame +result_df = df.withColumn("matched_words", string_matching_udf(array("word1", "word2", "word3"))) +# Dynamically build arrays without hard-coding the number of columns +# result_df = df.withColumn("matched_words", string_matching_udf(array(*df.columns))) + +# Show the result +result_df.show(truncate=False) + +# Stop Spark session +spark.stop() + +""" +很多时候，我们想使用spark的分布式处理数据能力，这里提供了一个普通函数改造成spark udf函数，进而使用spark能力的方法， +但是对于想要改造成spark任务的函数需要满足： +1、 数据并行性：函数的输入数据可以划分为多个部分并进行并行处理。 +2、可序列化和不可变性：Spark 中的函数必须是可序列化的，以便在不同节点上传输。 +3、不依赖于特定计算节点：函数的执行不依赖于特定节点的计算资源或数据存储位置，以便能够在集群中的任何节点上执行。 +4、无状态或可共享状态：函数不依赖于外部状态或只依赖于可共享的状态。这样可以确保在不同计算节点上并行执行函数时不会发生冲突或竞争条件。 + + + 在使用 UDF 时，应该考虑性能和优化。有些函数可能在本地 Python 环境中运行良好，但在分布式 Spark 环境中可能效率不高。 + 对于复杂的逻辑或需要大量内存的函数，可能需要进一步的优化和考虑。UDF 是为了简单的逻辑和数据处理而设计的，对于更复杂的计算，可能需要使用 Spark 的原生算子来进行处理。 + +""" diff --git a/flagdata/language_identification/__init__.py b/flagdata/language_identification/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/language_identification/jsonql.py b/flagdata/language_identification/jsonql.py similarity index 100% rename from language_identification/jsonql.py rename to flagdata/language_identification/jsonql.py diff --git a/language_identification/readMe.md b/flagdata/language_identification/readMe.md similarity index 100% rename from language_identification/readMe.md rename to flagdata/language_identification/readMe.md diff --git a/language_identification/split_by_lang.py b/flagdata/language_identification/split_by_lang.py similarity index 70% rename from language_identification/split_by_lang.py rename to flagdata/language_identification/split_by_lang.py index fa70b64..01b0fd7 100644 --- a/language_identification/split_by_lang.py +++ b/flagdata/language_identification/split_by_lang.py @@ -2,7 +2,7 @@ import fasttext # type: ignore from typing import Dict, Optional -from language_identification import jsonql +from flagdata.language_identification import jsonql def predict(model, text: str, k: int = 1): @@ -104,9 +104,12 @@ def __repr__(self): classifier.__enter__() doc_zh = dict(text="面向大模型研究领域的高效易用数据处理工貝包") doc_en = dict(text="Efficient and Easy-to-Use Data Processing Workbags for Large Modeling Research Domain") - doc_ru = dict(text="Эффективные и простые в использовании рабочие пакеты для обработки данных в области исследования больших моделей") - doc_fr = dict(text="Sacs de travail efficaces et faciles à utiliser pour le traitement des données dans le domaine de la recherche sur les grands modèles") - doc_de = dict(text="Effiziente und einfach zu verwendende Datenverarbeitungs-Workbags für große Modellforschungsbereiche") + doc_ru = dict( + text="Эффективные и простые в использовании рабочие пакеты для обработки данных в области исследования больших моделей") + doc_fr = dict( + text="Sacs de travail efficaces et faciles à utiliser pour le traitement des données dans le domaine de la recherche sur les grands modèles") + doc_de = dict( + text="Effiziente und einfach zu verwendende Datenverarbeitungs-Workbags für große Modellforschungsbereiche") doc_ja = dict(text="大規模モデル研究領域のための効率的で使いやすいデータ処理ワークバッグ") results_zh = classifier(doc_zh) results_en = classifier(doc_en) @@ -114,9 +117,15 @@ def __repr__(self): results_fr = classifier(doc_fr) results_de = classifier(doc_de) results_ja = classifier(doc_ja) - print(f"language type: {results_zh.get('lang')}, language source:{results_zh.get('lang_score')} , original text: {results_zh.get('text')} .") - print(f"language type: {results_en.get('lang')}, language source:{results_en.get('lang_score')} , original text: {results_en.get('text')} .") - print(f"language type: {results_ru.get('lang')}, language source:{results_ru.get('lang_score')} , original text: {results_ru.get('text')} .") - print(f"language type: {results_fr.get('lang')}, language source:{results_fr.get('lang_score')} , original text: {results_fr.get('text')} .") - print(f"language type: {results_de.get('lang')}, language source:{results_de.get('lang_score')} , original text: {results_de.get('text')} .") - print(f"language type: {results_ja.get('lang')}, language source:{results_ja.get('lang_score')} , original text: {results_ja.get('text')} .") + print( + f"language type: {results_zh.get('lang')}, language source:{results_zh.get('lang_score')} , original text: {results_zh.get('text')} .") + print( + f"language type: {results_en.get('lang')}, language source:{results_en.get('lang_score')} , original text: {results_en.get('text')} .") + print( + f"language type: {results_ru.get('lang')}, language source:{results_ru.get('lang_score')} , original text: {results_ru.get('text')} .") + print( + f"language type: {results_fr.get('lang')}, language source:{results_fr.get('lang_score')} , original text: {results_fr.get('text')} .") + print( + f"language type: {results_de.get('lang')}, language source:{results_de.get('lang_score')} , original text: {results_de.get('text')} .") + print( + f"language type: {results_ja.get('lang')}, language source:{results_ja.get('lang_score')} , original text: {results_ja.get('text')} .") diff --git a/quickstart/analysis/run_analysis.py b/quickstart/analysis/run_analysis.py deleted file mode 100644 index 3493452..0000000 --- a/quickstart/analysis/run_analysis.py +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright © 2023 BAAI. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License") -from flagdata.analysis.text_analyzer import CoreNLPAnalyzer - -with CoreNLPAnalyzer(url="https://corenlp.run", lang="zh", annotators="parse") as analyzer: - text = "随着大规模预训练模型在人工智能领域所取得的成功，相关研究对数据规模和数据处理工具提出了更高的要求。因此我们推出了FlagData，一个便于使用且易于扩展的数据处理工具包。" - ana_result = analyzer.analyze(text) - print("*" * 10, "tokens", "*" * 10) - print(ana_result.tokens) # tokens - print("*" * 10, "parse", "*" * 10) - print(ana_result.parse_tree) # parse - print("*" * 10, "binaryParse", "*" * 10) - print(ana_result.bi_parse_tree) # binaryParse - print("*" * 10, "basicDependencies", "*" * 10) - print(ana_result.basic_dep) # basicDependencies - print("*" * 10, "enhancedDependencies", "*" * 10) - print(ana_result.enhanced_dep) # enhancedDependencies - print("*" * 10, "enhancedPlusPlusDependencies", "*" * 10) - print(ana_result.enhanced_pp_dep) # enhancedPlusPlusDependencies - print("*" * 10, "entitymentions", "*" * 10) - print(ana_result.entities) # entitymentions - print("*" * 10, "openie", "*" * 10) - print(ana_result.openie) # openie - print("*" * 10, "prettyPrintParseTree", "*" * 10) - print(analyzer.pretty_print_tree(ana_result.parse_tree[0])) # pretty print - # print(ana_result.ann_result) # original server's response format diff --git a/quickstart/condensation/run_distillation.py b/quickstart/condensation/run_distillation.py deleted file mode 100644 index 0ed2926..0000000 --- a/quickstart/condensation/run_distillation.py +++ /dev/null @@ -1,7 +0,0 @@ -# Copyright © 2023 BAAI. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License") -from flagdata.condensation.data_distillation import DataDistillationTrainer -trainer = DataDistillationTrainer("/data1/scripts/FlagData/config/distillation_config.yaml") -trainer.load_data() # setup dataset class here -trainer.fit() diff --git a/tests/test_analysis.py b/tests/test_analysis.py deleted file mode 100644 index 0a0607c..0000000 --- a/tests/test_analysis.py +++ /dev/null @@ -1,26 +0,0 @@ -import unittest -from flagdata.analysis.text_analyzer import CoreNLPAnalyzer - - -class AnalysisTestCase(unittest.TestCase): - def setUp(self): - self.analyzer = CoreNLPAnalyzer(url="https://corenlp.run", lang="zh", annotators="parse") - - def test_analysis(self): - data = "FlagData is a fast and extensible toolkit for data processing provided by BAAI. Enjoy yourself! " - ann = self.analyzer.analyze(data) - print(ann.parse_tree) - - def tearDown(self): - self.analyzer.close() - - -def suite(): - suite = unittest.TestSuite() - suite.addTest(AnalysisTestCase('test_analysis')) - return suite - - -if __name__ == '__main__': - runner = unittest.TextTestRunner() - runner.run(suite()) From 2a8cba9b5dd570e36577f31a77838d1c78baca48 Mon Sep 17 00:00:00 2001 From: wuchengwei Date: Thu, 14 Dec 2023 22:31:37 +0800 Subject: [PATCH 05/38] pipeline --- README_zh.md | 4 ++++ pipeline.png | Bin 0 -> 1341019 bytes 2 files changed, 4 insertions(+) create mode 100644 pipeline.png diff --git a/README_zh.md b/README_zh.md index 6da6815..c6861f1 100644 --- a/README_zh.md +++ b/README_zh.md @@ -1,3 +1,7 @@ +pipeline流程为： +![pipeline](pipeline.png) + +
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b/README.md @@ -1,3 +1,5 @@ +pipeline流程为： +![pipeline](pipeline.png)

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a/flagdata/deduplication/README.md b/flagdata/deduplication/README.md index 6344122..7ad5012 100644 --- a/flagdata/deduplication/README.md +++ b/flagdata/deduplication/README.md @@ -1,4 +1,5 @@ 下面详细描述了如何使用分布式能力来进行数据去重 + 一、Spark standalone集群（1台master2台worker）搭建 1. 安装jdk 1. 下载jdk包 From 230e76d77ee85bde7fb17f570275f29a191a37fa Mon Sep 17 00:00:00 2001 From: wuchengwei Date: Mon, 18 Dec 2023 12:51:29 +0800 Subject: [PATCH 08/38] new functions --- README_zh.md | 14 +- flagdata/all2txt/epub2txt.py | 7 +- flagdata/all2txt/pdf2txt.py | 4 + flagdata/analysis/README_zh.md | 1 + .../analysis/average_rotation_analysis.py | 4 + flagdata/analysis/draw_pie_chart.py | 4 + .../analysis/field_distribution_analysis.py | 12 +- .../language_distribution_analysis.py | 4 + flagdata/analysis/nested_pie_chart.py | 5 + flagdata/analysis/text_length_analysis.py | 4 + flagdata/data_gen/README.md | 16 +- flagdata/data_gen/README_zh.md | 11 + flagdata/data_gen/example.py | 36 +- flagdata/data_gen/prompt_template.py | 4 + flagdata/data_gen/strategy.py | 68 +- flagdata/data_gen/utils.py | 11 +- flagdata/deduplication/README.md | 231 +- flagdata/deduplication/README_zh.md | 191 + flagdata/deduplication/addTwoDigits.py | 12 - flagdata/deduplication/minhash.py | 29 +- flagdata/deduplication/stringMatching.py | 4 + .../deduplication/udf_spark_addTwoDigits.py | 49 - .../deduplication/udf_spark_stringMatching.py | 4 + flagdata/language_identification/README.md | 4 + .../{readMe.md => README_zh.md} | 0 .../language_identification/split_by_lang.py | 4 + .../quality_assessment/Bert/bert_config.yaml | 38 + flagdata/quality_assessment/Bert/evaluate.py | 182 + .../Bert/input_data/example_data.jsonl | 6 + .../Bert/models/config.json | 35 + .../models/tokenizer/special_tokens_map.json | 7 + .../models/tokenizer/tokenizer_config.json | 15 + .../Bert/models/tokenizer/vocab.txt | 21128 ++++++++++++++++ .../network/document_bert_architectures.py | 122 + .../model_architechure_bert_multi_scale.py | 113 + .../quality_assessment/Bert/utils/data.py | 519 + .../quality_assessment/Bert/utils/encode.py | 155 + .../FastText/data/cleared1.jsonl | 100 + .../FastText/data/cn_stopwords.txt | 746 + .../quality_assessment/FastText/evaluate.py | 78 + flagdata/quality_assessment/README.md | 14 + flagdata/quality_assessment/README_zh.md | 9 + .../quality_assessment/quality_assessment.png | Bin 0 -> 27235 bytes requirements.txt | 142 +- 44 files changed, 23858 insertions(+), 284 deletions(-) create mode 100644 flagdata/data_gen/README_zh.md create mode 100644 flagdata/deduplication/README_zh.md delete mode 100644 flagdata/deduplication/addTwoDigits.py delete mode 100644 flagdata/deduplication/udf_spark_addTwoDigits.py create mode 100644 flagdata/language_identification/README.md rename flagdata/language_identification/{readMe.md => README_zh.md} (100%) create mode 100644 flagdata/quality_assessment/Bert/bert_config.yaml create mode 100644 flagdata/quality_assessment/Bert/evaluate.py create mode 100644 flagdata/quality_assessment/Bert/input_data/example_data.jsonl create mode 100644 flagdata/quality_assessment/Bert/models/config.json create mode 100644 flagdata/quality_assessment/Bert/models/tokenizer/special_tokens_map.json create mode 100644 flagdata/quality_assessment/Bert/models/tokenizer/tokenizer_config.json create mode 100644 flagdata/quality_assessment/Bert/models/tokenizer/vocab.txt create mode 100644 flagdata/quality_assessment/Bert/network/document_bert_architectures.py create mode 100644 flagdata/quality_assessment/Bert/network/model_architechure_bert_multi_scale.py create mode 100644 flagdata/quality_assessment/Bert/utils/data.py create mode 100644 flagdata/quality_assessment/Bert/utils/encode.py create mode 100644 flagdata/quality_assessment/FastText/data/cleared1.jsonl create mode 100644 flagdata/quality_assessment/FastText/data/cn_stopwords.txt create mode 100644 flagdata/quality_assessment/FastText/evaluate.py create mode 100644 flagdata/quality_assessment/README.md create mode 100644 flagdata/quality_assessment/README_zh.md create mode 100644 flagdata/quality_assessment/quality_assessment.png diff --git a/README_zh.md b/README_zh.md index c6861f1..5ca706d 100644 --- a/README_zh.md +++ b/README_zh.md @@ -1,5 +1,4 @@ -pipeline流程为： -![pipeline](pipeline.png) +

----------------------------------------------------------------------- -数据是人工智能领域发展的基础要素之一。随着大规模预训练模型及相关技术不断取得突破，在相应研究中使用高效数据处理工具提升数据质量变得越来越重要。因此我们推出了FlagData，一个便于使用且易于扩展的数据处理工具包。FlagData集成了包含清洗、标注、压缩、统计分析等功能在内的多个数据处理工具与算法，为自然语言处理、计算机视觉等领域的模型训练与部署提供了数据层面的有力支撑。 +数据是人工智能领域发展的基础要素之一。随着大规模预训练模型及相关技术不断取得突破，在相应研究中使用高效数据处理工具提升数据质量变得越来越重要。因此我们推出了FlagData，一个便于使用且易于扩展的数据处理工具包。FlagData集成了包含数据来源、数据准备、数据预处理、数据分析等4个阶段的多个数据处理工具与算法，为自然语言处理、计算机视觉等领域的模型训练与部署提供了数据层面的有力支撑。 FlagData支持以下特性： * 安装后简单配置即可上手使用，低代码量实现自定义功能。 -* 基于数据蒸馏算法压缩模型训练数据，达到与全量数据训练可比的效果。 +* 可从原始html/text/pdf/epub 快速清洗得到高质量结构化数据，注重敏感信息滤除，避免隐私泄露风险。 -* 可从原始html/text快速清洗得到高质量结构化数据，注重敏感信息滤除，避免隐私泄露风险。 +* 支持海量文本数据去重，并提供详细的部署文档。 -* 支持自然语言处理和计算机视觉领域多项任务的数据标注，标注结果集方便读取。 +* 支持数据质量评估与常见数据分析。 +完整的pipeline流程以及功能如下图： +![pipeline](pipeline.png) ## 动态 +- [Dec 15st, 2023] FlagData v1.1.0 升级 - [Jan 31st, 2023] FlagData v1.0.0 上线了! -------------------------------------------------------------------------------- diff --git a/flagdata/all2txt/epub2txt.py b/flagdata/all2txt/epub2txt.py index e00d344..1602c71 100644 --- a/flagdata/all2txt/epub2txt.py +++ b/flagdata/all2txt/epub2txt.py @@ -1,8 +1,11 @@ +# Copyright © 2023 BAAI. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License") + + import argparse from unstructured.partition.epub import partition_epub -filename = "/Users/wuchengwei/Desktop/职场开挂必读：高效人士手册（全5册）-职场开挂必读：高效人士手册（全5册）.epub" - def epub2txt(input_path, process_all): elements = partition_epub(filename=input_path) diff --git a/flagdata/all2txt/pdf2txt.py b/flagdata/all2txt/pdf2txt.py index ffa5f9c..4503df3 100644 --- a/flagdata/all2txt/pdf2txt.py +++ b/flagdata/all2txt/pdf2txt.py @@ -1,3 +1,7 @@ +# Copyright © 2023 BAAI. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License") + from unstructured.partition.pdf import partition_pdf import argparse diff --git a/flagdata/analysis/README_zh.md b/flagdata/analysis/README_zh.md index bdb7544..c97d99a 100644 --- a/flagdata/analysis/README_zh.md +++ b/flagdata/analysis/README_zh.md @@ -1,4 +1,5 @@ 数据分析中，提供了一些常见的功能函数 + （1）文本的平均轮次分析代码，计算平均轮次（以换行符为例）： ```bash diff --git a/flagdata/analysis/average_rotation_analysis.py b/flagdata/analysis/average_rotation_analysis.py index cc5e1e8..840f96c 100644 --- a/flagdata/analysis/average_rotation_analysis.py +++ b/flagdata/analysis/average_rotation_analysis.py @@ -1,3 +1,7 @@ +# Copyright © 2023 BAAI. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License") + import jsonlines file_path = './data/average_rounds_data.jsonl' # JSONL文件路径 diff --git a/flagdata/analysis/draw_pie_chart.py b/flagdata/analysis/draw_pie_chart.py index e087265..bf755c8 100644 --- a/flagdata/analysis/draw_pie_chart.py +++ b/flagdata/analysis/draw_pie_chart.py @@ -1,3 +1,7 @@ +# Copyright © 2023 BAAI. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License") + import matplotlib.pyplot as plt # 给定的数据列表 diff --git a/flagdata/analysis/field_distribution_analysis.py b/flagdata/analysis/field_distribution_analysis.py index da03b9c..9604686 100644 --- a/flagdata/analysis/field_distribution_analysis.py +++ b/flagdata/analysis/field_distribution_analysis.py @@ -1,12 +1,16 @@ +# Copyright © 2023 BAAI. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License") + import os import requests import json, copy import openai -openai.api_key = '40de0393c800428a9984dd350659ffef' # os.getenv("AZURE_OPENAI_KEY") -openai.api_base = 'https://llm-sft.openai.azure.com/' # os.getenv("AZURE_OPENAI_ENDPOINT") # your endpoint should look like the following https://YOUR_RESOURCE_NAME.openai.azure.com/ -openai.api_type = 'azure' -openai.api_version = '2023-05-15' # this may change in the future +openai.api_key = '' # os.getenv("AZURE_OPENAI_KEY") +openai.api_base = '' # os.getenv("AZURE_OPENAI_ENDPOINT") # your endpoint should look like the following https://YOUR_RESOURCE_NAME.openai.azure.com/ +openai.api_type = '' +openai.api_version = '' # this may change in the future deployment_name = 'llmsft' # This will correspond to the custom name you chose for your deployment when you deployed a model. diff --git a/flagdata/analysis/language_distribution_analysis.py b/flagdata/analysis/language_distribution_analysis.py index 0139e95..0cae608 100644 --- a/flagdata/analysis/language_distribution_analysis.py +++ b/flagdata/analysis/language_distribution_analysis.py @@ -1,3 +1,7 @@ +# Copyright © 2023 BAAI. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License") + import jsonlines from pathlib import Path diff --git a/flagdata/analysis/nested_pie_chart.py b/flagdata/analysis/nested_pie_chart.py index 2b97240..734601e 100644 --- a/flagdata/analysis/nested_pie_chart.py +++ b/flagdata/analysis/nested_pie_chart.py @@ -1,3 +1,8 @@ +# Copyright © 2023 BAAI. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License") + + import matplotlib.pyplot as plt import numpy as np diff --git a/flagdata/analysis/text_length_analysis.py b/flagdata/analysis/text_length_analysis.py index 10eeda5..59307b0 100644 --- a/flagdata/analysis/text_length_analysis.py +++ b/flagdata/analysis/text_length_analysis.py @@ -1,3 +1,7 @@ +# Copyright © 2023 BAAI. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License") + import jsonlines import statistics diff --git a/flagdata/data_gen/README.md b/flagdata/data_gen/README.md index fc172be..8bda945 100644 --- a/flagdata/data_gen/README.md +++ b/flagdata/data_gen/README.md @@ -1,11 +1,9 @@ -### 基于OpenAI接口的数据增强模块 +### Data enhancement module based on OpenAI interface -利用OpenAI接口，以三种不同策略，构建一系列针对不同能力的单轮SFT数据。策略包括： +The OpenAI interface is utilized to construct a series of single rounds of SFT data for different abilities with three different strategies. The strategies include: -+ ImitateGenerator：以若干案例样本为模板，扩增数据。支持同时生成多种语言数据。 -+ AbilityExtractionGenerator: 利用OpenAI接口，归纳出若干案例样本中包含的能力。根据这个能力集合，生成新样本和答案。 -+ AbilityDirectGenerator: 根据指定的能力类型，或者任务类型，直接生成与该能力或任务相关的新样本。例如，指定能力为“逻辑推理”，则可生成一系列逻辑推理题目及答案。为增强生成样本的多样性，支持排除已生成样本。 - -具体示例见`example.py` - - \ No newline at end of file ++ ImitateGenerator: augment data using several case samples as templates. Supports simultaneous generation of data in multiple languages. ++ AbilityExtractionGenerator: using the OpenAI interface, generalize the abilities contained in several case samples. Generate new samples and answers based on this collection of capabilities. ++ AbilityDirectGenerator: Generate new samples directly related to a specified ability type or task type. For example, if you specify the ability as "Logical Reasoning", you can generate a series of logical reasoning questions and answers. In order to increase the diversity of generated samples, it is supported to exclude already generated samples. + + See `example.py` for an example. \ No newline at end of file diff --git a/flagdata/data_gen/README_zh.md b/flagdata/data_gen/README_zh.md new file mode 100644 index 0000000..fc172be --- /dev/null +++ b/flagdata/data_gen/README_zh.md @@ -0,0 +1,11 @@ +### 基于OpenAI接口的数据增强模块 + +利用OpenAI接口，以三种不同策略，构建一系列针对不同能力的单轮SFT数据。策略包括： + ++ ImitateGenerator：以若干案例样本为模板，扩增数据。支持同时生成多种语言数据。 ++ AbilityExtractionGenerator: 利用OpenAI接口，归纳出若干案例样本中包含的能力。根据这个能力集合，生成新样本和答案。 ++ AbilityDirectGenerator: 根据指定的能力类型，或者任务类型，直接生成与该能力或任务相关的新样本。例如，指定能力为“逻辑推理”，则可生成一系列逻辑推理题目及答案。为增强生成样本的多样性，支持排除已生成样本。 + +具体示例见`example.py` + + \ No newline at end of file diff --git a/flagdata/data_gen/example.py b/flagdata/data_gen/example.py index 23f1bce..4d14765 100644 --- a/flagdata/data_gen/example.py +++ b/flagdata/data_gen/example.py @@ -1,10 +1,15 @@ +# Copyright © 2023 BAAI. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License") + from strategy import * from utils import * import pandas as pd import random import copy import sys -sys.path.append('/home/baaidial/duli/sft_aug/gpt4_n') + +sys.path.append('') from multiprocess_call_api import run_multiprocess, get_gpt4_response import timeout_decorator import openai @@ -16,25 +21,26 @@ TIMEOUT = 240 -openai.api_key = '314c94b92ba04eda88ad930d51c1ae69' # os.getenv("AZURE_OPENAI_KEY") -openai.api_base = 'https://baaiaquila.openai.azure.com/' #os.getenv("AZURE_OPENAI_ENDPOINT") # your endpoint should look like the following https://YOUR_RESOURCE_NAME.openai.azure.com/ -openai.api_type = 'azure' -openai.api_version = '2023-05-15' # this may change in the future +openai.api_key = '' # os.getenv("AZURE_OPENAI_KEY") +openai.api_base = '' # os.getenv("AZURE_OPENAI_ENDPOINT") # your endpoint should look like the following https://YOUR_RESOURCE_NAME.openai.azure.com/ +openai.api_type = '' +openai.api_version = '' # this may change in the future -deployment_name='gpt-4' #This will correspond to the custom name you chose for your deployment when you deployed a model. +deployment_name = '' # This will correspond to the custom name you chose for your deployment when you deployed a model. -wkdir = "/home/baaidial/duli/sft_aug/dat/aug_dat/revising" +wkdir = "" output_path = 'generated_dat.jsonl' -example_ls = ['我小时候吃虾，然后出现过敏反应。几年后，我吃了带有章鱼的虾，然后出现了更严重的过敏反应。每次我去那些有强烈虾味的餐馆，我都会一贯性地出现过敏反应。这可以推断出：', - '小明观察到他周围的人们都喜欢吃巧克力。因此，他得出结论：所有人都喜欢吃巧克力。请问这个结论是否可靠？如果可靠，可以如何解释这种观察结果？如果不可靠，应该如何修正这个结论？', - '在一个实验中，A组实验者使用药物X，B组实验者使用药物Y。最后发现A组的痊愈率明显高于B组。基于这一结果，我们可以得出什么结论？', - '如果一个人经常迟到，那么他的时间观念是强还是弱？', - '如果一个地区的犯罪率降低了，那么这个地区的治安是提高了还是降低了？'] +example_ls = [ + '我小时候吃虾，然后出现过敏反应。几年后，我吃了带有章鱼的虾，然后出现了更严重的过敏反应。每次我去那些有强烈虾味的餐馆，我都会一贯性地出现过敏反应。这可以推断出：', + '小明观察到他周围的人们都喜欢吃巧克力。因此，他得出结论：所有人都喜欢吃巧克力。请问这个结论是否可靠？如果可靠，可以如何解释这种观察结果？如果不可靠，应该如何修正这个结论？', + '在一个实验中，A组实验者使用药物X，B组实验者使用药物Y。最后发现A组的痊愈率明显高于B组。基于这一结果，我们可以得出什么结论？', + '如果一个人经常迟到，那么他的时间观念是强还是弱？', + '如果一个地区的犯罪率降低了，那么这个地区的治安是提高了还是降低了？'] task = '逻辑推理' -generator = ImitateGenerator(example_ls, additional_ceveat = '', multi_language=False) +generator = ImitateGenerator(example_ls, additional_ceveat='', multi_language=False) generator.gen_ques(num_samples=100, task=task, language='中文', model='gpt4') generator.gen_ans() @@ -42,10 +48,10 @@ generator.gen_ques(num_examples=100) generator.gen_ans() -generator = AbilityDirectGenerator(example_ls, additional_ceveat = '', exclude_prev=True) +generator = AbilityDirectGenerator(example_ls, additional_ceveat='', exclude_prev=True) generator.gen_ques(num_examples=100, task=task, num_exclude=500, model='gpt4') generator.gen_ans() out_path = os.path.join(wkdir, output_path) os.makedirs(out_path, exist_ok=False) -save_jsonl(generator.samples_gen, output_path) \ No newline at end of file +save_jsonl(generator.samples_gen, output_path) diff --git a/flagdata/data_gen/prompt_template.py b/flagdata/data_gen/prompt_template.py index c433244..d83f3df 100644 --- a/flagdata/data_gen/prompt_template.py +++ b/flagdata/data_gen/prompt_template.py @@ -1,3 +1,7 @@ +# Copyright © 2023 BAAI. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License") + ability_extraction_prompt = "现在有一系列样本，例如：{sample_str} \n \ 请读题，并列举出这些题目需要运用哪些能力才能完成。\ diff --git a/flagdata/data_gen/strategy.py b/flagdata/data_gen/strategy.py index 861625e..6f1d3b4 100644 --- a/flagdata/data_gen/strategy.py +++ b/flagdata/data_gen/strategy.py @@ -1,8 +1,14 @@ +# Copyright © 2023 BAAI. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License") + import random import re from tqdm import tqdm from utils import get_gpt4_response, clean_patterns -from prompt_templates import ability_extraction_prompt, sample_gen_ability_prompt, single_language_imitate_ques_prompt, multi_language_imitate_ques_prompt, ceveat_precision_prompt,ceveat_unanswerable_prompt, ability_gen_prompt, ability_gen_exclude_sample_prompt +from prompt_templates import ability_extraction_prompt, sample_gen_ability_prompt, single_language_imitate_ques_prompt, \ + multi_language_imitate_ques_prompt, ceveat_precision_prompt, ceveat_unanswerable_prompt, ability_gen_prompt, \ + ability_gen_exclude_sample_prompt def clean(response): @@ -14,8 +20,9 @@ def clean(response): ques = [i for i in ques if len(i) > 5] return ques + class ImitateGenerator(): - def __init__(self, example_ls, additional_ceveat = '', multi_language=False): + def __init__(self, example_ls, additional_ceveat='', multi_language=False): self.example_ls = example_ls if multi_language: self.template_ques = single_language_imitate_ques_prompt @@ -33,20 +40,21 @@ def gen_ques(self, num_samples, task, language, model='gpt4'): language_1 = language_1 if isinstance(language, str) else language[0] ques_ls = [] for i in tqdm(num_samples): - n = random.randint(3,5) + n = random.randint(3, 5) sample_tmp = random.sample(self.example_ls, n) sample_str = '\n'.join(sample_tmp) if self.multi_language: - template_ques = self.template_ques.format(task=task, sample_str=sample_str, language_1=language_1, language_2=language_2) + template_ques = self.template_ques.format(task=task, sample_str=sample_str, language_1=language_1, + language_2=language_2) else: template_ques = self.template_ques.format(task=task, sample_str=sample_str, language_1=language_1) response = get_gpt4_response(template_ques, model) ques = clean(response) ques_ls.extend(ques) - + self.ques_ls = ques_ls return ques_ls - + def gen_ans(self, model='gpt4'): samples_gen = [] num_fail = 0 @@ -57,40 +65,40 @@ def gen_ans(self, model='gpt4'): num_fail += 1 else: ans = ans.strip('\n').strip() - samples_gen.append([{'role':'user', 'content': ques},{'role':'assistant', 'content':ans}]) + samples_gen.append([{'role': 'user', 'content': ques}, {'role': 'assistant', 'content': ans}]) print(f"Total num: {str(len(self.ques_ls))}, Failed num: {str(num_fail)}") self.samples_gen = samples_gen return samples_gen class AbilityExtractionGenerator(): - def __init__(self, example_ls, additional_ceveat = ''): + def __init__(self, example_ls, additional_ceveat=''): self.example_ls = example_ls - + self.template_ability_extraction = ability_extraction_prompt + '\n' + additional_ceveat[0] self.template_sample_gen = sample_gen_ability_prompt + '\n' + additional_ceveat[1] - + def ability_extraction(self, num_examples, model='gpt4'): ability_set = set() ability_ls = [] for i in tqdm(num_examples): - n = random.randint(3,5) + n = random.randint(3, 5) sample_tmp = random.sample(self.example_ls, n) sample_str = '\n'.join(sample_tmp) template_ability_extraction = self.template_ability_extraction.format(sample_str=sample_str) response = get_gpt4_response(template_ability_extraction, model) abilities = clean(response) - + for ability in abilities: ability_name = ability.split("：")[0].split(":")[0].strip() if ability_name not in ability_set: ability_set.add(ability_name) ability_ls.append(ability) - + self.ability_ls = ability_ls return ability_ls - + def gen_sample(self, num_samples, model='gpt4'): samples_gen_ori = [] num_fail = 0 @@ -102,16 +110,17 @@ def gen_sample(self, num_samples, model='gpt4'): ques_type = "选择" else: ques_type = "简答" - + indicator = random.random() if indicator < 0.15: difficulty = '偏难：4步以上推理才能得到答案' elif indicator < 0.6: difficulty = '适中：2-3步推理即可得到答案' else: - difficulty = '简单：1-2步推理即可得到答案' - template_sample_gen = self.template_sample_gen.format(ability=ability, ques_type=ques_type, difficulty=difficulty) - + difficulty = '简单：1-2步推理即可得到答案' + template_sample_gen = self.template_sample_gen.format(ability=ability, ques_type=ques_type, + difficulty=difficulty) + sample = get_gpt4_response(template_sample_gen, model) samples_gen_ori.append(sample) @@ -123,31 +132,31 @@ def gen_sample(self, num_samples, model='gpt4'): ques, ans = sample['response'].split('###') ques = clean_patterns(ques.strip('\n'), patterns) ans = clean_patterns(ans.strip('\n'), patterns) - samples_gen.append([{'role':'user', 'content': ques},{'role':'assistant', 'content':ans}]) + samples_gen.append([{'role': 'user', 'content': ques}, {'role': 'assistant', 'content': ans}]) except: num_fail += 1 - + print(f"Total num: {str(len(self.ques_ls))}, Failed num: {str(num_fail)}") self.samples_gen = samples_gen return samples_gen - + class AbilityDirectGenerator(): - def __init__(self, example_ls, additional_ceveat = '', exclude_prev=True) -> None: + def __init__(self, example_ls, additional_ceveat='', exclude_prev=True) -> None: self.example_ls = example_ls self.exclude_prev = exclude_prev - + self.ability_gen_exclude_prompt = ability_gen_exclude_sample_prompt + '\n' + additional_ceveat self.ability_gen_prompt = self.ability_gen_prompt + '\n' + additional_ceveat def gen_ques(self, num_examples, task, num_exclude=500, model='gpt4'): - + ques_ls = [] for i in tqdm(range(num_examples)): if len(ques_ls) > num_exclude and self.exclude_prev: - n = random.randint(3,5) + n = random.randint(3, 5) sample_tmp = random.sample(self.example_ls, n) - sample_str = '\n'.join(sample_tmp) + sample_str = '\n'.join(sample_tmp) ability_gen_prompt = self.ability_gen_exclude_prompt.format(task=task, sample_str=sample_str) else: ability_gen_prompt = self.ability_gen_prompt.format(task=task) @@ -156,7 +165,7 @@ def gen_ques(self, num_examples, task, num_exclude=500, model='gpt4'): ques_ls.extend(ques) return ques_ls - + def gen_ans(self, model='gpt4'): samples_gen = [] num_fail = 0 @@ -167,8 +176,7 @@ def gen_ans(self, model='gpt4'): num_fail += 1 else: ans = ans.strip('\n').strip() - samples_gen.append([{'role':'user', 'content': ques},{'role':'assistant', 'content':ans}]) + samples_gen.append([{'role': 'user', 'content': ques}, {'role': 'assistant', 'content': ans}]) print(f"Total num: {str(len(self.ques_ls))}, Failed num: {str(num_fail)}") self.samples_gen = samples_gen - return samples_gen - + return samples_gen diff --git a/flagdata/data_gen/utils.py b/flagdata/data_gen/utils.py index d13cfeb..9160c34 100644 --- a/flagdata/data_gen/utils.py +++ b/flagdata/data_gen/utils.py @@ -1,7 +1,12 @@ +# Copyright © 2023 BAAI. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License") + import os import json import openai + def load_jsonl(file_path): dat = open(file_path, 'r').readlines() dat = [json.loads(i) for i in dat] @@ -14,17 +19,19 @@ def save_jsonl(sample_ls, save_path): json_str = json.dumps(ipt, ensure_ascii=False) f.write(json_str + '\n') + def clean_patterns(txt, patterns): for pattern in patterns: txt = txt.replace(pattern, '') return txt + def get_gpt4_response(query, deployment_name='gpt4'): response = openai.ChatCompletion.create( - engine=deployment_name, # engine = "deployment_name". + engine=deployment_name, # engine = "deployment_name". messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": query} ] ) - return response['choices'][0]['message']['content'] \ No newline at end of file + return response['choices'][0]['message']['content'] diff --git a/flagdata/deduplication/README.md b/flagdata/deduplication/README.md index 6344122..e181ffd 100644 --- a/flagdata/deduplication/README.md +++ b/flagdata/deduplication/README.md @@ -1,12 +1,19 @@ -下面详细描述了如何使用分布式能力来进行数据去重 -一、Spark standalone集群（1台master2台worker）搭建 -1. 安装jdk -1. 下载jdk包 - 下载地址 https://www.oracle.com/java/technologies/downloads/ -2. 解压 +The following describes in detail how to use distributed capabilities for data deduplication + +first. Build a Spark standalone cluster (1 master2 worker) +1. Install jdk + +a. Download the jdk package + +Download address https://www.oracle.com/java/technologies/downloads/ + +b. Decompression + tar -zxvf ./jdk-8u181-linux-x64.tar.gz -3. 环境配置 - ~/.bashrc文件尾部添加，添加之后source ~/.bashrc生效 + +c. Environment configuration + + ~/.bashrc Add at the end of the file, after adding source ~/.bashrc Take effect ```bash @@ -20,52 +27,64 @@ export PATH=$PATH:${JAVA_HOME}/bin -4. 成功效果 - java -version显示版本号 -2. 安装spark集群 -1. 下载spark包 - 下载地址 https://spark.apache.org/downloads.html -2. 解压并重命名 +d. Successful effect + + java -version Show version number +2. Install spark cluster + +a. Download the spark package + +Download address https://spark.apache.org/downloads.html + +b. Extract and rename + tar -zxvf spark-2.3.1-bin-hadoop2.6.tgz + mv spark-2.3.1-bin-hadoop2.6.tgz spark-2.3.1 -3. 修改配置文件 -4. 配置spark-env.sh - 1. cd spark-2.3.1 - 2. cp spark-env.sh.template spark-env.sh - 3. vim spark-env.sh + +c. Modify the configuration file + +d. Configure spark-env.sh + + i. cd spark-2.3.1 + + ii. cp spark-env.sh.template spark-env.sh + + iii. vim spark-env.sh ```bash -SPARK_MASTER_PORT=7077 #master 服务端口 -SPARK_MASTER_HOST=172.31.32.51 #master 节点ip ifconifg命令查找，ifconfig命令找不到，需要apt install net-tools 安装再执行ifconfig -JAVA_HOME=/data1/jdk-11.0.15.1 #master 节点jdk地址 echo $JAVA_HOME 查找 -PYSPARK_PYTHON=/data1/miniconda3/bin/python #python环境 +SPARK_MASTER_PORT=7077 #master Service port +SPARK_MASTER_HOST=172.31.32.51 #master node ip ifconifg command to find，ifconfig The command cannot be found and is required. apt install net-tools Installation and execution ifconfig +JAVA_HOME=/data1/jdk-11.0.15.1 #master Node jdk address echo $JAVA_HOME Find +PYSPARK_PYTHON=/data1/miniconda3/bin/python #python Environment PYSPARK_DRIVER_PYTHON=/data1/miniconda3/bin/python -SPARK_MASTER_WEBUI_PORT=50010 #master webUI端口 防止端口冲突lsof -i:50010 -SPARK_WORKER_WEBUI_PORT=50011 #worker webUI端口 +SPARK_MASTER_WEBUI_PORT=50010 #master webUI Port prevents port conflicts lsof -i:50010 +SPARK_WORKER_WEBUI_PORT=50011 #worker webUI Port ``` -使用which python确定路径 -如果没有python环境，推荐conda进行管理： +Use which python to determine the path + +If there is no python environment, it is recommended that conda manage: ```bash -在Linux上安装Miniconda： +Install Miniconda on Linux: -1. 对于Linux系统，使用以下命令下载Miniconda安装脚本： +1. For Linux systems, download the Miniconda installation script using the following command: wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -2. 接下来，运行以下命令来执行安装脚本： +2. Next, run the following command to execute the installation script: bash Miniconda3-latest-Linux-x86_64.sh -3. 按照安装程序的指示进行安装。按照默认设置进行安装，或者根据需要进行自定义设置。 +3. Follow the instructions of the installer to install. Install according to the default settings, or customize the settings as needed. -4. 安装完成后，您可能需要激活Miniconda。可以通过执行以下命令来激活Miniconda： -在vim ~/.bashrc尾部添加 +4. After the installation is complete, you may need to activate Miniconda. You can activate Miniconda by executing the following command: +Add to the tail of vim ~ / .bashrc # >>> conda initialize >>> # !! Contents within this block are managed by 'conda init' !! __conda_setup="$('/root/miniconda3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)" @@ -84,47 +103,51 @@ unset __conda_setup source ~/.bashrc -5. 现在，验证Miniconda是否成功安装，可以尝试执行以下命令来检查Miniconda的版本： +5. Now, to verify that Miniconda is installed successfully, try the following command to check the version of Miniconda: conda --version -如果安装成功，是能够看到安装的Miniconda的版本号。 +If the installation is successful, you can see the version number of the installed Miniconda. -安装完成后，可以使用Miniconda来管理您的Python环境和安装各种包和依赖项。 +After installation, you can use Miniconda to manage your Python environment and install various packages and dependencies. ``` -5. 配置spark-defaults.conf - 1. cp spark-defaults.conf.template spark-defaults.conf - 2. vim spark-defaults.conf +e. 配置Spark-defaults.conf + + i. cp spark-defaults.conf.template spark-defaults.conf + + ii. vim spark-defaults.conf ```bash -spark.master spark://172.31.32.51:7077 #master节点:端口 -spark.driver.host 172.31.32.51 #master节点ip -spark.eventLog.enabled true #开启日志 -spark.eventLog.dir file:///cwwu/spark_setup/spark/eventLog #日志地址 +spark.master spark://172.31.32.51:7077 #master Nodes: Port +spark.driver.host 172.31.32.51 #master Nodes ip +spark.eventLog.enabled true #Open the log +spark.eventLog.dir file://your_path/spark/eventLog #Log address # spark.serializer org.apache.spark.serializer.KryoSerializer # spark.executor.instances 5 -spark.driver.memory 32g # Spark应用程序的驱动器内存，拿之前的示例给个参考值 +spark.driver.memory 32g # Spark application drive memory, take the previous example to give a reference value # spark.executor.memory 340g # spark.executor.extraJavaOptions -XX:+PrintGCDetails -Dkey=value -Dnumbers="one two three" -spark.executor.extraJavaOptions -Dio.netty.tryReflectionSetAccessible=true #Spark执行器配置Netty网络库以提高网络性能 +spark.executor.extraJavaOptions -Dio.netty.tryReflectionSetAccessible=true #Spark Actuator configures Netty Network Library to improve Network performance spark.network.timeout 10000000 spark.memory.offHeap.enabled true -spark.memory.offHeap.size 4g #堆外内存 +spark.memory.offHeap.size 4g #Out-of-heap memory ``` -6. 配置spark worker节点 - 1. 免密 - 1. ~/.ssh/config - 几个work配置几个，vim /etc/hosts配置本地hosts，输入hostname检验，# ubuntu 不要写成ubuntu +f. Configure the spark worker nod + + i. Non-secret + +1. ~/.ssh/config + Several work configurations, vim / etc/hosts configuration local hosts, enter hostname verification, # ubuntu should not be written as ubuntu vim ~/.ssh/config ```bash Host spark-worker0 - HostName 172.31.0.149 # ifconifg命令查找 + HostName 172.31.0.149 # Ifconifg command lookup User root - Port 22 # 机器登录端口号 + Port 22 # Machine login port number Host spark-worker1 HostName 172.xx.x.xxx - User root # ubuntu 不要写成ubuntu + User root # Ubuntu should not be written as ubuntu Port 22 Host spark-master-langchao HostName 172.31.32.51 @@ -136,108 +159,58 @@ Host spark-worker-langchao User root Port 22 ``` - 2. 发送免密 - ssh-copy-id -i ~/.ssh/id_rsa.pub spark-worker1【root用户下执行】 -如果master缺少公钥文件。请按照以下步骤检查和生成公钥文件： +2. Send secret-free + ssh-copy-id -i ~/.ssh/id_rsa.pub spark-worker1【execute under root user】 + If master is missing a public key file. Follow these steps to check and generate the public key file: ```bash -1. 首先，检查是否已经存在名为`id_rsa.pub`的公钥文件。可以执行以下命令检查： +1. First, check to see if a public key file named `id rsa.pub` already exists. You can execute the following command to check: ls ~/.ssh/id_rsa.pub -2. 如果文件不存在，可以使用`ssh-keygen`命令生成新的SSH密钥对。执行以下命令： +2. If the file does not exist, a new SSH key pair can be generated using the `ssh-keygen` command. Execute the following command: ssh-keygen - 按照提示输入路径和密码等信息，生成新的SSH密钥对。 + Follow the prompts to enter information such as path and password to generate a new SSH key pair. -3. 在生成的公钥文件`id_rsa.pub`中复制公钥内容。然后执行`ssh-copy-id`命令将公钥复制到目标主机，确保替换``和``为正确的用户名和远程主机名称： +3. Copy the public key content in the generated public key file `public key rsa.pub`. Then execute the `public key id` command to copy the public key to the target host, and make sure to replace `< your username >` and `< remote host >` as the correct user name and remote host name: ssh-copy-id -i ~/.ssh/id_rsa.pub spark-worker1 ``` -2. 配置workers +ii. Configure workers cp workers.template workers - vim workers，尾部添加 - spark-master #master节点也可以同时当做worker节点使用 + vim workers，tail addition +```bash + spark-master #Master nodes can also be used as worker nodes at the same time spark-worker1 -7. 将配置完成的spark包分别发送给各worker节点 - 1. scp -r spark-2.3.1 root@120.92.85.38:/cwwu/spark-standalone（worker启动的地址必须一致不然不能启动）被访问的文件，必须每一个worker都有（共享数据目录/或者hdfs） -3. 启动spark集群 +``` +g. Send the configured spark packets to each worker node respectively + +scp -r spark-2.3.1 root@120.92.85.38:spark-standalone (the address of worker startup must be the same or it cannot be started) every worker must have (shared data directory / or hdfs) the files being accessed + +3. Start the spark cluster ```bash 启动master ./sbin/start-master.sh -./sbin/stop-master.sh #关闭master +./sbin/stop-master.sh #Close master 启动work ./sbin/start-workers.sh -./sbin/stop-worker.sh #关闭work +./sbin/stop-worker.sh #close work ``` -4. 访问web -1. work http://120.92.14.245:50010/ -5. 提交spark任务 -1. Standalone提交命令 +4. Visit web + +work http://120.92.14.245:50010/ + +5. Submit spark task + +Standalone commit command ```bash cd bin ./spark-submit --master spark://172.31.32.51:7077 --class org.apache.spark.examples.SparkPi ../examples/jars/spark-examples_2.12-3.4.0.jar 10000 -./bin/spark-submit --master spark://172.31.32.51:7077 --class org.apache.spark.examples.SparkPi ./examples/jars/spark-examples_2.12-3.4.0.jar 10000 - -./bin/spark-submit --master spark://172.31.32.51:7077 /cwwu/kenlm/spark_script.py /cwwu/cn/paper_abstract_processed.jsonl /cwwu/spark_test_1105/paper_abstract_output ``` -scp -r /cwwu/cn/paper_abstract_processed.jsonl root@120.92.85.38:/cwwu/cn - - -./bin/spark-submit --master spark://172.31.67.138:7077 /cwwu/text-process-spark-tool/unified_format/post_processing_unified_format_spark_downfile0.py - -./spark-submit /cwwu/kenlm/spark_script.py /cwwu/cn/paper_abstract_processed.jsonl /cwwu/spark_test_1105/paper_abstract_output -*** - -## 改成去重的例子 - -*** - -master(worker) -worker - -172.31.32.51:43851 -172.31.0.149:27602 - -502.6 GiB 72cores -61.7 GiB 16 cores - -893.8s（14.8min） - -小于60s（小于1min） -2kw行（20424772） -87G - - -二、性能分析 -任务名 -数据描述 -Python脚本 -spark集群 -中文过滤 -paper_abstract_processed.jsonl -100w行 -586M -893.8s（14.8min） - -小于60s（小于1min） - - - -单台400（96核*384G） - - -单台200（112核*512G） - - -三、跳板机 -使用需要使用跳板机链接，多台机器通过跳板机，可以通过隧道方式链接 -挂载共享数据盘 -sudo su 切换到root用户 -mkdir /cwwu diff --git a/flagdata/deduplication/README_zh.md b/flagdata/deduplication/README_zh.md new file mode 100644 index 0000000..9af7dbd --- /dev/null +++ b/flagdata/deduplication/README_zh.md @@ -0,0 +1,191 @@ +下面详细描述了如何使用分布式能力来进行数据去重 + +一、Spark standalone集群（1台master2台worker）搭建 +1. 安装jdk +a. 下载jdk包 + 下载地址 https://www.oracle.com/java/technologies/downloads/ +b. 解压 + tar -zxvf ./jdk-8u181-linux-x64.tar.gz +c. 环境配置 + ~/.bashrc文件尾部添加，添加之后source ~/.bashrc生效 + + +```bash +# JAVA_HOME +export JAVA_HOME=/xxx/jdk1.8.0_181 +# PATH +export PATH=$PATH:${JAVA_HOME}/bin +``` + + + + + +d. 成功效果 + java -version显示版本号 +2. 安装spark集群 +a. 下载spark包 + 下载地址 https://spark.apache.org/downloads.html +b. 解压并重命名 + tar -zxvf spark-2.3.1-bin-hadoop2.6.tgz + mv spark-2.3.1-bin-hadoop2.6.tgz spark-2.3.1 +c. 修改配置文件 +d. 配置spark-env.sh + i. cd spark-2.3.1 + ii. cp spark-env.sh.template spark-env.sh + iii. vim spark-env.sh +```bash +SPARK_MASTER_PORT=7077 #master 服务端口 +SPARK_MASTER_HOST=172.31.32.51 #master 节点ip ifconifg命令查找，ifconfig命令找不到，需要apt install net-tools 安装再执行ifconfig +JAVA_HOME=/data1/jdk-11.0.15.1 #master 节点jdk地址 echo $JAVA_HOME 查找 +PYSPARK_PYTHON=/data1/miniconda3/bin/python #python环境 +PYSPARK_DRIVER_PYTHON=/data1/miniconda3/bin/python +SPARK_MASTER_WEBUI_PORT=50010 #master webUI端口 防止端口冲突lsof -i:50010 +SPARK_WORKER_WEBUI_PORT=50011 #worker webUI端口 +``` + + +使用which python确定路径 +如果没有python环境，推荐conda进行管理： + +```bash +在Linux上安装Miniconda： + +1. 对于Linux系统，使用以下命令下载Miniconda安装脚本： + + +wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh + + +2. 接下来，运行以下命令来执行安装脚本： + + +bash Miniconda3-latest-Linux-x86_64.sh + + +3. 按照安装程序的指示进行安装。按照默认设置进行安装，或者根据需要进行自定义设置。 + +4. 安装完成后，您可能需要激活Miniconda。可以通过执行以下命令来激活Miniconda： +在vim ~/.bashrc尾部添加 +# >>> conda initialize >>> +# !! Contents within this block are managed by 'conda init' !! +__conda_setup="$('/root/miniconda3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)" +if [ $? -eq 0 ]; then + eval "$__conda_setup" +else + if [ -f "/root/miniconda3/etc/profile.d/conda.sh" ]; then + . "/root/miniconda3/etc/profile.d/conda.sh" + else + export PATH="/root/miniconda3/bin:$PATH" + fi +fi +unset __conda_setup +# <<< conda initialize <<< + +source ~/.bashrc + + +5. 现在，验证Miniconda是否成功安装，可以尝试执行以下命令来检查Miniconda的版本： +conda --version + +如果安装成功，是能够看到安装的Miniconda的版本号。 + +安装完成后，可以使用Miniconda来管理您的Python环境和安装各种包和依赖项。 +``` + +e. 配置spark-defaults.conf + i. cp spark-defaults.conf.template spark-defaults.conf + ii. vim spark-defaults.conf +```bash +spark.master spark://172.31.32.51:7077 #master节点:端口 +spark.driver.host 172.31.32.51 #master节点ip +spark.eventLog.enabled true #开启日志 +spark.eventLog.dir file://your_path/spark/eventLog #日志地址 +# spark.serializer org.apache.spark.serializer.KryoSerializer +# spark.executor.instances 5 +spark.driver.memory 32g # Spark应用程序的驱动器内存，拿之前的示例给个参考值 +# spark.executor.memory 340g +# spark.executor.extraJavaOptions -XX:+PrintGCDetails -Dkey=value -Dnumbers="one two three" +spark.executor.extraJavaOptions -Dio.netty.tryReflectionSetAccessible=true #Spark执行器配置Netty网络库以提高网络性能 +spark.network.timeout 10000000 +spark.memory.offHeap.enabled true +spark.memory.offHeap.size 4g #堆外内存 +``` + +f. 配置spark worker节点 + i. 免密 + 1. ~/.ssh/config + 几个work配置几个，vim /etc/hosts配置本地hosts，输入hostname检验，# ubuntu 不要写成ubuntu + vim ~/.ssh/config + +```bash +Host spark-worker0 + HostName 172.31.0.149 # ifconifg命令查找 + User root + Port 22 # 机器登录端口号 +Host spark-worker1 + HostName 172.xx.x.xxx + User root # ubuntu 不要写成ubuntu + Port 22 +Host spark-master-langchao + HostName 172.31.32.51 + User root + Port 60022 + +Host spark-worker-langchao + HostName 172.xx.x.xxx + User root + Port 22 +``` + 2. 发送免密 + ssh-copy-id -i ~/.ssh/id_rsa.pub spark-worker1【root用户下执行】 +如果master缺少公钥文件。请按照以下步骤检查和生成公钥文件： + +```bash + +1. 首先，检查是否已经存在名为`id_rsa.pub`的公钥文件。可以执行以下命令检查： + ls ~/.ssh/id_rsa.pub + +2. 如果文件不存在，可以使用`ssh-keygen`命令生成新的SSH密钥对。执行以下命令： + ssh-keygen + + 按照提示输入路径和密码等信息，生成新的SSH密钥对。 + +3. 在生成的公钥文件`id_rsa.pub`中复制公钥内容。然后执行`ssh-copy-id`命令将公钥复制到目标主机，确保替换``和``为正确的用户名和远程主机名称： + ssh-copy-id -i ~/.ssh/id_rsa.pub spark-worker1 +``` + +ii. 配置workers + cp workers.template workers + vim workers，尾部添加 +```bash + spark-master #master节点也可以同时当做worker节点使用 + spark-worker1 +``` +g. 将配置完成的spark包分别发送给各worker节点 + +scp -r spark-2.3.1 root@120.92.85.38:spark-standalone（worker启动的地址必须一致不然不能启动）被访问的文件，必须每一个worker都有（共享数据目录/或者hdfs） + +3. 启动spark集群 + +```bash +启动master +./sbin/start-master.sh +./sbin/stop-master.sh #关闭master +启动work +./sbin/start-workers.sh +./sbin/stop-worker.sh #关闭work +``` + +4. 访问web +work http://120.92.14.245:50010/ +5. 提交spark任务 +Standalone提交命令 + + +```bash +cd bin +./spark-submit --master spark://172.31.32.51:7077 --class org.apache.spark.examples.SparkPi ../examples/jars/spark-examples_2.12-3.4.0.jar 10000 +``` + + diff --git a/flagdata/deduplication/addTwoDigits.py b/flagdata/deduplication/addTwoDigits.py deleted file mode 100644 index 9a2490e..0000000 --- a/flagdata/deduplication/addTwoDigits.py +++ /dev/null @@ -1,12 +0,0 @@ -import random - - -def add_two_digits(): - num1 = random.randint(1, 9) - num2 = random.randint(1, 9) - sum = num1 + num2 - return num1, num2, sum - - -result = add_two_digits() -print("两位数相加的结果为：", result[0], "+", result[1], "=", result[2]) diff --git a/flagdata/deduplication/minhash.py b/flagdata/deduplication/minhash.py index ed1eba7..66a246d 100644 --- a/flagdata/deduplication/minhash.py +++ b/flagdata/deduplication/minhash.py @@ -1,3 +1,16 @@ +""" +@article{ +kocetkov2023the, +title={The Stack: 3 {TB} of permissively licensed source code}, +author={Denis Kocetkov and Raymond Li and Loubna Ben allal and Jia LI and Chenghao Mou and Yacine Jernite and Margaret Mitchell and Carlos Mu{\~n}oz Ferrandis and Sean Hughes and Thomas Wolf and Dzmitry Bahdanau and Leandro Von Werra and Harm de Vries}, +journal={Transactions on Machine Learning Research}, +issn={2835-8856}, +year={2023}, +url={https://openreview.net/forum?id=pxpbTdUEpD}, +note={} +} +""" + import hashlib import re import struct @@ -120,12 +133,6 @@ def sha1_hash32(data): return struct.unpack(" config.score_threshold + + for text_id in text_id_list: + + indexes = np.where(text_index_array == text_id)[0] + cur_text_bool = pred_bool_array[indexes] + + # Object that is continuously determined to correspond to high-quality text index + continuous_ranges = np.where(np.diff(np.concatenate(( + [False], cur_text_bool, [False]))))[0].reshape(-1, 2) + + if len(continuous_ranges) == 0: + text = "".join([text_segments_list[index] for index in indexes]) + score = np.mean(np.array(pred_list)[np.array(indexes)]) + text_dict = {"text_id": text_id, "text": text, "info": info_list[ + text_id_list.index(text_id)], "score": score} + + # Write text that is not in a contiguous interval to a file (jsonl format) + with open(filtered_file_name, "a", encoding="utf-8") as f: + json.dump(text_dict, f, ensure_ascii=False) + f.write('\n') + + continue + + index_lists = [] + for start, end in continuous_ranges: + index_list = [index for index in indexes[start:end]] + index_lists.append(index_list) + text = "".join([text_segments_list[index] for index in index_list]) + score = np.mean(np.array(pred_list)[np.array(index_list)]) + text_dict = {"text_id": text_id, "text": text, "info": info_list[ + text_id_list.index(text_id)], "score": score} + + # Writes filtered text to a file (jsonl format) + with open(dist_file, "a", encoding="utf-8") as f: + json.dump(text_dict, f, ensure_ascii=False) + f.write('\n') + + # Write text that is not in a contiguous interval to a file (jsonl format) + for index in indexes: + flag = False + # Determine whether it is in a continuous interval + for index_list in index_lists: + if index in index_list: + flag = True + break + + if not flag: + score = pred_list[index] + text_dict = {"text_id": text_id, "text": text_segments_list[index], "info": info_list[ + text_id_list.index(text_id)], "score": score} + + with open(filtered_file_name, "a", encoding="utf-8") as f: + json.dump(text_dict, f, ensure_ascii=False) + f.write('\n') + + +def predict(file, model: torch.nn.Module, tokenizer: BertTokenizer, + config: SimpleNamespace): + # Create data_loader + model_cfg = BertConfig.from_pretrained(config.bert_model_path) + num_workers = config.num_workers + dataset = DocumentDatasetForPredict(file, tokenizer, + model_cfg.max_position_embeddings, + model_cfg.doc_cfg, model_cfg.segment_cfg, + config) + + dataloader = DataLoader( + dataset, batch_size=None, num_workers=num_workers, + shuffle=False) + + # Forecast + world_size = torch.cuda.device_count() + + model.eval() + + text_index_list = [] + text_segments_list = [] + representation_segment_list = [] + info_list = [] + with torch.no_grad(): + for text_index, text_segments, representation, info in tqdm( + dataloader, desc=f"Evaluation", leave=False): + + text_index_list.extend(text_index) + text_segments_list.extend(text_segments) + representation_segment_list.append(representation) + info_list.append(info) + + if len(text_index_list) < world_size * config.batch_size: + continue + + # inference + try: + # Splicing segments with torch + representation_doc_token = torch.cat(representation_segment_list, dim=0) + except Exception as e: + logging.warning(f"representation_segment_list ERROR : {representation_segment_list}") + logging.warning(f"INFO: {e}") + continue + # representation_doc_token = torch.cat(representation_segment_list, dim=0) + # (batch_size, seq_len, hidden_size) -> (batch_size, 1, seq_len, hidden_size) + representation_doc_token = representation_doc_token.unsqueeze(dim=1) + with torch.cuda.amp.autocast(): + pred = model(representation_doc_token) + + # Integrate retained text based on text index, text segments, pred + text_select_with_pred(file, pred.tolist(), text_index_list, + text_segments_list, info_list, config) + + +def predict_setup(config): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + # Define the model (data parallelism) + model = DocumentBertScoringModel(config) + checkpoint = torch.load(config.checkpoint) + model.load_state_dict(checkpoint["state_dict"]) + model.to(device) + model = torch.nn.DataParallel(model) + + # dataloader + tokenizer = BertTokenizer.from_pretrained(config.tokenizer_path) + + return model, tokenizer, device + + +def process(config): + model, tokenizer, device = predict_setup(config) + + # Get the json files under data_path + data_path = config.data_path + json_files = [os.path.join(data_path, file) for file in os.listdir(data_path)] + print(json_files) + + for file in json_files: + print("Start reasoning, file: " + file) + predict(file, model, tokenizer, config) + + +import yaml +from types import SimpleNamespace + +if __name__ == "__main__": + # Load the configuration from the YAML file + with open("bert_config.yaml", "r", encoding="utf-8") as f: + cfg = yaml.safe_load(f) + cfg = SimpleNamespace(**cfg) + + # Call the process function and pass the configuration + process(cfg) diff --git a/flagdata/quality_assessment/Bert/input_data/example_data.jsonl b/flagdata/quality_assessment/Bert/input_data/example_data.jsonl new file mode 100644 index 0000000..5763ee9 --- /dev/null +++ b/flagdata/quality_assessment/Bert/input_data/example_data.jsonl @@ -0,0 +1,6 @@ +{"raw_content": " 长信恒利优势股票型证券投资基金\r\n 更新的招募说明书\r\n 2013 年第【2】号\r\n 基金管理人：长信基金管理有限责任公司\r\n 基金托管人：中国建设银行股份有限公司\r\n 二○一三年九月重 要 提 示\r\n 本基金经 2009 年 5 月 27 日中国证券监督管理委员会证监许可【2009】439号文核准募集。本基金的基金合同于 2009 年 7 月 30 日正式生效。\r\n 长信基金管理有限责任公司(以下简称“基金管理人”)保证本招募说明书的\r\n 内容真实、准确、完整。\r\n 本招募说明书经中国证监会核准，但中国证监会对本基金募集的核准，并不表明其对本基金的价值和收益作出实质性判断或保证，也不表明投资于本基金没有风险。\r\n 基金管理人管理的其他基金的业绩并不构成新基金业绩表现的保证。\r\n 投资有风险，本基金投资于证券市场，基金净值会因为证券市场波动等因素产生波动，投资人申购基金时应认真阅读本招募说明书，全面认识本基金产品的风险收益特征，充分考虑投资人自身的风险承受能力，并对于申购基金的意愿、时机、数量等投资行为作出独立决策。基金管理人提醒投资人基金投资要承担相应风险，包括因整体政治、经济、社会等环境因素对证券价格产生影响而形成的系统性风险，个别证券特有的非系统性风险，由于基金投资人连续大量赎回基金产生的流动性风险，基金管理人在基金管理实施过程中产生的基金管理风险等。\r\n 投资有风险，投资者申购基金时请仔细阅读本基金的《招募说明书》及《基金合同》。\r\n 基金的过往业绩并不预示其未来表现。\r\n 基金管理人依照恪尽职守、诚实信用、谨慎勤勉的原则管理和运用基金财产，但不保证本基金一定盈利，也不保证最低收益。因基金价格可升可跌，亦不保证基金份额持有人能全数取回其原始投资。\r\n 本更新的招募说明书所载的内容截止日为 2013 年 7 月 30 日，有关财务数据和净值表现截止日为 2013 年 6 月 30 日（财务数据未经审计）。\r\n 目 录\r\n 一、绪言 .......................................................................................................................................... 2\r\n 二、释义 .......................................................................................................................................... 3\r\n 三、基金管理人 .............................................................................................................................. 6\r\n 四、基金托管人 ............................................................................................................................ 16\r\n 五、相关服务机构 ........................................................................................................................ 21\r\n 六、基金的募集 ............................................................................................................................ 33\r\n 七、基金合同的生效 .................................................................................................................... 34\r\n 八、基金份额的申购与赎回......................................................................................................... 35\r\n 九、基金的投资 ............................................................................................................................ 47\r\n 十、基金投资组合报告................................................................................................................. 60\r\n 十一、基金的业绩 ........................................................................................................................ 64\r\n 十二、基金的财产 ........................................................................................................................ 66\r\n 十三、基金资产的估值................................................................................................................. 68\r\n 十四、基金收益与分配................................................................................................................. 75\r\n 十五、基金费用与税收................................................................................................................. 77\r\n 十六、基金的会计与审计............................................................................................................. 80\r\n 十七、基金的信息披露................................................................................................................. 81\r\n 十八、风险揭示 ............................................................................................................................ 87\r\n 十九、基金合同的变更、终止与基金财产的清算 ..................................................................... 90\r\n 二十、基金合同的内容摘要......................................................................................................... 93\r\n 二十一、基金托管协议的内容摘要 ........................................................................................... 109\r\n 二十二、基金份额持有人服务................................................................................................... 120\r\n 二十三、其它应披露的事项....................................................................................................... 123\r\n 二十四、招募说明书存放及查阅方式 ....................................................................................... 124\r\n 二十五、备查文件 ...................................................................................................................... 125一、绪言本招募说明书依据《中华人民共和国证券投资基金法》（以下简称“《基金法》”）、《证券投资基金运作管理办法》（以下简称“《运作办法》”）、《证券投资基金销售管理办法》（以下简称“《销售办法》”）、《证券投资基金信息披露管理办法》（以下简称“《信息披露办法》”）和其他有关法律法规的规定，以及《长信恒利优势股票型证券投资基金基金合同》（以下简称“合同”或“基金合同”）编写。\r\n 本招募说明书阐述了长信恒利优势股票型证券投资基金的投资目标、投资策略、风险、费率等与投资人投资决策有关的全部必要事项，投资人在做出投资决策前应仔细阅读本招募说明书。\r\n 本基金管理人承诺本招募说明书不存在任何虚假内容、误导性陈述或重大遗漏，并对其真实性、准确性、完整性承担法律责任。\r\n 本基金是根据本招募说明书所载明的资料申请募集的。本招募说明书由长信基金管理有限责任公司解释。本基金管理人没有委托或授权任何其他人提供未在本招募说明书中载明的信息，或对本招募说明书做出任何解释或者说明。\r\n 本招募说明书根据本基金的基金合同编写，并经中国证监会核准。基金合同是约定基金当事人之间权利、义务关系的法律文件。基金投资人自依基金合同取得基金份额，即成为基金份额持有人和基金合同的当事人，其持有基金份额的行为本身即表明其对基金合同的承认和接受，并按照《基金法》、基金合同及其他有关规定享有权利、承担义务。基金投资人欲了解基金份额持有人的权利和义务，应详细查阅基金合同。\r\n 二、释义\r\n 在本招募说明书中，除非文意另有所指，下列词语或简称代表如下含义：\r\n 释义\r\n 基金或本基金 指长信恒利优势股票型证券投资基金\r\n 基金管理人 指长信基金管理有限责任公司\r\n 基金托管人 指中国建设银行股份有限公司\r\n 基金合同或本基金合同 指《长信恒利优势股票型证券投资基金基金合同》及对本基金合同的任何有效修订和补充托管协议指基金管理人与基金托管人就本基金签订之《长信恒利优势股票型证券投资基金托管协议》及对该托管协议的任何有效修订和补充\r\n 招募说明书 指《长信恒利优势股票型证券投资基金招募说明书》及其定期的更新\r\n 基金份额发售公告 指《长信恒利优势股票型证券投资基金份额发售公告》法律法规\r\n 指中国现行有效并公布实施的法律、行政法规、规范性文件、司法解释、行政规章以及其他对基金合同当事人有约束力的决定、决议、通知等《基金法》\r\n 指 2003 年 10 月 28 日经第十届全国人民代表大会常务委员会第五次会议通过，自 2004 年 6 月 1 日起实施的《中华人民共和国证券投资基金法》及对其不时作出的修订\r\n 《销售办法》 指中国证监会 2011 年 6月 9 日颁布、同年 10月 1日实施的《证券投资基金销售管理办法》及对其不时作出的修订《信息披露办法》 指中国证监会 2004 年 6 月 8 日颁布、同年 7 月 1 日实施的《证券投资基金信息披露管理办法》及对其不时作出的修订《运作办法》 指中国证监会 2004 年 6月 29 日颁布、同年 7月 1日实施的《证券投资基金运作管理办法》及对其不时作出的修订中国 指中华人民共和国，就本基金合同而言，不包括香港特别行政区、澳门特别行政区和台湾地区中国证监会 指中国证券监督管理委员会\r\n 银行业监督管理机构 指中国人民银行和/或中国银行业监督管理委员会\r\n 基金合同当事人 指受基金合同约束，根据基金合同享有权利并承担义务的法律主体，包括基金管理人、基金托管人和基金份额持有人个人投资者\r\n 指合法持有现时有效的中国居民身份证、军人证件等有效身份证件的中国公民，以及中国证监会允许的其他可以投资证券投资基金的自然人机构投资者\r\n 指依法可以投资开放式证券投资基金的、在中国境内合法注册登记并存续或经有关政府部门批准设立并存续的企业法人、事业法人、社会团体或其他组织合格境外机构投资者 指符合《合格境外机构投资者境内证券投资管理办法》及其他相关法律法规规定的条件，经中国证监会批准投资于中国证券市场，并取得国家外汇管理局额度批准的中国境外基金管理机构、保险公司、证券公司以及其他资产管理机构投资人 指个人投资者、机构投资者和合格境外机构投资者以及法律法规或中国证监会允许购买证券投资基金的其他投资人的合称基金份额持有人 指依基金合同、招募说明书及其他有关文件合法取得基金份额的投资人基金销售业务\r\n 指基金管理人或代销机构宣传推介基金，发售基金份额，办理基金份额的申购、赎回、转换、非交易过户、转托管及定期定额投资等业务\r\n 销售机构 指直销机构和代销机构\r\n 直销中心 指长信基金管理有限责任公司代销机构\r\n 指符合《销售办法》和中国证监会规定的其他条件，取得基金代销业务资格并与基金管理人签订了基金销售服务代理协议，代为办理基金销售业务的机构\r\n 基金销售网点 指直销机构的直销中心及代销机构的代销网点注册登记业务\r\n 指基金登记、存管、过户、清算和结算业务，具体内容包括投资人基金账户的建立和管理、基金份额注册登记、基金销售业务的确认、清算和结算、代理发放红利、建立并保管基金份额持有人名册等注册登记机构指办理注册登记业务的机构。基金的注册登记机构为长信基金管理有限责任公司或接受长信基金管理有限责任公司委托代为办理注册登记业务的机构\r\n 基金账户 指注册登记机构为投资人开立的、记录其持有的、基金管理人所管理的基金份额余额及其变动情况的账户基金交易账户 指销售机构为投资人开立的、记录投资人通过该销售机构买卖基金份额的变动及结余情况的账户基金合同生效日指基金募集期结束后达到法律法规规定及基金合同约定的备案条件，基金管理人向中国证监会办理基金备案手续完毕，并获得其书面确认的日期\r\n 基金合同终止日 指基金合同规定的基金合同终止事由出现后，基金财产清算完毕，清算结果报中国证监会备案并予以公告的日期基金募集期限 指自基金份额发售之日起至发售结束之日止的期间，最长不得超过 3 个月存续期 指基金合同生效至终止之间的不定期期限\r\n 日 指公历日\r\n 月 指公历月\r\n 工作日 指上海证券交易所、深圳证券交易所的正常交易日T 日 指销售机构在规定时间受理投资人申购、赎回或其他业务申请的工作日T+n 日 指自 T日起第 n个工作日（不包含 T日）\r\n 开放日 指为投资人办理基金份额申购、赎回或其他业务的日期交易时间 指开放日基金接受申购、赎回或其他交易的时间段场内销售 指通过上海证券交易所开放式基金销售系统办理的基金销售业务\r\n 场外销售 指不通过上海证券交易所开放式基金销售系统，而通过各销售机构柜台系统办理的基金销售业务认购 指在基金募集期间，投资人申请购买基金份额的行为申购 指基金合同生效后，投资人根据基金合同和招募说明书的规定申请购买基金份额的行为赎回 指基金合同生效后，基金份额持有人按基金合同规定的条件要求将基金份额兑换为现金的行为基金转换指基金份额持有人按照本基金合同和基金管理人届时有效公告\r\n 规定的条件，申请将其持有基金管理人管理的、某一基金的基金份额转换为基金管理人管理的其他基金基金份额的行为\r\n 转托管 指基金份额持有人在本基金的不同销售机构之间实施的变更所持基金份额销售机构的操作定期定额投资计划\r\n 指投资人通过有关销售机构提出申请，约定每期扣款日、扣款金额及扣款方式，由销售机构于每期约定扣款日在投资人指定银行账户内自动完成扣款及基金申购申请的一种投资方式巨额赎回指本基金单个开放日，基金净赎回申请（赎回申请份额总数加上基金转换中转出申请份额总数后扣除申购申请份额总数及基金转换中转入申请份额总数后的余额）超过上一日基金总份额的\r\n 10%时\r\n 元 指人民币元基金收益\r\n 指基金投资所得红利、股息、债券利息、买卖证券价差、银行存款利息、已实现的其他合法收入及因运用基金财产带来的成本和费用的节约\r\n 基金资产总值 指基金拥有的各类有价证券、银行存款本息、基金应收申购款及其他资产的价值总和基金资产净值 指基金资产总值减去基金负债后的价值\r\n 基金份额净值 指计算日基金资产净值除以计算日基金份额总数的数值\r\n 基金资产估值 指计算评估基金资产和负债的价值，以确定基金资产净值和基金份额净值的过程指定媒体 指中国证监会指定的用以进行信息披露的报刊、互联网网站及其他媒体不可抗力\r\n 指本基金合同当事人不能预见、不能避免且不能克服，且在本基金合同由基金管理人、基金托管人签署之日后发生的，使本基金合同当事人无法全部或部分履行本基金合同的任何事件，包括但不限于洪水、地震及其他自然灾害、战争、骚乱、火灾、政府征用、没收、法律法规变化、突发停电或其他突发事件、证券交易所非正常暂停或停止交易三、基金管理人\r\n （一） 基金管理人基本情况基金管理人概况\r\n 名称 长信基金管理有限责任公司\r\n 注册地址 上海市浦东新区银城中路 68 号 9 楼\r\n 办公地址 上海市浦东新区银城中路 68 号 9 楼\r\n 邮政编码 200120\r\n 批准设立机关 中国证券监督管理委员会\r\n 批准设立文号 中国证监会证监基金字[2003]63 号\r\n 注册资本 壹亿伍仟万元人民币\r\n 设立日期 2003 年 5月 9日\r\n 组织形式 有限责任公司\r\n 法定代表人 田丹\r\n 电话 021-61009999\r\n 传真 021-61009800\r\n 联系人 魏明东\r\n 存续期间 持续经营\r\n 经营范围 基金管理业务，发起设立基金，中国证监会批准的其他业务 （涉及许可经营的凭许可证经营）股权结构\r\n 股东名称 出资额 出资比例\r\n 长江证券股份有限公司 7350 万元 49%\r\n 上海海欣集团股份有限公司 5149.5 万元 34.33%\r\n 武汉钢铁股份有限公司 2500.5 万元 16.67%\r\n 总计 15000 万元 100%\r\n （二）主要人员情况\r\n 1、基金管理人的董事会成员情况董事会成员\r\n 姓名 职务 性别 简历\r\n 田丹 董事长 男中共党员，硕士，EMBA。现任长信基金管理有限责任公司董事长，兼长江证券股份有限公司第六届监事会监事长。曾任湖北省军区参谋、秘书，中国人民银行湖北省分行金融调研处科长，湖北证券公司证券交易部经理、总经理助理、副总经理，三峡证券公司副总经理，湖北证券有限责任公司董事、副总裁，长江证券有限责任公司董事、副总裁、财务负责人，长江证券有限责任公司董事、总裁。\r\n 胡运钊 董事 男中共党员，在职研究生学历，高级经济师。现任长江证券股份有限公司董事长、党委书记。曾任湖北省经委工业处副处长、处长，黄石市人民政府副市长，中共黄石市委常委、常务副市长，湖北省人民政府副秘书长，省政府副秘书长兼办公厅主任、办公厅党组副书记，长江证券有限责任公司党委书记。\r\n 陈谋亮 董事 男中共党员，法学硕士，工商管理博士，高级经济师，高级商务谈判师。现任上海海欣集团股份有限公司总裁。曾任湖北经济学院讲师；扬子石化化工厂、总裁办、股改办、宣传部、企管处管理干部，集团法律顾问，中石化/扬子石化与德国巴斯夫合资特大\r\n 型一体化石化项目中方谈判代表；上海交大南洋部门经理兼证券事务代表；上海海欣集团股份有限公\r\n 司董事会秘书，总裁助理，战略投资管理部常务副总监、总监，副总裁，常务副总裁兼董事会秘书等职。\r\n 彭辰 董事 男中共党员，在职研究生学历，经济学博士，正高职高级会计师，研究员。现任武汉钢铁（集团）公司党委常委、武汉钢铁股份有限公司总经理。曾任武钢经济技术中心副干事长，财务部副部长兼会计处处长、财务部部长，总会计师兼财务部部长，总会计师，总会计师兼财务委派管理中心主任，副总经理兼总会计师、财务委派管理中心主任，副总经理兼总会计师，党委常委、副总经理兼总会计师。\r\n 蒋学杰 董事 男\r\n 经济学硕士，澳大利亚墨尔本大学商学院 MBA。现任长信基金管理有限责任公司总经理。曾任中南财经大学财政金融学院财政系教师、武汉科技信托投资公司武昌证券营业部总经理，并兼公司总助，副总 等 职 、 Loftus Capital Partners Limited，\r\n Australia 旗下基金亚洲市场投资组合及私人股权\r\n 投资的亚洲区域负责人、KVB Kunlun Pty Ltd，\r\n Australia 金融衍生产品持牌交易投资顾问、长信基金管理有限责任公司副总经理。\r\n 朱恒 独立董事 男中共党员，本科，高级经济师。曾任中国农业银行上海市分行副行长，上海浦东发展银行副行长，万国证券公司、申银万国证券公司董事长，上海浦东发展银行党委副书记、副行长，上海轻工控股（集团）公司监事会主席。\r\n 胡柏枝 独立董事 男中共党员，大专，高级会计师，注册会计师，注册税务师。曾任湖北省财政厅副厅长、党组副书记，湖北省国有资产管理局副局长、局长，中国三峡总公司财务部主任、副总会计师，三峡财务公司总经理，财政部驻湖北省专员办监察专员、党组书记、巡视员，湖北省人大财经委、预工委副主任。\r\n 黄宪 独立董事 男中共党员，博士，博士生导师。现任武汉大学经济与管理学院金融系教授、博士生导师。曾任武汉大学教师、商学院副院长，美国新泽西西东大学访问学者，武汉大学珞珈城市信用社副董事长，中港合资武新公司财务总经理。\r\n 宋晓燕 独立董事 女 中共党员，法学博士，律师。现任上海财经大学法学院教授。曾任武汉大学管理学院国际金融系教师。\r\n 注：上述人员之间均不存在近亲属关系\r\n 2、监事会成员监事会成员\r\n 姓名 职务 性别 简历\r\n 余汉生 监事会主席 男中共党员，本科，正高职高级会计师。现任武汉钢铁股份有限公司计划财务部部长。曾任武钢实业公司审计处处长、武钢实业公司财务处处长、武汉钢铁（集团）公司计划财务部会计处处长。\r\n 陈水元 监事 男\r\n 硕士研究生，会计师、经济师。现任长江证券股份有限公司执行副总裁、财务负责人。曾任湖北证券有限责任公司营业部财务主管、经纪事业部财务经理，长江证券有限责任公司经纪事业部总经理助理、经纪业务总部总经理助理、营业部总经理，长江证券股份有限公司营业部总经理总裁特别助理。\r\n 杨爱民 监事 男中共党员，在职研究生学历，会计师。现任上海海欣集团股份有限公司财务副总监，主持财务总监室工作。曾任甘肃铝厂财务科副科长、科长；甘肃省铝业公司财务处副处长、处长。1997 年至 2008 年任上海海欣集团股份有限公司财务副总监，2009 年\r\n 至2012年6月任上海海欣集团股份有限公司审计室主任。\r\n 李毅 监事 女中共党员，硕士。现任长信基金管理有限责任公司零售服务部负责人。曾任长信基金管理有限责任公司综合管理部副总监。\r\n 郜杰 监事 男中共党员，法律硕士。现任长信基金管理有限责任公司监察稽核部副总监。曾任长信基金管理有限责任公司监察稽核部法律事务专员、风险监察经理。\r\n 注：上述人员之间均不存在近亲属关系\r\n 3、经理层成员经理层成员\r\n 姓名 职务 性别 简历\r\n 蒋学杰 总经理 男 简历同上。\r\n 周永刚 督察长 男\r\n 经济学硕士，EMBA。现任长信基金管理有限责任公司督察长。曾任湖北证券有限责任公司武汉自治街营业部总经理，长江证券有限责任公司北方总部总经理兼北京展览路证券营业部总经理，长江证券有限责任公司经纪业务总部副总经理兼上海代表处主\r\n 任、上海汉口路证券营业部总经理。\r\n 宋小龙 副总经理 男理学硕士，北京大学计算机软件专业研究生毕业，具有基金从业资格。2012 年 10 月加入长信基金管理有限责任公司，现任长信基金管理有限责任公司副总经理，兼任长信金利趋势股票型证券投资基金的基金经理。曾任北京北大青鸟公司项目经理、富国基金管理公司项目经理、行业研究员、基金经理及权益投资部总经理。\r\n 覃波 副总经理 男硕士，上海国家会计学院 EMBA 毕业，具有基金从业资格。现任长信基金管理有限责任公司副总经理。\r\n 曾在长江证券公司从事资产管理和债券承销发行工\r\n 作。2002 年加入长信基金管理有限责任公司，历任\r\n 市场开发部区域经理、营销策划部副总监、市场开发部总监、专户理财部总监、总经理助理。\r\n 注：上述人员之间均不存在近亲属关系\r\n 4、基金经理本基金基金经理情况\r\n 姓名 职务 任职时间 简历\r\n 叶松 基金经理 2011 年 3月 30 日至今\r\n 经济学硕士，中南财经政法大学投资学专业研究生毕业，具有基金从业资格。现任本基金的基金经理。2007 年 7 月加入长信基金管理有限责任公司，担任行业研究员，从事行业和上市公司研究工作，2010\r\n 年 5 月 26 日开始兼任长信增利动态策略股票型证券投资基金的基金经理助理。\r\n 李枝蓬 基金经理 2009 年 7月 30 日至2011 年 4月 21 日 曾任本基金的基金经理。\r\n 曾芒 基金经理 2009 年 7月 30 日至2010 年 8月 26 日 曾任本基金的基金经理。\r\n 5、投资决策委员会成员投资决策委员会成员\r\n 姓名 职务\r\n 蒋学杰 总经理\r\n 宋小龙 副总经理、投资决策委员会主任委员、长信金利趋势股票型证券投资基金的基金经理胡志宝\r\n 公司投资总监、投资决策委员会执行委员、长信金利趋势股票型证券投资基金的基金经理、长信增利动态策略股票型证券投资基金的基金经理安昀 研究发展部副总监、长信内需成长股票型证券投资基金的基金经理、长信银利精选开放式证券投资基金的基金经理姚飞军 交易管理部总监\r\n 雷勇 产品开发部总监、专户理财部副总监、投资决策委员会执行委员李小羽\r\n 总经理助理、固定收益部总监、投资决策委员会执行委员、长信利丰债券型证券投资基金的基金经理、长信可转债债券型证券投资基金的基金经理\r\n 薛天 国际业务部投资总监、投资决策委员会执行委员、长信美国标准普尔100 等权重指数增强型证券投资基金的基金经理注：上述人员之间均不存在近亲属关系\r\n （三）内部组织结构及员工情况内部组织结构及员工情况\r\n 股东会是公司的最高权力机构，下设董事会和监事会。公司组织管理实行董事会领导下的总经理负责制，总经理、副总经理和督察长组成公司的经营管理层。\r\n 公司经营管理层下\r\n 设的委员会 内部控制委员会、投资决策委员会公司内部组织结构\r\n 金融工程部、投资管理部、研究发展部、固定收益部、交易管理部、基金事务部、零售服务部、机构业务部、信息技术部、监察稽核部、综合行政部、专户理财部、国际业务部、产品开发部分支机构 北京分公司、深圳分公司、武汉办事处员工总数 109 人（截至 2013 年 7月 30 日）员工学历构成\r\n 学历 人数 占比\r\n 博士 2 人 2%\r\n 硕士 58 人 53%\r\n 本科 43 人 39%\r\n 专科 4 人 4%\r\n 其他 2 人 2%\r\n 总计 109 人 100%\r\n （四）基金管理人的职责\r\n 1、依法募集基金，办理或者委托经中国证监会认定的其他机构代为办理基\r\n 金份额的发售、申购、赎回和登记事宜；\r\n 2、办理基金备案手续；\r\n 3、建立健全内部风险控制、监察与稽核、财务管理及人事管理等制度，保\r\n 证所管理的基金财产和基金管理人的财产相互独立，对所管理的不同基金财产分别管理，分别记账，进行证券投资；\r\n 4、按照基金合同的约定确定基金收益分配方案，及时向基金份额持有人分配收益；\r\n 5、进行基金会计核算并编制基金财务会计报告；\r\n 6、编制基金季度、半年度和年度报告；\r\n 7、计算并公告基金资产净值，确定基金份额申购、赎回价格；\r\n 8、严格按照《基金法》、基金合同及其他有关规定，履行信息披露及报告义务；\r\n 9、依据《基金法》、基金合同及其他有关规定召集基金份额持有人大会或配\r\n 合基金托管人、基金份额持有人依法召集基金份额持有人大会；\r\n 10、保存基金财产管理业务活动的记录、账册、报表和其他相关资料；\r\n 11、以基金管理人名义，代表基金份额持有人利益行使诉讼权利或者实施其他法律行为；\r\n 12、法律法规、中国证监会和基金合同规定的其他职责。\r\n （五）基金管理人的承诺\r\n 1、基金管理人承诺严格遵守《基金法》及相关法律法规，建立健全的内部控制制度，采取有效措施，防止违反上述法律法规的行为发生；\r\n 2、基金管理人承诺防止下列禁止行为的发生：\r\n （1）将其固有财产或者他人财产混同于基金财产从事证券投资；\r\n （2）不公平地对待其管理的不同基金财产；\r\n （3）利用基金财产为基金份额持有人以外的第三人牟取利益；\r\n （4）向基金份额持有人违规承诺收益或者承担损失；\r\n （5）依照法律法规有关规定，由中国证监会禁止的其他行为。\r\n 3、基金经理承诺\r\n （1）依照有关法律法规和基金合同的规定，本着谨慎的原则为基金份额持有人谋取最大利益；\r\n （2）不利用职务之便为自己、受雇人或任何第三者谋取利益；\r\n （3）不泄露在任职期间知悉的有关证券、基金的商业秘密，尚未依法公开\r\n 的基金投资内容、基金投资计划等信息；\r\n （4）不协助、接受委托或以其他任何形式为其他组织或个人进行证券交易。\r\n （六）基金管理人的风险管理和内部控制制度\r\n 基金管理人高度重视内部风险控制，建立了完善的风险管理体系和控制体系，从制度上保障本基金的规范运作。\r\n 1、公司内部控制的总体目标\r\n （1）保证公司经营管理活动的合法合规性；\r\n （2）保证基金份额持有人的合法权益不受侵犯；\r\n （3）实现公司稳健、持续发展、维护股东权益；\r\n （4）促进公司全体员工恪守职业操守，正直诚信，廉洁自律，勤勉尽责。\r\n 2、内部控制制度遵循的原则\r\n （1）合法合规性原则：公司内部控制制度应当符合国家法律法规和各项规定。\r\n （2）全面性原则： 内部控制制度应当覆盖公司的所有部门和岗位， 渗透\r\n 各项业务过程和业务环节，并普遍适用于公司每一位员工。\r\n （3）有效性原则：通过科学的内控手段和方法，建立合理的内控程序，维\r\n 护内控制度的有效执行，内部控制制度应当具有高度的权威性，任何员工不得拥有超越制度约束的权利。\r\n （4）独立性原则：公司在精简的基础上设立能够充分满足公司经营运作需\r\n 要的机构、部门和岗位，各机构、部门和岗位职责应当保持相对独立。公司固有财产、基金财产和其他财产的运作应当分离。\r\n （5）相互制约原则：公司内部机构、部门和岗位的设置应当权责分明、相互制衡。\r\n （6）防火墙原则：公司基金投资、交易、研究策划、市场开发等相关部门，应当在空间上和制度上适当分离，以达到风险防范的目的。对因业务需要知悉内幕信息的人员，应当制定严格的审批程序和监管措施。\r\n （7）审慎性原则：制定内部控制制度应当以审慎经营、防范和化解风险为出发点。\r\n （8）适时性原则：内部控制制度的制定应当随着有关法律法规的调整和公\r\n 司经营战略、经营方针、经营理念等内外部环境的变化进行及时的修改或完善。\r\n （9）成本效益原则：公司运用科学化的经营管理方法降低运作成本，提高经济效益，以合理的控制成本达到最佳的内部控制效果。\r\n （10）保持与业务发展的同等地位原则：公司的发展必须建立在风险控制制\r\n 度完善和稳固的基础上，内部风险控制应与公司业务发展放在同等地位上。\r\n （11）定性和定量相结合原则： 建立完备风险控制指标体系， 使风险控制更具客观性和操作性。\r\n 3、内部风险控制体系结构\r\n 公司内控体系由以下不同层面构成。公司董事会、经营管理层（包括督察长）、内部控制委员会、监察稽核部及公司其他部门、各岗位在各自职责范围内承担风险控制责任。\r\n （1）董事会：全面负责公司的风险控制工作，对建立内部控制系统和维持其有效性承担最终责任；\r\n （2）经营管理层：负责日常经营管理中的风险控制工作，对内部控制制度\r\n 的有效执行承担责任；督察长：负责对公司内部管理、资产运作以及经营管理层、内部各部门、各岗位执行制度及遵纪守法情况进行监督和检查，并对公司内部风险控制制度的合法性、合规性、合理性进行评价；\r\n （3）内部控制委员会：协助经营管理层负责公司风险控制工作，主要负责\r\n 对公司在经营管理和基金运作中的风险进行评估并研究制定相应的控制制度，协调处理突发性重大事件或危机事件。内部控制委员会由公司总经理、副总经理、监察稽核部总监、基金事务部总监、市场开发部总监和部分从事内部控制方面的业务骨干组成；\r\n （4）监察稽核部：负责检查评价公司内部控制制度的合法性、合规性、完\r\n 备性、有效性以及执行情况；对公司经营业务和基金运作情况进行日常稽核；对各部门、各岗位、各项业务的风险控制情况实施全面的监督检查，并及时报告检查结果。监察稽核部独立行使检查权并对经营管理层负责；\r\n （5）业务部门和公司各岗位：公司业务部门根据公司各项基本管理制度，结合部门具体情况制定本部门的管理办法和实施细则，加强对各项业务和各业务环节的风险控制；公司各岗位：根据岗位职责和业务操作流程，按业务授权规范操作，严格控制操作风险。\r\n 、内部控制制度体系\r\n 公司制定了合理、完备、有效并易于执行的内部风险控制制度体系。公司制度体系由不同层面的制度构成，按照其效力大小分为四个层面：\r\n 第一个层面是公司章程。\r\n 第二个层面是公司内部控制大纲，它是公司自定各项规章制度的基础和依据，内部控制大纲明确了内控目标、内控原则、控制环境、内控措施等内容。\r\n 第三个层面是公司基本管理制度，它包括风险控制制度、投资管理制度、基\r\n 金会计制度、信息披露制度、监察稽核制度、信息技术管理制度、公司财务制度、资料档案管理制度、人力资源制度、业绩评估考核制度和危机处理制度，等等。\r\n 第四个层面是公司各部门业务规章、实施细则等。部门业务规章、实施细则\r\n 是在基本管理制度的基础上，对各部门的主要职责、岗位设置、岗位责任、操作守则等的具体说明。\r\n 公司重视对制度的持续检验，结合业务的发展、法规及监管环境的变化以及公司风险控制的要求，不断检讨和增强公司制度的完备性、有效性。\r\n 5、内部风险管理体系\r\n 基金在运作过程中面临的风险主要包括市场风险、信用风险、流动性风险、操作风险、合规性风险以及不可抗力风险。\r\n 针对上述各种风险，基金管理人建立了一套完整的风险管理程序，具体包括以下内容：\r\n （1）投资风险管理；\r\n （2）交易风险管理；\r\n （3）巨额赎回风险管理；\r\n （4）基金注册登记风险管理；\r\n （5）基金核算风险管理；\r\n （6）市场开发风险管理；\r\n （7）信息披露风险管理；\r\n （8）不可抗力风险管理。\r\n 6、风险管理和内部控制的措施\r\n （1）建立内控结构，完善内控制度：建立、健全了行之有效的内控制度，确保各项业务活动都有适当的授权和明确的分工，确保监察稽核活动的独立性、权威性；\r\n （2）建立相互分离、相互制衡的内控机制：建立了明确的岗位分离制度，做到研究、决策分开，基金交易集中，形成不同部门，不同岗位之间的制衡机制，同时进行空间隔离，建立防火墙，充分保证信息的隔离和保密，从制度上降低和防范风险；\r\n （3）建立、健全岗位责任制：建立、健全了岗位责任制，使每位员工都明\r\n 确自己的任务、职责，及时上报各自工作领域中发现的风险隐患，以防范和化解风险；\r\n （4）建立风险分类、识别、评估、报告、提示程序：建立了内部控制委员会，使用适合的程序和方法，确认和评估公司经营管理和基金运作中的风险；建立自下而上的风险报告程序，对风险隐患进行层层汇报，使各个层次的人员及时掌握风险状况，从而以最快速度做出决策，减少风险造成的损失；\r\n （5）建立有效的内部监控系统：建立了足够、有效的内部监控系统，如计\r\n 算机预警系统、投资监控系统，能对可能出现的各种风险进行全面和实时的监控；\r\n （6）使用数量化的风险管理手段：采取数量化、技术化的风险控制手段，建立数量化的风险管理模型，用以提示指数趋势、行业及个股的风险，以便公司及时采取有效的措施，对风险进行分散、控制和规避，尽可能地减少损失；\r\n （7）提供足够的培训：制定了完整的培训计划，为所有员工提供足够和适当的培训，不断提高员工素质和职业技能，防范和化解风险。\r\n 7、基金管理人关于内部合规控制声明书\r\n （1）基金管理人承诺以上关于内部控制的披露真实、准确；\r\n （2）基金管理人承诺根据市场变化和公司发展不断完善内部控制。\r\n 四、基金托管人\r\n （一）基金托管人情况\r\n 1、基本情况\r\n 名称：中国建设银行股份有限公司(简称：中国建设银行)\r\n 住所：北京市西城区金融大街 25 号\r\n 办公地址：北京市西城区闹市口大街 1 号院 1 号楼\r\n 法定代表人：王洪章\r\n 成立时间：2004 年 09 月 17 日\r\n 组织形式：股份有限公司\r\n 注册资本：贰仟伍佰亿壹仟零玖拾柒万柒仟肆佰捌拾陆元整\r\n 存续期间：持续经营\r\n 基金托管资格批文及文号：中国证监会证监基字[1998]12 号\r\n 联系人：田青\r\n 联系电话：(010) 6759 5096中国建设银行股份有限公司拥有悠久的经营历史，其前身“中国人民建设银行”于 1954 年成立，1996 年易名为“中国建设银行”。中国建设银行是中国四大商业银行之一。中国建设银行股份有限公司由原中国建设银行于 2004 年 9 月分立而成立，承继了原中国建设银行的商业银行业务及相关的资产和负债。中国建设银行(股票代码：939)于 2005 年 10 月 27 日在香港联合交易所主板上市，是\r\n 中国四大商业银行中首家在海外公开上市的银行。2006 年 9 月 11 日，中国建设\r\n 银行又作为第一家 H 股公司晋身恒生指数。2007 年 9 月 25 日中国建设银行 A 股在上海证券交易所上市并开始交易。A 股发行后中国建设银行的已发行股份总数\r\n 为：250，010，977，486 股(包括 240，417，319，880 股 H 股及 9，593，657，606 股 A股)。\r\n 截至 2012 年 12 月 31 日，中国建设银行资产总额 139，728.28 亿元，较上\r\n 年末增长 13.77%。2012 年，中国建设银行实现净利润 1，936.02 亿元，较上年同期增长 14.26%。年化资产回报率为 1.47%，年化加权净资产收益率为 21.98%。\r\n 利息净收入 3532.02 亿元，较上年同期增长 15.97%。净利差为 2.58%，净利息收益率为 2.75%，均较上年同期提高 0.01 个百分点。手续费及佣金净收入 962.18亿元，较上年同期增长 7.51%。\r\n 中国建设银行在中国内地设有 1.4 万余个分支机构，并在香港、新加坡、法兰克福、约翰内斯堡、东京、首尔、纽约、胡志明市、悉尼、墨尔本设有分行，在莫斯科、台北设有代表处，海外机构已覆盖到全球 13 个国家和地区，基本完成在全球主要金融中心的网络布局，24 小时不间断服务能力和基本服务架构已初步形成。中国建设银行筹建、设立村镇银行 26 家，拥有建行亚洲、建银国际，建行伦敦、建信基金、建信金融租赁、建信信托、建信人寿、中德住房储蓄银行等多家子公司，为客户提供一体化全面金融服务能力进一步增强。\r\n 2012 年，本集团各方面良好表现，得到市场与社会各界广泛认可，先后荣\r\n 获国内外知名机构授予的 30 多个重要奖项。在英国《银行家》杂志联合 Brand\r\n Finance 发布的“世界银行品牌 500 强”以及 Interbrand 发布的“2012 年度中国最佳品牌”中，位列中国银行业首位；在美国《财富》杂志“世界 500 强排名”中列第 77 位，较上年上升 31 位。此外，本集团还荣获了国内外重要机构授予的包括公司治理、中小企业服务、私人银行、现金管理、托管、投行、投资者关系和企业社会责任等领域的多个专项奖。\r\n 中国建设银行总行设投资托管业务部，下设综合处、基金市场处、证券保险资产市场处、理财信托股权市场处、QFII 托管处、核算处、清算处、监督稽核处、涉外资产核算团队、养老金托管处、托管业务系统规划与管理团队、上海备份中心等 12 个职能处室、团队，现有员工 210 人。自 2007 年起，托管部连续聘请外部会计师事务所对托管业务进行内部控制审计，并已经成为常规化的内控工作手段。\r\n 2、主要人员情况杨新丰，投资托管业务部总经理，曾就职于中国建设银行江苏省分行、广东省分行、中国建设银行总行会计部、营运管理部，长期从事计划财务、会计结算、营运管理等工作，具有丰富的客户服务和业务管理经验。\r\n 纪伟，投资托管业务部副总经理，曾就职于中国建设银行南通分行、中国建设银行总行计划财务部、信贷经营部、公司业务部，长期从事大客户的客户管理及服务工作，具有丰富的客户服务和业务管理经验。\r\n 张军红，投资托管业务部副总经理，曾就职于中国建设银行青岛分行、中国建设银行零售业务部、个人银行业务部、行长办公室，长期从事零售业务和个人存款业务管理等工作，具有丰富的客户服务和业务管理经验。\r\n 郑绍平，投资托管业务部副总经理，曾就职于中国建设银行总行投资部、委托代理部，长期从事客户服务、信贷业务管理等工作，具有丰富的客户服务和业务管理经验。\r\n 3、基金托管业务经营情况\r\n 作为国内首批开办证券投资基金托管业务的商业银行，中国建设银行一直秉持“以客户为中心”的经营理念，不断加强风险管理和内部控制，严格履行托管人的各项职责，切实维护资产持有人的合法权益，为资产委托人提供高质量的托管服务。经过多年稳步发展，中国建设银行托管资产规模不断扩大，托管业务品种不断增加，已形成包括证券投资基金、社保基金、保险资金、基本养老个人账\r\n 户、QFII、企业年金等产品在内的托管业务体系，是目前国内托管业务品种最齐\r\n 全的商业银行之一。截至 2012 年 12 月 31 日，中国建设银行已托管 285 只证券投资基金。建设银行专业高效的托管服务能力和业务水平，赢得了业内的高度认同。中国建设银行在 2005 年及自 2009 年起连续四年被国际权威杂志《全球托管人》评为“中国最佳托管银行”，在 2007 年及 2008 年连续被《财资》杂志评为“国内最佳托管银行”奖，并获和讯网 2011 年度和 2012 年度中国“最佳资产托管银行”奖，和境内权威经济媒体《每日经济观察》2012 年度“最佳基金托管银行”奖。\r\n （二）基金托管人的内部控制制度\r\n （一）内部控制目标\r\n 作为基金托管人，中国建设银行严格遵守国家有关托管业务的法律法规、行业监管规章和本行内有关管理规定，守法经营、规范运作、严格监察，确保业务的稳健运行，保证基金财产的安全完整，确保有关信息的真实、准确、完整、及时，保护基金份额持有人的合法权益。\r\n （二）内部控制组织结构\r\n 中国建设银行设有风险与内控管理委员会，负责全行风险管理与内部控制工作，对托管业务风险控制工作进行检查指导。投资托管业务部专门设置了监督稽核处，配备了专职内控监督人员负责托管业务的内控监督工作，具有独立行使监督稽核工作职权和能力。\r\n （三）内部控制制度及措施\r\n 投资托管业务部具备系统、完善的制度控制体系，建立了管理制度、控制制度、岗位职责、业务操作流程，可以保证托管业务的规范操作和顺利进行；业务人员具备从业资格；业务管理严格实行复核、审核、检查制度，授权工作实行集中控制，业务印章按规程保管、存放、使用，账户资料严格保管，制约机制严格有效；业务操作区专门设置，封闭管理，实施音像监控；业务信息由专职信息披露人负责，防止泄密；业务实现自动化操作，防止人为事故的发生，技术系统完整、独立。\r\n （三）基金托管人对基金管理人运作基金进行监督的方法和程序\r\n （一）监督方法\r\n 依照《基金法》及其配套法规和基金合同的约定，监督所托管基金的投资运作。利用自行开发的“托管业务综合系统——基金监督子系统”，严格按照现行法律法规以及基金合同规定，对基金管理人运作基金的投资比例、投资范围、投资组合等情况进行监督，并定期编写基金投资运作监督报告，报送中国证监会。\r\n 在日常为基金投资运作所提供的基金清算和核算服务环节中，对基金管理人发送的投资指令、基金管理人对各基金费用的提取与开支情况进行检查监督。\r\n （二）监督流程\r\n 1.每工作日按时通过基金监督子系统，对各基金投资运作比例控制指标进行例行监控，发现投资比例超标等异常情况，向基金管理人发出书面通知，与基金管理人进行情况核实，督促其纠正，并及时报告中国证监会。\r\n 2.收到基金管理人的划款指令后，对涉及各基金的投资范围、投资对象及交易对手等内容进行合法合规性监督。\r\n 3.根据基金投资运作监督情况，定期编写基金投资运作监督报告，对各基金投资运作的合法合规性、投资独立性和风格显著性等方面进行评价，报送中国证监会。\r\n 4.通过技术或非技术手段发现基金涉嫌违规交易，电话或书面要求基金管理\r\n 人进行解释或举证，并及时报告中国证监会。\r\n 五、相关服务机构\r\n （一）基金份额发售机构\r\n 1、直销中心：长信基金管理有限责任公司\r\n 注册地址：上海市浦东新区银城中路 68号时代金融中心九楼\r\n 办公地址：上海市浦东新区银城中路 68号时代金融中心九楼\r\n 法定代表人：田丹 联系人：计磊\r\n 电话：021-61009918 传真：021-61009917\r\n 客户服务电话：400-700-5566 网站：www.cxfund.com.cn\r\n 2、场外代销机构\r\n （1）中国邮政储蓄银行股份有限公司\r\n 注册地址：北京市西城区金融大街 3号\r\n 办公地址：北京市西城区金融大街 3号\r\n 法定代表人：李国华 传真：010-68858117\r\n 客户服务电话：95580 网址：www.psbc.com\r\n （2）中国农业银行股份有限公司\r\n 注册地址：北京市东城区建国门内大街 69 号\r\n 办公地址：北京市东城区建国门内大街 69 号\r\n 法定代表人：蒋超良 联系人：刘一宁\r\n 电话：010-85108227 传真：010-85109219\r\n 客户服务电话：95599 网址：www.abchina.com\r\n （3）中国工商银行股份有限公司\r\n 注册地址：中国北京复兴门内大街 55 号\r\n 办公地址：中国北京复兴门内大街 55 号\r\n 法定代表人：姜建清\r\n 客户服务电话：95588 网址：www.icbc.com.cn\r\n （4）中国银行股份有限公司\r\n 注册地址：北京市西城区复兴门内大街 1号\r\n 办公地址：北京市西城区复兴门内大街 1号\r\n 法定代表人：肖钢\r\n 客户服务电话：95566 网址：www.boc.cn\r\n （5）中国建设银行股份有限公司\r\n 注册地址：北京市西城区金融大街 25 号\r\n 办公地址：北京市西城区闹市口大街 1 号院 1号楼\r\n 法定代表人：王洪章 联系人：张静\r\n 客户服务电话:95533 网址：www.ccb.com（6）交通银行股份有限公司\r\n 注册地址：上海市银城中路 188 号\r\n 办公地址：上海市银城中路 188 号\r\n 法定代表人：胡怀邦 联系人：曹榕\r\n 电话：021-58781234 传真：021-58408483\r\n 客户服务电话：95559 网址：www.bankcomm.com\r\n （7）招商银行股份有限公司\r\n 注册地址：深圳市深南大道 7088 号招商银行大厦\r\n 办公地址：深圳市深南大道 7088 号招商银行大厦\r\n 法定代表人：傅育宁 联系人：邓炯鹏\r\n 电话：0755-83198888 传真：0755-83195109\r\n 客户服务电话：95555 网址：www.cmbchina.com\r\n （8）上海浦东发展银行股份有限公司\r\n 注册地址：上海市浦东新区浦东南路 500 号\r\n 办公地址：上海市中山东一路 12 号\r\n 法定代表人：吉晓辉 联系人：唐苑\r\n 电话：021-61618888 传真：021-63604199\r\n 客户服务电话：95528 网址：www.spdb.com.cn\r\n （9）中国民生银行股份有限公司\r\n 注册地址：北京市西城区复兴门内大街 2号\r\n 办公地址：北京市西城区复兴门内大街 2号\r\n 法定代表人：董文标 联系人：董云巍\r\n 电话：010-58560666 传真：010-57092611\r\n 客户服务电话：95568 网址：www.cmbc.com.cn\r\n （10）中信银行股份有限公司\r\n 注册地址：北京东城区朝阳门北大街 8 号富华大厦 C 座\r\n 办公地址：北京东城区朝阳门北大街 8 号富华大厦 C 座\r\n 法定代表人：田国立\r\n 电话：010-65558888 联系人：丰靖\r\n 客户服务电话：95558 网址：bank.ecitic.com\r\n （11）华夏银行股份有限公司\r\n 注册地址：北京市东城区建国门内大街 22 号华夏银行大厦\r\n 办公地址：北京市东城区建国门内大街 22 号华夏银行大厦\r\n 法定代表人：吴建 联系人：郑鹏\r\n 电话：010-85238667 传真：010-85238680\r\n 客户服务电话：010-95577 网址：www.hxb.com.cn（12）汉口银行股份有限公司\r\n 注册地址：武汉市江汉区建设大道 933 号武汉商业银行大厦\r\n 办公地址：武汉市江汉区建设大道 933 号\r\n 法定代表人：陈新民 联系人：骆芸\r\n 电话：027-82656224 传真：027-82656236\r\n 客户服务电话：（027）96558，4006096558 网址：www.hkbchina.com\r\n （13）杭州银行股份有限公司\r\n 注册地址：杭州银行庆春路 46号杭州银行大厦\r\n 办公地址：杭州银行庆春路 46号杭州银行大厦\r\n 法定代表人：吴太普 联系人：严峻\r\n 电话：0571-85108195 传真：0571-85106576\r\n 客户服务电话：96523，400-8888-508 网址：www.hzbank.com.cn\r\n （14）平安银行股份有限公司\r\n 注册地址：深圳市深南中路 1099 号平安银行大厦\r\n 办公地址：深圳市深南中路 1099 号平安银行大厦\r\n 法定代表人：孙建一 联系人：张青\r\n 电话：0755-22166118 传真：0755-25841098\r\n 客户服务电话：95511-3 网址：bank.pingan.com\r\n （15）厦门银行股份有限公司\r\n 注册地址：福建省厦门市思明区湖滨北路 101 号商业银行大厦\r\n 办公地址：福建省厦门市思明区湖滨北路 101 号商业银行大厦\r\n 法定代表人：吴世群 联系人：杨明艳\r\n 电话：0592-5365735 传真：0592-5061952\r\n 客户服务电话：400-858-8888 网址：www.xmbankonline.com\r\n （16）重庆银行股份有限公司\r\n 注册地址：重庆市渝中区邹容路 153 号\r\n 办公地址：重庆市渝中区邹容路 153 号\r\n 法定代表人：马千真 联系人：孔文超\r\n 电话：023-63799379 传真：023-63792412\r\n 客户服务电话：(023)63836229 网址：www.cqcbank.com\r\n （17）安信证券股份有限公司\r\n 注册地址：深圳市福田区金田路 4018 号安联大厦 35 层、28层 A02 单元办公地址：深圳市福田区金田路 4018 号安联大厦 35 层、28层 A02 单元法定代表人：牛冠兴 联系人：陈剑虹\r\n 电话：0755-82558305 传真：0755-28558355\r\n 客户服务电话：4008-001-001 网址：www.essence.com.cn（18）长城证券有限责任公司\r\n 注册地址：深圳市深南大道 6008 号特区报业大厦 14、16、17 层办公地址：深圳市深南大道 6008 号特区报业大厦 14、16、17 层法定代表人：黄耀华 联系人：刘阳\r\n 电话：0755-83516289 传真：0755-83515567\r\n 客户服务电话：400-6666-888 网址：www.cgws.com\r\n （19）长江证券股份有限公司\r\n 注册地址：武汉市新华路特 8 号长江证券大厦\r\n 办公地址：武汉市新华路特 8 号长江证券大厦\r\n 法定代表人：胡运钊 联系人：李良\r\n 电话：027-65799999 传真：027-85481900\r\n 客户服务电话：95579 或 4008-888-999 网址：www.95579.com\r\n （20）大通证券股份有限公司\r\n 注册地址：辽宁省大连市中山区人民路 24 号\r\n 办公地址：辽宁省大连市沙河口区会展路 129 号大连期货大厦 39层\r\n 法定代表人：张智河 联系人：谢立军\r\n 电话：0411-39673202 传真：0411-39673219\r\n 客户服务电话：4008-169-169 网址：www.daton.com.cn\r\n （21）德邦证券有限责任公司\r\n 注册地址：上海市普陀区曹阳路 510 号南半幢 9 楼\r\n 办公地址：上海市福山路 500 号城建国际中心 26楼\r\n 法定代表人：姚文平 联系人：叶蕾\r\n 电话：021-68761616 传真：021-68767032\r\n 客户服务电话：400-8888-128 网址：www.tebon.com.cn\r\n （22）第一创业证券股份有限公司\r\n 注册地址：深圳市罗湖区笋岗路 12号中民时代广场 B座 25、26 层办公地址：深圳市罗湖区笋岗路 12号中民时代广场 B座 25、26 层法定代表人：刘学民 联系人：崔国良\r\n 电话：0755-25832852 传真：0755-82485081\r\n 客户服务电话：400-888-1888 网址：www.firstcapital.com.cn\r\n （23）东方证券股份有限公司\r\n 注册地址：上海市中山南路 318 号新源广场 2号楼 22-29 层\r\n 办公地址：上海市中山南路 318 号新源广场 2号楼 21-29 层\r\n 法定代表人：潘鑫军 联系人：吴宇\r\n 电话：021-63325888 传真：021-63326173\r\n 客户服务电话：95503 网址：www.dfzq.com.cn（24）东莞证券有限责任公司\r\n 注册地址：东莞市莞城区可园南路 1号\r\n 办公地址：东莞市莞城区可园南路 1号金源中心 30楼\r\n 法定代表人：张运勇 联系人：苏卓仁\r\n 电话：0769-22112062 传真：0769-22119423\r\n 客户服务电话：0769-961130 网址：www.dgzq.com.cn\r\n （25）东吴证券股份有限公司\r\n 注册地址：苏州工业园区翠园路 181 号商旅大厦\r\n 办公地址：苏州工业园区翠园路 181 号商旅大厦\r\n 法定代表人：吴永敏 联系人：方晓丹\r\n 电话：0512-65581136 传真：0512-65588021\r\n 客户服务电话：0512-33396288 网址：www.dwzq.com.cn\r\n （26）光大证券股份有限公司\r\n 注册地址：上海市静安区新闸路 1508 号\r\n 办公地址：上海市静安区新闸路 1508 号\r\n 法定代表人：徐浩明 联系人：刘晨\r\n 电话：021-22169999 传真：021-22169134\r\n 客户服务电话：4008888788、10108998、95525 网址：www.ebscn.com\r\n （27）华福证券有限责任公司\r\n 注册地址：福州市五四路 157 号新天地大厦 7、8层办公地址：福州市五四路 157 号新天地大厦 7至 10 层\r\n 法定代表人：黄金琳 联系人：张腾\r\n 电话：0591-87383623 传真：0591-87383610\r\n 客户服务电话：96326（福建省外请先拨 0591） 网址：www.hfzq.com.cn\r\n （28）广发证券股份有限公司\r\n 注册地址：广州市天河北路 183 号大都会广场 43楼\r\n 办公地址：广州市天河北路 183 号大都会广场 43楼\r\n 法定代表人：孙树明 联系人：黄岚\r\n 电话：020-87555888 转各营业网点 传真：020-87557985\r\n 客户服务电话：95575 或致电各地营业部 网址：www.gf.com.cn\r\n （29）国都证券有限责任公司\r\n 注册地址：北京市东城区东直门南大街 3号国华投资大厦 9层 10 层\r\n 办公地址：北京市东城区东直门南大街 3号国华投资大厦 9层 10 层\r\n 法定代表人：常喆 联系人：黄静\r\n 电话：010-84183389 传真：010-84183311-3389\r\n 客户服务电话：400-818-8118 网址：www.guodu.com（30）国金证券股份有限公司\r\n 注册地址：成都市东城根上街 95 号\r\n 办公地址：成都市东城根上街 95 号\r\n 法定代表人：冉云\r\n 电话：028-86690126 传真：028-86690126\r\n 客户服务电话：4006600109 网址：www.gjzq.com.cn\r\n （31）国联证券股份有限公司\r\n 注册地址：江苏省无锡市县前东街 168 号\r\n 办公地址：江苏省无锡市太湖新城金融一街 8号国联金融大厦 702\r\n 法定代表人：雷建辉 联系人：沈刚\r\n 电话：0510-82831662 传真：0510-82830162\r\n 客户服务电话：95570 网址：www.glsc.com.cn\r\n （32）国盛证券有限责任公司\r\n 注册地址：南昌市北京西路 88号（江信国际金融大厦）\r\n 办公地址：南昌市北京西路 88号（江信国际金融大厦）\r\n 法定代表人：曾小普 联系人：吴祖平\r\n 电话：0791-6281061 传真：0791-6281061\r\n 客户服务电话：4008-222-111 网址：www.gsstock.com\r\n （33）国泰君安证券股份有限公司\r\n 注册地址：上海市浦东新区商城路 618 号\r\n 办公地址：上海市浦东新区银城中路 168 号上海银行大厦 29楼\r\n 法人代表人：万建华 联系人：芮敏祺\r\n 电话：021-38676666 传真：021-38670666\r\n 客户服务热线：4008888666 网址：www.gtja.com\r\n （34）国信证券股份有限公司\r\n 注册地址：深圳市罗湖区红岭中路 1012 号国信证券大厦十六层至二十六层\r\n 办公地址：深圳市罗湖区红岭中路 1012 号国信证券大厦十六层至二十六层\r\n 法定代表人：何如 联系人：齐晓燕\r\n 电话：0755-82130833 传真：0755-82133952\r\n 客户服务电话：95536 网址：www.guosen.com\r\n （35）国元证券股份有限公司\r\n 注册地址：安徽省合肥市寿春路 179 号\r\n 办公地址：安徽省合肥市寿春路 179 号\r\n 法定代表人：凤良志 联系人：陈琳琳\r\n 电话：05551-2615542 传真：0551-2634906\r\n 客户服务电话：955784008-888-777 网址：www.gyzq.com.cn（36）海通证券股份有限公司\r\n 注册地址：上海市淮海中路 98号\r\n 办公地址：上海市广东路 689 号海通证券大厦\r\n 法定代表人：王开国 联系人：李笑鸣\r\n 电话：021-23219000 传真：021-23219100\r\n 客户服务电话：400-8888-001 或 95553 网址：www.htsec.com\r\n （37）宏源证券股份有限公司\r\n 注册地址：新疆乌鲁木齐市文艺路 233 号宏源大厦\r\n 办公地址：北京市西城区太平桥大街 19号宏源证券\r\n 法定代表人：冯戎 联系人：李巍\r\n 电话：010-88085858 传真：010-88085195\r\n 客户服务电话：4008-000-562 网址：www.hysec.com\r\n （38）华安证券有限责任公司\r\n 注册地址：安徽省合肥市长江中路 357 号\r\n 办公地址：合肥市蜀山区南二环 959 号 B1 座华安证券\r\n 法定代表人：李工 联系人：甘霖\r\n 电话：0551-5161666 传真：0551-5161600\r\n 客户服务电话：96518、400-80-96518 网址：www.hazq.com\r\n （39）华宝证券有限责任公司\r\n 注册地址：上海市浦东新区世纪大道 100 号 57 层\r\n 办公地址：上海市浦东新区世纪大道 100 号 57 层\r\n 法定代表人：陈林 联系人：赵洁\r\n 电话：021-68777222 传真：021-68777822\r\n 客户服务电话：400-820-9898 网址：www.cnhbstock.com\r\n （40）华龙证券有限责任公司\r\n 注册地址：甘肃省兰州市城关区东岗西路 638 号财富大厦 21楼\r\n 办公地址：甘肃省兰州市城关区东岗西路 638 号财富大厦\r\n 法定代表人：李晓安 联系人：李昕田\r\n 电话：0931-8784656 传真：0931-4890118\r\n 客户服务电话：4006898888 网址：www.hlzqgs.com.cn\r\n （41）华泰联合证券有限责任公司\r\n 注册地址：深圳市福田区中心区中心广场香港中旅大厦第五层\r\n 办公地址：深圳市福田中心区中心广场香港中旅大厦第 5层、17层、18 层、24 层、25层、\r\n 26 层\r\n 法定代表人：马昭明 联系人：盛宗凌、庞晓芸\r\n 电话：0755-82492000 传真：0755-82492962客户服务电话：95513 网址：www.lhzq.com\r\n （42）华泰证券股份有限公司\r\n 注册地址：南京市中山东路 90号华泰证券大厦\r\n 办公地址：南京市中山东路 90号华泰证券大厦\r\n 法定代表人：吴万善 联系人：万鸣\r\n 电话：025-83290979 传真：025-84579763\r\n 客户服务电话：95597 网址：www.htsc.com.cn\r\n （43）江海证券有限公司\r\n 注册地址：黑龙江省哈尔滨市香坊区赣水路 56 号\r\n 办公地址：黑龙江省哈尔滨市香坊区赣水路 56 号\r\n 法定代表人：孙名扬 联系人：张宇宏\r\n 电话：0451-82336863 传真：0451-82287211\r\n 客户服务电话：400-666-2288 网址：www.jhzq.com.cn\r\n （44）中航证券有限公司\r\n 注册地址：南昌市红谷滩新区红谷中大道 1619 号国际金融大厦 A座 41 楼\r\n 办公地址：南昌市红谷滩新区红谷中大道 1619 号国际金融大厦 A座 41 楼\r\n 法定代表人：杜航 联系人：戴蕾\r\n 电话：0791-86768681 传真：0791-86770178\r\n 客户服务电话：400-8866-567 网址：www.avicsec.com\r\n （45）齐鲁证券有限公司\r\n 注册地址：山东省济南市市中区经七路 86 号\r\n 办公地址：山东省济南市市中区经七路 86 号\r\n 法定代表人：李玮 联系人：吴阳\r\n 电话：0531-68889155 传真：0531-68889752\r\n 客户服务电话：95538 网址：www.qlzq.com.cn\r\n （46）上海证券有限责任公司\r\n 注册地址：上海市西藏中路 336 号上海证券\r\n 办公地址：上海市西藏中路 336 号\r\n 法定代表人：郁忠民 联系人：张瑾\r\n 电话：021-53519888 传真：021-53519888\r\n 客户服务电话：4008918918、021-962518 网址：www.962518.com\r\n （47）申银万国证券股份有限公司\r\n 注册地址：上海市常熟路 171 号\r\n 办公地址：上海市常熟路 171 号\r\n 法定代表人：储晓明 联系人：高夫\r\n 电话：021-54047673 传真：021-54038844客户服务电话：021—95523 网址：www.sywg.com.cn\r\n （48）天风证券股份有限公司\r\n 注册地址:中国湖北省武汉市东湖新技术开发区关东园路 2号高科大厦四楼\r\n 办公地址：中国湖北省武汉市东湖新技术开发区关东园路 2 号高科大厦四楼\r\n 法定代表人：余磊 联系人：翟璟\r\n 电话：027-87618882 传真：027-87618863\r\n 客服电话：028-86711410 网址：www.tfzq.com\r\n （49）天相投资顾问有限公司\r\n 注册地址：北京市西城区金融街 19号富凯大厦 B座 701\r\n 办公地址：北京市西城区金融街 5 号新盛大厦 B座 4 层\r\n 法定代表人：林义相 联系人：林爽\r\n 电话：010-66045608 传真：010-66045527\r\n 客户服务电话：010-66045678 网址：www.txsec.com\r\n （50）天源证券有限公司\r\n 注册地址：西宁市城中区西大街 11号\r\n 办公地址：深圳市福田区民田路新华保险大厦 18楼\r\n 法定代表人：林小明 联系人：关键\r\n 电话：0755-33331188-8805 传真：0755-33329815\r\n 客户服务电话：400-65432-18 网址：www.tyzq.com.cn\r\n （51）西藏同信证券有限责任公司\r\n 注册地址：拉萨市北京中路 101 号\r\n 办公地址：上海市永和路 118 弄东方企业园 24 号\r\n 法定代表人：贾绍君 联系人：王钧\r\n 电话：021-36533017 传真：021-36533017\r\n 客户服务电话：400-8811-177 网址：www.xzsec.com\r\n （52）湘财证券有限责任公司\r\n 注册地址：湖南省长沙市天心区湘府中路 198 号标志商务中心 11楼\r\n 办公地址：湖南省长沙市天心区湘府中路 198 号标志商务中心 11楼\r\n 法定代表人：林俊波 联系人：钟康莺\r\n 电话：021-68634518 传真：021-68865680\r\n 客户服务电话：400-888-1551 网址：www.xcsc.com\r\n （53）日信证券有限责任公司\r\n 注册地址：呼和浩特市新城区锡林南路 18 号\r\n 办公地址：北京西城区闹市口大街 1号长安兴融中心西楼 11层\r\n 法定代表人：孔佑杰 联系人：文思婷\r\n 电话：010-83991743 传真：010-66412537客户服务电话：010-83991888 网址：www.rxzq.com.cn\r\n （54）兴业证券股份有限公司\r\n 注册地址：福州市湖东路 268 号\r\n 办公地址：浦东新区民生路 1199 弄证大.五道口广场 1 号楼 21 层\r\n 法定代表人：兰荣 联系人：谢高得\r\n 电话：021-38565785\r\n 客户服务电话：400-8888-123 网址：www.xyzq.com.cn\r\n （55）招商证券股份有限公司\r\n 注册地址：深圳市福田区益田路江苏大厦 38-45 层\r\n 办公地址：广东省深圳市福田区益田路江苏大厦 38-45 层\r\n 法定代表人：宫少林 联系人：林生迎\r\n 电话：0755-82960223 传真：0755-82943121\r\n 客户服务电话：95565 或 400-8888-111 网址：www.newone.com.cn\r\n （56）中国民族证券有限责任公司\r\n 注册地址：北京市西城区金融街 5 号新盛大厦 A座 6-9 层\r\n 办公地址：北京市西城区金融街 5 号新盛大厦 A座 6-9 层\r\n 法定代表人：赵大建 联系人：姜建平\r\n 电话：010-59355543 传真：010-66553791\r\n 客户服务电话：40088-95618 网址：www.e5618.com\r\n （57）中国银河证券股份有限公司\r\n 注册地址：北京市西城区金融大街 35 号国际企业大厦 C 座\r\n 办公地址：北京市西城区金融大街 35 号国际企业大厦 C 座\r\n 法定代表人：陈有安 联系人：田薇\r\n 电话：010-66568888 传真：010-66568990\r\n 客户服务电话：4008-888-888 网址：www.chinastock.com.cn\r\n （58）中信建投证券股份有限公司\r\n 注册地址：北京市朝阳区安立路 66号 4号楼\r\n 办公地址:北京市朝阳门内大街 188 号\r\n 法定代表人：王常青 联系人：魏明\r\n 电话：010-85130588 传真：010-65182261\r\n 客户服务电话：400-8888-108 网址：www.csc108.com\r\n （59）中信证券（浙江）有限责任公司注册地址：浙江省杭州市滨江区江南大道 588 号恒鑫大厦主楼 19、20 层办公地址：浙江省杭州市滨江区江南大道 588 号恒鑫大厦主楼 19、20 层法定代表人：沈强 联系人：周妍\r\n 电话：0571-86078823 传真：0571-85783771客户服务电话：057196598 网址：www.bigsun.com.cn\r\n （60）中信证券股份有限公司\r\n 注册地址：广东省深圳市福田区中心三路 8 号卓越时代广场（二期）北座\r\n 办公地址：北京市朝阳区亮马桥路 48 号中信证券大厦\r\n 法定代表人：王东明 联系人：陈忠\r\n 电话：010-60838888 传真：010-60833739\r\n 客户服务电话：95558 网址：www.citics.com\r\n （61）中信万通证券有限责任公司\r\n 注册地址：青岛市崂山区苗岭路 29号澳柯玛大厦 15 层（1507-1510 室）\r\n 办公地址：青岛市崂山区深圳路 222 号青岛国际金融广场 1 号楼第 20层（266061）\r\n 法定代表人：张智河 联系人：吴忠超\r\n 电话：0532-85022326 传真：0532-85022605\r\n 客户服务电话：0532-96577 网址：www.zxwt.com.cn\r\n （62）中山证券有限责任公司\r\n 注册地址：深圳市福田区益田路 6009 号新世界中心 29层\r\n 办公地址：深圳市福田区益田路 6009 号新世界中心 29层\r\n 法定代表人：吴永良 联系人：刘军\r\n 电话：0755-83734659 传真：0755-82960582\r\n 客户服务电话：4001-022-011 网址：www.zszq.com.cn\r\n （63）杭州数米基金销售有限公司\r\n 注册地址：杭州市余杭区仓前街道海曙路东 2号\r\n 办公地址：浙江省杭州市滨江区江南大道 3588 号恒生大厦 12楼\r\n 法定代表人：陈柏青 联系人：周嬿旻\r\n 电话：0571-28829790，021-60897869 传真：0571-26698533\r\n 客户服务电话：4000-766-123 网址：www.fund123.cn\r\n （64）上海长量基金销售投资顾问有限公司\r\n 注册地址：上海市浦东新区高翔路 526 号 2幢 220 室\r\n 办公地址：上海市浦东新区浦东大道 555 号裕景国际 B座 16 层\r\n 法定代表人：张跃伟 联系人：吕慧\r\n 电话：021-58787678-8816 传真：021-58788678-8101\r\n 客户服务电话：400-089-1289 网址：www.erichfund.com\r\n （65）诺亚正行（上海）基金销售投资顾问有限公司注册地址：上海市金山区廊下镇漕廊公路 7650 号 205 室\r\n 办公地址：上海市浦东新区陆家嘴银城中路 68 号时代金融中心 8楼 801\r\n 法定代表人：汪静波 联系人：方成\r\n 电话：021-38602377 传真：021-38509777客户服务电话：400-821-5399 网址：www.noah-fund.com\r\n （66）上海好买基金销售有限公司\r\n 注册地址：上海市虹口区场中路 685 弄 37 号 4 号楼 449 室\r\n 办公地址：上海市浦东新区浦东南路 1118 号鄂尔多斯国际大厦 903-906 室\r\n 法定代表人：杨文斌\r\n 电话：021-58870011 传真：021-68596916\r\n 客户服务电话：4007009665 网址：www.ehowbuy.com\r\n （67）和讯信息科技有限公司\r\n 注册地址：北京市朝阳区朝外大街 22 号泛利大厦 10 层\r\n 办公地址：北京市朝阳区朝外大街 22 号泛利大厦 10 层\r\n 法定代表人：王莉 联系人：严跃俊\r\n 电话：021-68419822 传真：021-68419822-8566\r\n 客户服务电话：400-920-0022 网址：www.licaike.com\r\n 3、场内销售机构\r\n 场内代销机构是指有资格的上证所会员，名单详见上海证券交易所网站：\r\n http://www.sse.com.cn/sseportal/ps/zhs/hyzq/zxzg_szjjt.jsp\r\n 基金管理人可根据有关法律法规的要求，选择其他符合要求的机构代理销售本基金，并及时公告。\r\n （二）与本基金有关的注册登记机构、律师事务所、会计师事务所信息：\r\n 信息类型 注册登记机构 律师事务所 会计师事务所\r\n 名称 中国证券登记结算有限责任公司 上海源泰律师事务所 毕马威华振会计师事务所\r\n 注册地址 北京市西城区太平桥大街 17 号\r\n 上海市浦东南路 256 号\r\n 华夏银行大厦 1405 室\r\n 北京东长安街 1号东方广\r\n 场东 2座 8层\r\n 办公地址 北京市西城区太平桥大街 17 号\r\n 上海市浦东南路 256 号\r\n 华夏银行大厦 1405 室上海南京西路1266号恒隆\r\n 广场 50 楼\r\n 法定代表人 金颖 廖海（负责人） 姚建华\r\n 联系电话 010-59378835 021-51150298 021-22122888\r\n 传真 010-59378907 021-51150398 021-62881889\r\n 联系人 任瑞新 梁丽金、刘佳 王国蓓（王国蓓、戴丽为经办注册会计师）六、基金的募集\r\n 本基金由基金管理人依照有关法律法规和基金合同募集，基金募集申请经中国证监会 2009 年 5 月 27 日证监许可【2009】439 号文核准。\r\n 本基金募集期为 2009 年 6 月 26 日至 2009 年 7 月 24 日。经会计师事务所验资，按照每份基金份额面值人民币 1.00 元计算，基金募集期共募集\r\n 1，182，530，117.97 份基金份额，有效认购户数为 34，608 户。\r\n 七、基金合同的生效\r\n （一）根据《中华人民共和国证券投资基金法》、《证券投资基金运作管理办法》\r\n 以及《长信恒利优势股票型证券投资基金基金合同》（以下简称“基金合同”）、《长信恒利优势股票型证券投资基金招募说明书》的有关规定，本基金本次募集符合基金合同的生效条件，本基金管理人已向中国证监会办理完毕基金备案手续，并于 2009 年 7 月 30 日获得其书面确认，本基金基金合同自该日起正式生效。自基金合同生效之日起，本基金管理人正式开始管理本基金。\r\n （二）基金存续期内的基金份额持有人数量和资金数额\r\n 基金合同生效后的存续期内，基金份额持有人数量不满 200 人或者基金资产净值低于 5000 万元，基金管理人应当及时报告中国证监会；基金份额持有人数量连续 20 个工作日达不到 200 人，或连续 20 个工作日基金资产净值低于 5000万元，基金管理人应当向中国证监会说明出现上述情况的原因并提出解决方案。\r\n 法律法规或监管部门另有规定的，按其规定办理。\r\n 八、基金份额的申购与赎回\r\n （一）申购和赎回的场所本基金通过场内销售和场外销售两种方式公开发售。\r\n 场外销售渠道为基金管理人的直销中心和不通过上海证券交易所开放式基金销售系统办理相关业务的场外代销机构的代销网点（具体名单见基金份额发售公告及基金管理人的其他公告）。\r\n 场内销售渠道为通过上海证券交易所开放式基金销售系统办理相关业务的上海证券交易所会员单位（具体名单见基金份额发售公告及基金管理人的其他公告）。场内认购须遵守上海证券交易所相关规则。除法律法规另有规定外，任何与基金份额发售有关的当事人不得预留和提前发售基金份额。\r\n （二）申购和赎回的开放日及时间\r\n 1、开放日及开放时间\r\n 投资人在开放日申请办理基金份额的申购和赎回，具体办理时间为上海证券交易所的正常交易日的交易时间。场内申购、赎回业务的开放时间为 9:30-11:30和 13:00-15:00，场外申购、赎回业务的开放时间由基金管理人与销售机构约定，但基金管理人根据法律法规、中国证监会的要求或本基金合同的规定公告暂停申购、赎回时除外。\r\n 基金合同生效后，若出现新的证券交易市场、证券交易所交易时间变更或其他特殊情况，基金管理人将视情况对前述开放日及开放时间进行相应的调整并公告。\r\n 2、申购、赎回开始日及业务办理时间\r\n 基金管理人自基金合同生效之日起不超过 3 个月开始办理申购。\r\n 基金管理人自基金合同生效之日起不超过 3 个月开始办理赎回。\r\n 本基金（基金代码：519987 [前端模式]）于 2009 年 10 月 30 日开始办理日\r\n 常申购、赎回业务。\r\n 基金管理人尚未开放本基金[后端模式]日常申购、赎回业务，具体办理申购、赎回业务的时间基金管理人应提前 2 个工作日在指定媒体及基金管理人网站刊登相关公告。\r\n 基金管理人不得在基金合同约定之外的日期或者时间办理基金份额的申购、赎回或者转换。投资人在基金合同约定之外的日期和时间提出申购、赎回或转换申请的，其基金份额申购、赎回、转换价格为下一开放日基金份额申购、赎回的价格。\r\n （三）申购和赎回的原则\r\n 1、“未知价”原则，即申购、赎回价格以申请当日收市后计算的基金份额净值为基准进行计算；\r\n 2、“金额申购、份额赎回”原则，即申购以金额申请，赎回以份额申请；\r\n 3、赎回遵循“先进先出”原则，即按照投资人认购、申购的先后次序进行顺序赎回；\r\n 4、当日的申购与赎回申请可以在基金管理人规定的时间以内撤销。\r\n 基金管理人可根据基金运作的实际情况依法对上述原则进行调整。基金管理人必须在新规则开始实施前依照《信息披露办法》的有关规定在指定媒体上公告。\r\n （四）申购和赎回的程序\r\n 1、申购和赎回的申请方式\r\n 投资人必须根据销售机构规定的程序，在开放日的具体业务办理时间内提出申购或赎回的申请。\r\n 投资人在提交申购申请时须按销售机构规定的方式备足申购资金，投资人在提交赎回申请时须持有足够的基金份额余额，否则所提交的申购、赎回申请无效。\r\n 2、申购和赎回申请的确认基金管理人应以交易时间结束前受理申购和赎回申请的当天作为申购或赎\r\n 回申请日（T 日），在正常情况下，本基金注册登记机构在 T+1 日内对该交易的有效性进行确认。T 日提交的有效申请，投资人应在 T+2 日后（包括该日）到销售网点柜台或以销售机构规定的其他方式查询申请的确认情况。\r\n 3、申购和赎回的款项支付\r\n 申购采用全额缴款方式，若申购资金在规定时间内未全额到账则申购不成功。若申购不成功或无效，基金管理人或基金管理人指定的代销机构将投资人已缴付的申购款项退还给投资人。\r\n 投资人赎回申请成功后，基金管理人将在 T＋7 日（包括该日）内支付赎回款项。在发生巨额赎回时，款项的支付办法参照基金合同有关条款处理。\r\n （五）申购和赎回的金额\r\n 1、申购金额的限制场外申购时，代销网点每个账户单笔申购本基金的最低金额为 1，000 元（含申购费），若代销机构有关于交易级差规定的，以代销机构的规定为准。自 2012\r\n 年 5 月 3 日起，直销中心每个账户首次申购本基金的最低金额自 10 万元调整为\r\n 为 1 万元（含申购费），追加申购的最低金额为 1，000 元（含申购费）。已在直销中心有本基金认购记录的投资人不受首次申购最低金额的限制。在本公司网上直销系统可接受个人投资者单笔申购金额在 1，000 元人民币以上（含 1，000 元）的申购申请。具体申购限额以投资者指定结算机构的规定为准。代销网点的投资人欲转入直销网点进行交易要受直销网点最低金额的限制。投资人当期分配的基金收益转购基金份额时，不受最低申购金额的限制。基金管理人可根据市场情况，调整本基金首次申购的最低金额。\r\n 场内申购时，每笔申购金额最低为 1，000 元（含申购费），同时每笔申购必\r\n 须是 100 元的整数倍，并且单笔申购最高不超过 99，999，900 元（含申购费）。\r\n 2、赎回份额的限制\r\n 场外赎回时，每次对本基金的赎回申请不得低于 1.00 份基金份额。场内赎回时，赎回的最低份额为 1.00 份；\r\n 基金份额持有人赎回时或赎回后，导致每个交易帐户的基金份额余额不足\r\n 1.00 份时，在赎回时需一次全部赎回。\r\n 3、基金管理人可以根据市场情况，在法律法规允许的情况下，对以上限制进行调整，最迟在调整前 2 日在指定媒体及基金管理人网站上公告。\r\n 4、申购份额及余额的处理方式：场外申购时，申购的有效份额为按实际确\r\n 认的申购金额在扣除相应的费用后，以申请当日基金份额净值为基准计算，四舍\r\n 五入保留到小数点后两位，由此误差产生的收益或损失由基金财产承担；场内申购时，申购的有效份额为按实际确认的申购金额在扣除相应的费用后，以申请当日基金份额净值为基准计算，保留到整数位，不足一份基金份额部分的认购资金零头由交易所会员单位返还给投资者。\r\n 5、赎回金额的处理方式：赎回费用以人民币元为单位，计算结果按照四舍\r\n 五入方法，保留小数点后 2 位；赎回金额为按实际确认的有效赎回份额乘以当日\r\n 基金份额净值并扣除相应的费用，计算结果保留到小数点后 2 位，小数点 2 位以后的部分四舍五入。由此产生的损失由基金财产承担，产生的收益归入基金财产。\r\n 6、基金份额净值的计算，保留到小数点后 3 位，小数点后第 4 位四舍五入，由此误差产生的损失由基金财产承担，产生的收益归入基金财产。\r\n （六）申购费用和赎回费用\r\n 1、申购费用投资者可选择在申购本基金或赎回本基金时交纳申购费用。投资者选择在申购时交纳的称为前端申购费用，投资者选择在赎回时交纳的称为后端申购费用。\r\n 投资人选择前端申购费用时，按申购金额采用比例费率。投资人在一天之内如果有多笔申购，适用费率按单笔分别计算。具体费率如下：\r\n 表 8-1：基金前端申购费率表\r\n 申购金额（M，含申购费） 前端申购费率\r\n M＜100 万 1.5%\r\n 100 万≤M＜500 万 1.0%\r\n 500 万≤M 1000 元每笔\r\n 注：M 为申购金额\r\n 投资者选择交纳后端申购费用时，按申购金额采用比例费率，费率随持有时间的增加而递减，具体费率如下：\r\n 表 8-2：基金后端申购费率表\r\n 持有年限（N） 后端申购费率\r\n N＜1年 1.8%\r\n 1 年≤N＜3年 1.2%\r\n 3 年≤N＜5年 0.6% 年≤N 0\r\n 注：N 为持有年限投资人选择红利自动再投资所转成的份额不收取申购费用。\r\n 2、赎回费用\r\n 本基金的赎回费用在投资人赎回本基金份额时收取，赎回费率随持有时间的增加而递减，具体费率如下：\r\n 表 8-3：基金赎回费率表\r\n 持有时间（N，年） 赎回费率\r\n N＜1年 0.5%\r\n 1 年≤N＜2年 0.2%\r\n 2 年≤N 0\r\n 注：N 为持有年限\r\n 3、基金管理人可以在履行相关手续后，在基金合同约定的范围内调整申购、赎回费率或调整收费方式，基金管理人最迟应于新的费率或收费方式实施日前 2日在至少一种指定媒体和基金管理人的公司网站上公告。\r\n 4、基金管理人可以在不违背法律法规规定及基金合同约定的情形下根据市\r\n 场情况制定基金促销计划，针对以特定交易方式（如网上交易、电话交易等）等进行基金交易的投资者定期或不定期地开展基金促销活动。在基金促销活动期间，基金管理人可以按中国证监会要求履行必要手续后对投资人适当调整基金申购费率、基金赎回费率和基金转换费率。\r\n 5、本基金的申购费用应在投资人申购基金份额时收取，不列入基金财产，主要用于本基金的市场推广、销售、注册登记等各项费用。赎回费用由赎回基金份额的基金份额持有人承担，在基金份额持有人赎回基金份额时收取。不低于赎回费总额的 25%应归基金财产，其余用于支付注册登记费和其他必要的手续费。\r\n （七）申购份额和赎回金额的计算\r\n 1、申购份额的计算公式申购本基金的申购费用收取方式包括前端收费模式和后端收费模式两种；\r\n （1）前端收费模式\r\n 申购费用=申购金额－申购金额/（1＋申购费率）净申购金额＝申购金额－申购费用\r\n 申购份额＝净申购金额÷T 日基金份额净值\r\n 例：假定 T 日的基金份额净值为 1.1 元，申购金额为 1 万元，采用前端收费模式，申购费率为 1.5%，则需负担的申购费用和得到的基金份额为：\r\n 申购金额 申购费率 净申购金额 申购费用 基金份额净值 申购份数\r\n 10，000 元 1.5% 9，852.22 元 147.78 元 1.100 元 8，956.56 份\r\n 如果投资者是场外申购，可得到的申购份额为 8956.56 份；\r\n 如果投资者是场内申购，可得到的申购份额为 8956 份，剩余 0.62 元返还给投资者。\r\n （2）后端收费模式\r\n 申购份额=申购金额/T 日基金份额净值\r\n 例：假定 T 日的基金份额净值为 1.1 元，申购金额为 1 万元，采用后端收费模式，假定申购当日基金份额净值为 1.1 元，则其可得到的申购份额为：\r\n 申购金额 基金份额净值 申购份数\r\n 10，000 元 1.100 元 9，090.91 份\r\n 2、赎回金额的计算\r\n 投资人提交赎回申请时，其在销售机构（网点）必须有足够的基金份额余额。\r\n （1）如果投资人在申购时选择交纳前端申购费用，则赎回金额的计算方法\r\n 如下：\r\n 赎回总额=赎回份额×T 日基金份额净值\r\n 赎回费用=赎回总额×赎回费率\r\n 赎回金额=赎回总额－赎回费用\r\n 例：某投资人赎回 1 万份基金份额，对应的赎回费率为 0.5%，假设赎回当日基金份额净值是 1.2 元，则其可得到的赎回金额为：\r\n 赎回份额 赎回费率 赎回总额 赎回费用 赎回当日基金份额净值 赎回金额\r\n 10，000 份 0.5% 1，2000 元 60 元 1.200 元 11，940 元\r\n （2）如果投资人在申购时选择交纳后端申购费用，则赎回金额的计算方法如下：\r\n 赎回总额=赎回份额×T 日基金份额净值\r\n 后端申购费用=赎回份额×申购日基金份额净值（基金份额面值）×后端申购费率\r\n 赎回费用=赎回总额×赎回费率\r\n 赎回金额=赎回总额－后端申购费用-赎回费用\r\n 例：某投资人赎回 1 万份基金份额，对应的赎回费率为 0.5%，假设赎回当日基金份额净值是 1.2 元，对应的后端申购费率为 1.8%，申购时的基金份额净\r\n 值为 1.1 元，则其可得到的赎回金额为：\r\n 赎回份额赎回当日基金份额净值赎回总额申购当日基金份额净值后端申购费率后端申购费用赎回费率赎回\r\n 费用 赎回金额\r\n 10，000\r\n 份 1.200 元\r\n 12，000\r\n 元 1.100 元 1.8% 198 元 0.5% 60 元 11，742 元\r\n 3、基金份额净值的计算公式为：\r\n 基金份额净值＝基金资产净值总额÷基金份额总数。\r\n 本基金 T 日的基金份额净值在当日收市后计算，并在 T＋1 日公告。遇特殊情况，经中国证监会同意，可以适当延迟计算或公告。\r\n （八）申购和赎回的注册登记\r\n 投资人申购基金份额成功后，基金注册登记机构在 T+1 日为投资人登记权益并办理注册登记手续，投资人自 T+2 日（含该日）后有权赎回该部分基金份额。\r\n 投资人赎回基金份额成功后，基金注册登记机构在 T+1 日自动为投资人办理扣除权益的注册登记手续。\r\n 基金管理人可以在法律法规允许的范围内，对上述注册登记办理时间进行调整，但不得实质影响投资人的合法权益，并最迟于开始实施前 3 个工作日在中国证监会指定媒体及基金管理人网站公告。\r\n （九）拒绝或暂停申购的情形及处理方式\r\n 发生下列情况时，基金管理人可拒绝或暂停接受投资人的申购申请：\r\n 、不可抗力的原因导致基金无法正常运作；\r\n 2、证券交易场所在交易时间非正常停市，导致基金管理人无法计算当日的基金资产净值；\r\n 3、发生本基金合同规定的暂停基金资产估值情况；\r\n 4、基金管理人、基金托管人、基金销售机构或注册登记机构的技术保障或人员支持等不充分；\r\n 5、基金资产规模过大，使基金管理人无法找到合适的投资品种，或基金管理人认为会损害已有基金份额持有人的利益的申购；\r\n 6、法律法规规定或中国证监会认定的其他情形。\r\n 发生上述 1、2、3、5、6 项暂停申购情形时，基金管理人应当根据有关规定在指定媒体及基金管理人网站上刊登暂停申购公告。如果投资人的申购申请被拒绝，被拒绝的申购款项将退还给投资人。在暂停申购的情况消除时，基金管理人应及时恢复申购业务的办理、依法公告并报中国证监会和基金管理人主要办公场所所在地中国证监会派出机构备案。\r\n （十）暂停赎回或者延缓支付赎回款项的情形及处理方式\r\n 发生下列情形时，基金管理人可暂停接受投资人的赎回申请或延缓支付赎回款项：\r\n 1、因不可抗力导致基金管理人不能支付赎回款项；\r\n 2、证券交易场所交易非正常停市，导致基金管理人无法计算当日基金资产净值；\r\n 3、连续两个开放日发生巨额赎回；\r\n 4、发生基金合同规定的暂停基金资产估值情况；\r\n 5、法律法规规定或中国证监会认定的其他情形。\r\n 发生上述情形时，基金管理人应在当日报中国证监会备案，已接受的赎回申请，基金管理人应足额支付；如暂时不能足额支付，应将可支付部分按单个账户申请量占申请总量的比例分配给赎回申请人，未支付部分可延期支付，并以后续开放日的基金份额净值为依据计算赎回金额。若连续两个或两个以上开放日发生巨额赎回，延期支付最长不得超过 20 个工作日，并在指定媒体上公告。投资人在申请赎回时可事先选择将当日可能未获受理部分予以撤销。在暂停赎回的情况消除时，基金管理人应及时恢复赎回业务的办理并予以公告。\r\n （十一）巨额赎回的情形及处理方式\r\n 1、巨额赎回的认定若本基金单个开放日内的基金份额净赎回申请（赎回申请份额总数加上基金转换中转出申请份额总数后扣除申购申请份额总数及基金转换中转入申请份额总数后的余额）超过前一日基金总份额的 10%时，即认为是发生了巨额赎回。\r\n 2、巨额赎回的处理方式\r\n 当基金出现巨额赎回时，基金管理人可以根据基金当时的资产组合状况决定全额赎回或部分延期赎回。\r\n （1）全额赎回：当基金管理人认为有能力支付投资人的全部赎回申请时，按正常赎回程序执行。\r\n （2）部分延期赎回：当基金管理人认为支付投资人的赎回申请有困难或认为因支付投资人的赎回申请而进行的财产变现可能会对基金资产净值造成较大波动时，基金管理人在当日接受赎回比例不低于上一开放日基金总份额的 10%的前提下，可对其余赎回申请延期办理。对于当日的赎回申请，应当按单个账户赎回申请量占赎回申请总量的比例，确定当日受理的赎回份额；对于未能赎回部分，投资人在提交赎回申请时可以选择延期赎回或取消赎回。选择延期赎回的，将自动转入下一个开放日继续赎回，直到全部赎回为止；选择取消赎回的，当日未获受理的部分赎回申请将被撤销。延期的赎回申请与下一开放日赎回申请一并处理，无优先权并以下一开放日的基金份额净值为基础计算赎回金额，以此类推，直到全部赎回为止。如投资人在提交赎回申请时未作明确选择，投资人未能赎回部分作自动延期赎回处理。\r\n （3）暂停赎回：连续 2 日以上（含本数）发生巨额赎回，如基金管理人认为有必要，可暂停接受基金的赎回申请；已经接受的赎回申请可以延缓支付赎回款项，但不得超过 20 个工作日，并应当在指定媒体上进行公告。\r\n 3、巨额赎回的公告\r\n 当发生上述延期赎回并延期办理时，基金管理人应当通过邮寄、传真或者招募说明书规定的其他方式在 3 个交易日内通知基金份额持有人，说明有关处理方法，同时在指定媒体上刊登公告。\r\n （十二）暂停申购或赎回的公告和重新开放申购或赎回的公告\r\n 1、发生上述暂停申购或赎回情况的，基金管理人当日应立即向中国证监会备案，并在规定期限内在依照有关规定在指定媒体及基金管理人网站刊登暂停公告。\r\n 2、如果发生暂停的时间为 1 日，基金管理人应于重新开放日在指定媒体上\r\n 刊登基金重新开放申购或赎回公告，并公布最近 1 个开放日的基金份额净值。\r\n 3、如果发生暂停的时间超过一日但少于两周，暂停结束基金重新开放申购或赎回时，基金管理人应提前 2 个工作日在指定媒体及基金管理人网站刊登基金重新开放申购或赎回公告，并公告最近一个开放日的基金份额净值。\r\n 4、如果发生暂停的时间超过 2 周，暂停期间，基金管理人应每 2 周至少刊\r\n 登暂停公告一次。暂停结束基金重新开放申购和赎回时，基金管理人应提前 2个工作日在指定媒体及基金管理人网站上连续刊登基金重新开放申购或赎回公告并公告最近一个开放日的基金份额净值。\r\n （十三）基金的转换\r\n 本公司已开通了长信恒利优势股票基金与长信利息收益货币基金 A、B 级份\r\n 额（A 级别代码：519999；B 级代码：519998）、长信银利精选股票基金（前端代码：519997）、长信金利趋势股票基金（前端代码：519995）、长信增利动态股票基金（前端代码：519993）、长信中短债基金、长信量化先锋股票基金、长信利丰债券基金、长信双利优选混合基金、长信内需股票基金、长信可转债债券基金之间的双向转换业务。\r\n 根据中国证监会[2009]32 号《开放式证券投资基金销售费用管理规定》的相关规定，本公司从 2010 年 4 月 23 日起调整开放式基金转换业务规则。具体规则如下：\r\n 1、投资者进行基金转换时，转换费率将按照转出基金的赎回费用加上转出与转入基金申购费用补差的标准收取。当转出基金申购费率低于转入基金申购费率时，费用补差为按照转出基金金额计算的申购费用差额；当转出基金申购费率高于转入基金申购费率时，不收取费用补差。\r\n 2、转换份额的计算公式：\r\n （1）非货币基金之间转换\r\n 转出确认金额＝转出份额×转出基金份额净值\r\n 赎回费＝转出确认金额×赎回费率\r\n 补差费＝(转出确认金额-赎回费)×补差费率÷(1+补差费率)\r\n 转入确认金额＝转出确认金额－赎回费－补差费\r\n 转入确认份额＝转入确认金额÷转入基金份额净值（若转出基金申购费率高于转入基金申购费率时，补差费为零）\r\n （2）货币基金转至非货币基金\r\n 转出确认金额＝转出份额×转出基金份额净值＋转出份额对应的未结转收益\r\n 补差费＝转出份额×转出基金份额净值×补差费率÷(1＋补差费率)\r\n 转入确认金额＝转出确认金额－补差费\r\n 转入确认份额＝转入确认金额÷转入基金份额净值\r\n (货币基金份额净值为 1.00 元，没有赎回费)\r\n （十四）基金的转托管\r\n 基金份额持有人可办理已持有基金份额在不同销售机构之间的转托管，基金销售机构可以按照规定的标准收取转托管费。\r\n 如果出现基金管理人、注册登记机构、办理转托管的销售机构因技术系统性能限制或其它合理原因，可以暂停该业务或者拒绝基金份额持有人的转托管申请。\r\n （十五）定期定额投资计划\r\n 在各项条件成熟的情况下，本基金可为投资人提供定期定额投资计划服务，具体实施方法以更新后的招募说明书和基金管理人的公告为准。投资人在办理定期定额投资计划时可自行约定每期扣款金额，每期扣款金额必须不低于基金管理人在相关公告或更新的招募说明书中所规定的定期定额投资计划最低申购金额。\r\n （十六）基金的非交易过户\r\n 指基金注册登记机构受理继承、捐赠、司法强制执行和经注册登记机构认可的其它情况而产生的非交易过户。无论在上述何种情况下，接受划转的主体必须是依法可以持有本基金基金份额的投资人。\r\n 继承是指基金份额持有人死亡，其持有的基金份额由其合法的继承人继承；\r\n 捐赠指基金份额持有人将其合法持有的基金份额捐赠给福利性质的基金会或社会团体；司法强制执行是指司法机构依据生效司法文书将基金份额持有人持有的\r\n 基金份额强制划转给其他自然人、法人或其他组织。办理非交易过户必须提供基金注册登记机构要求提供的相关资料，对于符合条件的非交易过户申请按注册登记机构的有关规定办理，并按基金注册登记机构规定的标准收费。\r\n （十七）其他情形\r\n 基金份额冻结、解冻的业务，由注册登记机构办理。\r\n 基金注册登记机构只受理国家有关机关依法要求的基金份额的冻结与解冻以及注册登记机构认可的其他情况的基金份额的冻结与解冻。\r\n 当基金份额处于冻结状态时，基金注册登记机构或其他相关机构应拒绝基金份额的赎回申请、非交易过户以及基金的转托管。\r\n 九、基金的投资\r\n （一）投资目标\r\n 把握经济及产业发展趋势和市场运行特征，充分利用产业升级和产业机构调整的机会，挖掘在经济发展以及市场运行不同阶段中优势产业内所蕴含的投资机会，重点投向受益于产业增长和兼具优质品质的个股，力争获取基金资产的长期稳定增值。\r\n （二）投资范围\r\n 本基金的投资范围为具有良好流动性的金融工具，包括国内依法发行的股票、债券、权证、资产支持证券以及法律法规或中国证监会允许基金投资的其他金融工具。\r\n 如法律法规或监管机构以后允许基金投资的其他品种，基金管理人在履行适当程序后，可以将其纳入投资范围。\r\n 本基金投资于股票资产的比例范围是基金资产的 60%-95%，投资于现金、债券资产以及中国证监会允许基金投资的其他证券品种的比例范围是基金资产的\r\n 5%-40%，现金及到期日在一年以内的政府债券不低于基金资产净值的 5%。本基\r\n 金投资优势产业的资产不低于股票资产的 80%。投资于权证、资产支持证券或其它衍生金融工具的比例遵守法律法规或中国证监会的有关规定。\r\n （三）投资策略\r\n 1、投资理念\r\n 采用全球产业分析的框架，以全球产业分析的视野和角度，选择符合经济发展趋势、产业发展趋势以及市场运行特征的优势产业作为重点投资对象，将优势产业的巨大发展潜力转化为获取持续投资回报的源动力。\r\n 采用上市公司品质评价模型，对上市公司进行深入研究和跟踪，真正发掘上市公司的内在价值及其持续增长的潜力，利用上市公司内在价值持续稳定增长的前景所推动的持续股价上涨为投资者获取投资回报。\r\n 本基金管理人将用产业投资的视角，选择具备价值增值优势的产业作为重点投资对象选取范围，结合深入的上市公司内在价值分析，投资于产业价值增值背景下的优质上市公司，为投资者获取长期、稳定的预期投资收益。\r\n 2、投资策略\r\n 本基金实行风险管理下的主动投资策略，即采用自上而下和自下而上相结合的投资策略，自上而下是以战略的角度强调资产配置和组合管理，自下而上是以战术的角度强调精选个股。\r\n 具体而言，在资产配置和组合管理方面，利用组合管理技术和金融工程相结合的方法，严格控制及监测组合的风险和保持组合流动性。在股票组合方面，自上而下通过深入研究宏观经济环境、市场环境、产业特征等因素，优选增长前景良好和增长动力充分，具备价值增值优势的产业作为重点投资对象，并结合自下而上的对上市公司的深入分析，投资于精选出来的个股，为投资者获取优势产业增长所能带来的最佳投资收益；\r\n 根据本基金对优势产业特征的定义和分析，现阶段本基金重点关注的优势产业为具备以下特征的产业：\r\n （1）在全球拥有竞争优势的产业；\r\n （2）在经济全球化趋势下具有资源优势的产业；\r\n （3）在中国经济增长环境下呈现出具有持续竞争优势，良好成长性和持续盈\r\n 利增长潜力的产业；这类产业细分为以下三个板块：\r\n A、内需增长背景下具有竞争优势的产业\r\n B、国家政策导向和大力扶持的优势产业\r\n C、产业结构变化带来价值增值的优势产业\r\n 本基金将捕捉各个产业中各行业发展的不同态势，从而选取优势产业方向中细分的行业进行投资，并且随其变化适时调整，以更好把握经济变化的趋势。\r\n 未来，全球产业结构会随着各国社会和经济的发展而变化，中国经济增长也将发生适应自身需求的转变，本基金管理人将根据产业变化的前瞻性的研究和判断，对投资方向作出适合本国经济增长变化的调整。\r\n 个股分析是本基金股票投资策略中“自下而上”投资策略的体现，在有效控制风险的前提上，为投资者实现尽可能高的投资回报。在个股分析中采用本公司特有的品质分析模型，结合风险收益特征分析和估值分析来确定股票的投资价值。在优势产业确定的基础上，本基金对个股的投资可以在不同风格类型、不同市值大小和不同行业板块之间进行灵活选择。\r\n 3、资产配置\r\n （1）资产配置策略和决策流程\r\n 在资产配置的具体流程中，本基金采用“自上而下”的资产配置方法，以及定性分析与定量分析相补充的方法，从全球和国家的视野出发，通过短期和长期两个角度进行深入研究和分析，充分把握宏观经济增长的长期趋势，短期的经济周期波动，以及国家的长期和短期发展策略，从而判断各类资产的风险程度和收益特征在控制风险的前提下，合理配置股票、债券、及其他金融工具在整体基金资产中的配置比例，调整原则和调整范围，并进行定期或不定期的调整，以达到控制风险、增加收益的目的。\r\n 本基金进行资产配置时所采用的指标包括 GDP 增长率、货币供应量及其变化、利率水平与走势、通货膨胀率、货币与财政政策、汇率政策等。\r\n 此外，本基金还将对市场信心指标、行业动量指标、收益率曲线变化等指标进行观察和分析，对市场变化特征和运行规律进行判断，增加整体资产配置的有效性。\r\n （2）大类资产配置的流程\r\n 本基金的大类资产的配置流程如下图所示：\r\n 图 9-1：长信恒利优势股票型基金资产配置决策流程\r\n 具体分析流程为：\r\n A、宏观经济分析\r\n 根据各类经济指标，对宏观经济进行长期和短期分析，判断出经济运行的周期，产业发展景气等特征。\r\n B、对经济增长、资本市场和政策法规进行分析\r\n 这包括分析经济增长和法律法规对产业和上市公司的盈利预期影响，考察市场整体的估值水平，大类资产的风险和预期收益等。\r\n C、风险控制\r\n 基于上述的分析，对大类资产的风险和预期收益在各种经济状况下进行情景分析，得到风险控制和资产配置的预案。\r\n D、组合的优化和修正\r\n 金融工程部对资产配置比例进行资产配置优化分析，给出优化后各大类资产的配置比例。\r\n E、确定和修正资产配置比例\r\n 利用资产配置组合优化的结果和资产配置预案确定最后的资产配置比例，并定期根据市场环境的变化，及时修正资产配置比例。\r\n 经济增长分析宏观经济分析资本市场分析风险控制\r\n 股票类资产 债券类资产 其他资产政策法规分析\r\n 短期分析 长期分析确定和修正资产配置比例、股票投资策略\r\n （1）股票投资组合的分析框架和构建\r\n A、全球产业分析框架下优势产业的选择\r\n a、本基金按照“自上而下”的原则进行优势产业的分析和选择；\r\n b、本基金从全球产业分析的视野和角度出发，对各个产业的演进和变迁进行分析；\r\n c、国家分析在全球产业分析的指导下对国家因素进行分析，国家分析从短期和长期两个角度出发，通过以下指标来分析，其中短期指标包括：出口、投资和消费结构、预期真实增长率、货币政策、财政政策、工资刚性、竞争优势、社会环境、政治环境以及投资环境等。长期指标包括：经济周期、经济周期的波动、经济增长的长期可持续性和长短期产业政策的导向。\r\n d、产业分析产业分析从风险和收益两个角度出发具体对各个产业进行深入的分析和预测。\r\n B、具竞争优势的个股选择\r\n a、本基金按照“自下而上”的原则进行个股的分析和选择；\r\n b、通过长信基金的财务品质评估模型和经营品质评估模型对个股进行评估；\r\n 长信基金的品质评估模型包括：财务品质评估、经营品质评估，从多个角度的对上市公司的情况进行分析和评估。\r\n c、风险因素分析风险因素分析是对个股的风险暴露程度进行多因素分析，该分析主要从两个角度进行，一个是利用个股本身特有的信息进行分析。另一个是利用线性回归模型对风险因素进行敏感性分析。\r\n d、估值分析个股的估值是利用绝对估值和相对估值的方法，寻找估值合理和价值低估的个股进行投资。这里我们主要利用股利折现模型、现金流折现模型、剩余收入折现模型、P/E 模型、EV/EBIT 模型、Franchise P/E 模型等针对不同类型的产业和个股进行估值分析，另外在估值的过程中同时考虑通货膨胀因素对股票估值的影响，排除通胀的影响因素。\r\n C、股票备选库的构建\r\n 通过前面阐述的个股选择方法，本基金管理人将通过研究现有资料、实地调研和团队讨论分析相结合的方法，形成基金的备选股票库。基本流程如下：\r\n 剔除基金禁止投资的股票；\r\n 通过历史数据和市场表现对个股进行综合分析和评估；\r\n 结合实地调研对个股进行深入评定；\r\n 通过个股论证会，最终形成基金的备选股票库；\r\n 根据对个股影响因素的变化，及时对备选股票库中的股票进行调整。\r\n D、股票资产投资组合的调整\r\n 结合产业分析和个股分析最终确定出基金股票类资产的投资组合，之后本基金将定期和不定期的对股票组合进行调整。\r\n a、定期调整本基金将定期召开投资决策委员会会议，对资产配置和产业配置进行讨论，评估投资组合的构成，动态纳入优势产业，剔除缺乏优势的产业，以及评估整体资产配置效果以优化资产配置。\r\n b、不定期调整当遇到突发性的事件时，导致整体市场、产业或个股的基本面发生显著变化，则及时对资产配置和优势产业的选择进行评估和调整。例如，当国家或地区出台产业扶持政策或者限制政策，导致产业的发展出现重大变化；或者由于兼并重组、新技术的运用导致产业结构发生重大变化等。\r\n （2）股票投资组合管理流程\r\n 本基金根据宏观研判、产业分析和个股分析确定基金资产的类属配置和类别配置。在类别配置中，股票资产的配置为本基金的核心，在此阶段本基金通过优势产业的选择和具有竞争优势的个股分析，明确基金资产的配置目标，最后进入投资管理决策阶段。\r\n 本基金的股票投资组合管理流程如下图所示：\r\n 图 9-2：长信恒利优势基金股票投资组合管理流程\r\n 5、债券投资组合管理\r\n 本基金在债券资产的投资过程中，将采用积极主动的投资策略，结合宏观经济政策分析、经济周期分析、市场短期和中长期利率分析、供求变化等多种因素分析来配置债券品种，在兼顾收益的同时，有效控制基金资产的整体风险。总体而言，在股票市场的上升阶段，增加股票资产的配置比例，减少债券资产的投资比例；在股票市场的下降阶段，减少股票资产的配置比例，增加债券资产的配置比例。\r\n 在个券的选择上，本基金将综合运用估值策略、久期管理策略、利率预期策略等多种方法，结合债券的流动性、信用风险分析等多种因素，对各券进行积极的管理。\r\n （1）估值策略\r\n 通过建立收益率曲线预测模型，对各券进行估值，确定各券的合理价格，选择具有合理投资价值的个券进行投资。\r\n （2）久期管理策略\r\n 组合风险与业绩评估 构建投资组合资产配置分析股票类资产配置优势产业\r\n 所有 A股确定基本选择范围重点选择范围初选剔除个股分析模型产业分析模型基金资产久期是影响债券价格的重要因素，本基金将基于对各种利率影响因素的前瞻性判断，制定债券投资组合的目标久期，平衡基金组合的预期风险与收益。简单而言，如果预测利率趋于上升，基金则适当降低债券投资组合的久期；如果预测利率趋于下降，基金则适当增加债券投资组合的久期。\r\n （3）利率预期策略\r\n 本基金根据对宏观经济发展趋势、资本市场的变化进行分析和预测，重点分析利率趋势的变化，同时结合国家的货币政策、财政政策、物价指数、固定资产投资增长等因素变化形成利率的合理预期。\r\n 6、其它类型资产投资策略\r\n 在法律法规或监管机构允许的情况下，本基金将在严格控制投资风险的基础上适当参与对权证、资产支持证券等金融工具的投资。\r\n （1）权证投资策略\r\n 权证在收益结构上类似于期权，可以赋予权证买方在一定期限、既定价格下买入或卖出某项资产的权利，而不是义务。本基金对权证的投资将以控制投资风险为主，适当采用以下方式进行投资。\r\n A、综合分析权证的包括执行价格、标的股票波动率、剩余期限等因素在内\r\n 的定价因素，对权证的合理价值做出判断；\r\n B、基于权证的价值分析以及对权证标的股票未来走势的预期，对权证进行单向投资；\r\n C、基于权证价值对标的价格、波动率的敏感性以及价格未来走势等因素的判断，将权证、标的股票等金融工具合理配置进行结构性组合投资。\r\n （2）资产支持证券投资策略\r\n 目前国内的资产支持证券市场尚处于发展初期，市场的品种数量、发行规模、信息透明度等方面仍有较大的提升空间，本基金将在严格控制组合投资风险的前提下投资于这类金融工具。在具体品种和分支的选择上，本基金从资产池信用状况、违约相关性、历史违约记录和损失比例、证券的信用增强方式、利差补偿程度等方面对资产支持证券各个分支的风险与收益状况进行评估，根据基金投资组合管理的目标选择合适的分支进行投资。\r\n （四）业绩比较基准\r\n 本基金股票投资部分的业绩比较基准为沪深 300 指数，债券投资部分的业绩比较基准为上证国债指数，本基金的业绩比较基准为复合指数：\r\n 沪深 300 指数*70%+上证国债指数*30%\r\n 如果将来出现更合适的业绩比较基准，本基金将根据实际情况适当调整业绩比较基准，并在报送有关监管机构后予以公布。\r\n 选择业绩比较基准的理由为：\r\n （1）沪深 300 指数是国内股票市场广受投资者关注的市场指数之一，指数本\r\n 身对国内股票市场的走势有很强的代表性和广泛的影响力，可以在本基金的投资运作中较好地表征市场平均收益水平。上证国债指数的成分股包括在上海证券交易所上市的所有固定利率国债，该指数能够较好地反映债券市场的整体变动状况，在目前交易所债券市场和银行间市场尚未推出公认的全市场债券指数的情况下，上证国债指数是一个较为合适的选择。\r\n （2）本基金为股票型基金，在对国内股票市场长期看好的情况下，本基金将\r\n 长期保持相对较高的股票配置比例，设定股票指数和债券指数各自的权重分别为\r\n 70%和 20%能够体现基金的这一运作目标。\r\n （五）风险收益特征\r\n 本基金属于股票型证券投资基金，属于证券投资基金中较高预期风险和预期收益的证券投资基金产品，其预期风险和收益水平高于混合型基金、债券基金及货币市场基金。\r\n （六）投资决策依据\r\n 1、国家有关法律法规和本《基金合同》的有关规定；\r\n 2、宏观经济形势及前景、有关政策趋向对证券市场的影响等；\r\n 3、国家财政政策、货币政策、产业政策，以及利率走势、通货膨胀预期等；\r\n 4、股票、债券、衍生产品等类别资产的预期收益率及风险水平。\r\n （七）投资决策流程、研究发展部向投资决策委员会提交有关宏观经济分析、投资策略、债券\r\n 分析等各类研究报告和投资建议，为投资运作提供决策支持；\r\n 2、投资决策委员会对宏观经济形势、利率走势、微观经济运行环境和证券\r\n 市场走势等因素进行综合分析，制定本基金投资组合的资产配置比例等重大决策；\r\n 3、根据研究团队的报告和对市场的判断，在遵守投资决策委员会制定的投\r\n 资原则的前提下，向投资决策委员会提交基金的投资策略和资产配置建议；\r\n 4、投资决策委员会在认真分析研究团队所提供的研究报告和投资策略草案后，确定公司所管理的基金资产的投资战略、投资方向，形成决策，并以投资决议形式下达执行；\r\n 5、根据投资决策委员会的投资决策，结合研究人员提供的投资建议、自己\r\n 的研究与分析判断以及每日基金申购和赎回的净现金流量和市场整体情况、投资组合和个股的流动性变化，在授权范围内制订具体的投资组合及操作方案，交由交易管理部执行；\r\n 6、交易管理部主要是根据基金经理的投资指令，进行基金资产的日常交易活动，对交易情况及时反馈，并对投资指令进行监督，遇有异常情况要及时向投资总监反映；同时对投资指令合规性检查；\r\n 7、金融工程部的风险与业绩评估人员定期对基金投资组合进行业绩和风险评估，提供基金业绩评估报告，提出风险控制意见，作为投资决策委员会调整风险控制策略和评估投资业绩的参考；\r\n 8、监察稽核部负责监督整个投资交易全过程是否有违反国家相关法律、法\r\n 规和公司规章制度的规定，检查有无涉嫌内幕交易、侵犯基金份额持有人权益的行为，并考察所有参与基金投资全过程管理及业务人员的职业操守；\r\n 9、对整体投资组合及资金运用进行日常跟踪、分析，并根据市场变化、实\r\n 际交易情况及研究发展部的追踪研究，在其权限范围内及时调整投资组合；\r\n 10、在确保基金份额持有人利益的前提下，有权根据环境的变化和实际的需要对上述投资决策程序进行合理的调整。\r\n （八）投资限制本基金的投资组合将遵循以下限制：\r\n （1）本基金持有一家上市公司的股票，其市值不超过基金资产净值的 10%；\r\n （2）本基金持有的全部权证，其市值不得超过基金资产净值的 3%；\r\n （3）本基金管理人管理的全部基金持有一家公司发行的证券，不超过该证券\r\n 的 10%；\r\n （4）本基金管理人管理的全部基金持有的同一权证，不得超过该权证的 10%；\r\n （5）本基金进入全国银行间同业市场进行债券回购的资金余额不得超过基金\r\n 资产净值的 40%；\r\n （6）本基金投资于股票资产的比例范围是基金资产的 60%-95%，投资于现金、债券资产以及中国证监会允许基金投资的其他证券品种的比例范围是基金资产\r\n 的 5%-40%，现金及到期日在一年以内的政府债券不低于基金资产净值的 5%。本\r\n 基金投资优势产业的资产不低于股票资产的 80%；\r\n （7）本基金投资于同一原始权益人的各类资产支持证券的比例，不得超过基\r\n 金资产净值的 10%；\r\n （8）本基金持有的全部资产支持证券，其市值不得超过基金资产净值的 20%；\r\n （9）本基金持有的同一（指同一信用级别）资产支持证券的比例，不得超过\r\n 该资产支持证券规模的 10%；\r\n （10）本基金管理人管理的全部基金投资于同一原始权益人的各类资产支持证券，不得超过其各类资产支持证券合计规模的 10%；\r\n （11）本基金应投资于信用级别评级为 BBB 以上（含 BBB）的资产支持证券。基金持有资产支持证券期间，如果其信用等级下降、不再符合投资标准，应在评级报告发布之日起 3 个月内予以全部卖出；\r\n （12）基金财产参与股票发行申购，本基金所申报的金额不超过本基金的总资产，本基金所申报的股票数量不超过拟发行股票公司本次发行股票的总量；\r\n （13）本基金在任何交易日买入权证的总金额，不得超过上一交易日基金资\r\n 产净值的 0.5%；\r\n （14）如果法律法规对本基金合同约定投资组合比例限制进行变更的，以变\r\n 更后的规定为准。法律法规或监管部门取消上述限制，如适用于本基金，则本基金投资不再受相关限制。\r\n 因证券市场波动、上市公司合并、基金规模变动、股权分置改革中支付对价等基金管理人之外的因素致使基金投资比例不符合上述规定投资比例的，基金管理人应当在 10 个交易日内进行调整。\r\n 对于因基金份额拆分、大比例分红等集中持续营销活动引起的基金净资产规\r\n 模在 10 个交易日内增加 10 亿元以上的情形，而导致证券投资比例低于基金合同约定的，基金管理人履行相关程序后可将调整时限从 10 个交易日延长到 3 个月。\r\n 法律法规如有变更，从其变更。\r\n 基金管理人应当自基金合同生效之日起 6 个月内使基金的投资组合比例符\r\n 合基金合同的有关约定。除投资资产配置比例外，基金托管人对基金的投资比例的监督与检查自本基金合同生效之日起开始。\r\n （九）禁止行为\r\n 为维护基金份额持有人的合法权益，基金财产不得用于下列投资或者活动：\r\n 1、承销证券；\r\n 2、向他人贷款或者提供担保；\r\n 3、从事承担无限责任的投资；\r\n 4、买卖其他基金份额，但是国务院另有规定的除外；\r\n 5、向其基金管理人、基金托管人出资或者买卖其基金管理人、基金托管人发行的股票或者债券；\r\n 6、买卖与其基金管理人、基金托管人有控股关系的股东或者与其基金管理\r\n 人、基金托管人有其他重大利害关系的公司发行的证券或者承销期内承销的证券；\r\n 7、从事内幕交易、操纵证券交易价格及其他不正当的证券交易活动；\r\n 8、依照法律法规有关规定，由中国证监会规定禁止的其他活动；\r\n 9、法律法规或监管部门取消上述限制，如适用于本基金，则本基金投资不再受相关限制。\r\n （十）基金管理人代表基金行使股东和债券人权利权利的处理原则及方法\r\n 1、基金管理人按照国家有关规定代表基金独立行使股东权利，保护基金份额持有人的利益；\r\n 2、不谋求对上市公司的控股，不参与所投资上市公司的经营管理；\r\n 3、有利于基金财产的安全与增值；\r\n 4、不通过关联交易为自身、雇员、授权代理人或任何存在利害关系的第三人牟取任何不当利益。\r\n （十一）基金的融资、融券\r\n 本基金可以根据有关法律法规和政策的规定进行融资、融券。\r\n 十、基金投资组合报告\r\n 基金管理人的董事会及董事保证本报告所载资料不存在虚假记载、误导性陈述或重大遗漏，并对其内容的真实性、准确性和完整性承担个别及连带责任。\r\n 基金托管人中国建设银行股份有限公司根据本基金合同规定，于 2013 年 8月复核了本报告中的财务指标、净值表现和投资组合报告等内容，保证复核内容不存在虚假记载、误导性陈述或者重大遗漏。\r\n 本投资组合报告所载数据截至 2013 年 6 月 30 日（摘自本基金 2013 年 2 季报），本报告中所列财务数据未经审计。\r\n （一）报告期末基金资产组合情况\r\n 序号 项目 金额（元） 占基金总资产的比例（%）\r\n 1 权益投资 176，958，299.03 85.80\r\n 其中：股票 176，958，299.03 85.80\r\n 2 固定收益投资 － －\r\n 其中：债券 － －\r\n 资产支持证券 － －\r\n 3 金融衍生品投资 － －\r\n 4 买入返售金融资产 － －\r\n 其中：买断式回购的买入返售金融资产 － －\r\n 5 银行存款和结算备付金合计 29，169，531.28 14.14\r\n 6 其他资产 126，679.71 0.06\r\n 7 合计 206，254，510.02 100.00\r\n （二）报告期末按行业分类的股票投资组合\r\n 代码 行业类别 公允价值（元） 占基金资产净值比例(%)\r\n A 农、林、牧、渔业 6，794，526.30 3.33\r\n B 采矿业 741，500.00 0.36\r\n C 制造业 110，889，618.11 54.36\r\n D 电力、热力、燃气及水生产和供应业 － －\r\n E 建筑业 11，685，200.00 5.73\r\n F 批发和零售业 8，381，791.03 4.11\r\n G 交通运输、仓储和邮政业 － －H 住宿和餐饮业 － －\r\n I 信息传输、软件和信息技术服务业 9，320，428.92 4.57J 金融业 16，281，234.67 7.98\r\n K 房地产业 12，864，000.00 6.31\r\n L 租赁和商务服务业 － －\r\n M 科学研究和技术服务业 － －\r\n N 水利、环境和公共设施管理业 － －O 居民服务、修理和其他服务业 － －P 教育 － －\r\n Q 卫生和社会工作 － －\r\n R 文化、体育和娱乐业 － －S 综合 － －\r\n 合计 176，958，299.03 86.74\r\n （三）报告期末按公允价值占基金资产净值比例大小排序的前十名股票投资明细\r\n 序号 股票代码 股票名称 数量（股） 公允价值(元) 占基金资产净值比例(%)\r\n 1 300196 长海股份 447，470 9，714，573.70 4.76\r\n 2 601126 四方股份 590，000 8，525，500.00 4.18\r\n 3 600352 浙江龙盛 799，962 7，503，643.56 3.68\r\n 4 300124 汇川技术 200，000 6，998，000.00 3.43\r\n 5 002010 传化股份 815，723 6，599，199.07 3.23\r\n 6 600079 人福医药 250，000 6，520，000.00 3.20\r\n 7 600585 海螺水泥 479，983 6，422，172.54 3.15\r\n 8 600406 国电南瑞 429，899 6，418，392.07 3.15\r\n 9 601601 中国太保 392，419 6，251，234.67 3.06\r\n 10 002305 南国置业 700，000 5，775，000.00 2.83\r\n （四）报告期末按债券品种分类的债券投资组合本基金本报告期末未持有债券。\r\n （五）报告期末按公允价值占基金资产净值比例大小排名的前五名债券投资明细本基金本报告期末未持有债券。\r\n （六）报告期末按公允价值占基金资产净值比例大小排名的前十名资产支持证券投资明细本基金本报告期末未持有资产支持证券。\r\n （七）报告期末按公允价值占基金资产净值比例大小排名的前五名权证投资明细本基金本报告期末未持有权证。\r\n （八）报告期末本基金投资的股指期货交易情况说明本基金本报告期末未投资股指期货。\r\n （九）投资组合报告附注\r\n 1、报告期内本基金投资的前十名证券的发行主体未出现被监管部门立案调\r\n 查或在报告编制日前一年内受到公开谴责、处罚的情形。\r\n 2、本基金投资的前十名股票中，不存在超出基金合同规定备选股票库的情形。\r\n 3、其他资产构成\r\n 序号 名称 金额\r\n 1 存出保证金 118，197.90\r\n 2 应收证券清算款 －\r\n 3 应收股利 －\r\n 4 应收利息 6，210.59\r\n 5 应收申购款 2，271.22\r\n 6 其他应收款 －\r\n 7 待摊费用 －\r\n 8 其他 －\r\n 9 合计 126，679.71\r\n 4、报告期末持有的处于转股期的可转换债券明细。\r\n 本基金本报告期末未持有处于转股期的可转换债券。\r\n 、报告期末前十名股票中存在流通受限情况的说明本基金本报告期末前十名股票中不存在流通受限情况。\r\n 6、由于四舍五入的原因，分项之和与合计项可能存在尾差。\r\n 十一、基金的业绩\r\n 基金管理人依照恪尽职守、诚实信用、谨慎勤勉的原则管理和运用基金财产，但不保证基金一定盈利，也不保证最低收益。基金的过往业绩并不代表其未来表现。投资有风险，投资者在做出投资决策前应仔细阅读本基金的招募说明书。\r\n 2013 年 2 季度及历史各时间段基金份额净值增长率与同期业绩比较基准收\r\n 益率比较：\r\n 阶段 净值增长率①净值增长率标\r\n 准差②业绩比较基准收益\r\n 率③业绩比较基准收益率标\r\n 准差④\r\n ①-③ ②-④2009年7月30日（合同生效日）-2009 年\r\n 12 月 31 日\r\n -2.20% 1.56% 1.30% 1.54% -3.50% 0.02%\r\n 2010 年 1月 1日\r\n -2010 年 12 月 31 日 -6.75% 1.38% -7.46% 1.11% 0.71% 0.27%\r\n 2011 年 1月 1日\r\n -2011 年 12 月 31 日 -25.11% 1.32% -16.91% 0.91% -8.20% 0.41%\r\n 2012 年 1月 1日\r\n -2012 年 12 月 31 日 3.51% 1.23% 6.72% 0.90% -3.21% 0.33%\r\n 2013 年 1月 1日\r\n -2013 年 6月 30 日 4.81% 1.38% -8.41% 1.05% 13.22% 0.33%自基金合同生效日起至今（2009 年 07\r\n 月 30 日-2013 年 6\r\n 月 30 日）\r\n -25.90% 1.35% -23.87% 1.06% -2.03% 0.29%\r\n 自合同生效之日（2009 年 7 月 30 日）至 2013 年 6 月 30 日期间，本基金份额累计净值增长率与业绩比较基准收益率历史走势对比：\r\n 长长长长长长长长 基基基基\r\n 2009-07-30 2010-02-23 2010-09-08 2011-04-08 2011-10-27 2012-05-18 2012-11-30 2013-06-30\r\n 0%\r\n -5%\r\n -10%\r\n -15%\r\n -20%\r\n -25%\r\n -30%\r\n -35%\r\n 注：1、本基金基金合同生效日为2009年7月30日，图示日期为2009年7月30日至\r\n 2013年6月30日。\r\n 2、按基金合同规定，本基金自合同生效日起6个月内为建仓期，建仓期结束时，本基金的各项投资比例符合基金合同中关于投资范围、资产配置比例和投资限制的有关规定：投资于股票资产的比例范围是基金资产的60%-95%，投资于现金、债券资产以及中国证监会允许基金投资的其他证券品种的比例范围是基金资\r\n 产的5%-40%，现金及到期日在一年以内的政府债券不低于基金资产净值的5%。本\r\n 基金投资优势产业的资产不低于股票资产的80%。\r\n 十二、基金的财产\r\n （一）基金资产总值\r\n 基金资产总值是指基金拥有的各类有价证券、银行存款本息和基金应收申购款以及其他资产的价值总和。\r\n 其构成主要有：\r\n 1、银行存款及其应计利息；\r\n 2、清算备付金及其应计利息；\r\n 3、根据有关规定缴纳的保证金；\r\n 4、应收证券交易清算款；\r\n 5、应收申购款；\r\n 6、股票投资及其估值调整；\r\n 7、债券投资及其估值调整和应计利息；\r\n 8、其他投资及其估值调整；\r\n 9、其他资产等。\r\n （二）基金资产净值基金资产净值是指基金资产总值减去基金负债后的价值。\r\n 其构成主要有：\r\n 1、基金份额持有人申购基金份额所支付的款项；\r\n 2、运用基金资产所获得收益（亏损）；\r\n 3、以前年度实现的尚未分配的收益或尚未弥补的亏损。\r\n （三）基金财产的账户\r\n 本基金财产以基金名义开立银行存款账户，以基金托管人的名义开立证券交易清算资金的结算备付金账户，以基金托管人和本基金联名的方式开立基金证券账户，以本基金的名义开立银行间债券托管账户。开立的基金专用账户与基金管理人、基金托管人、基金销售机构和注册登记机构固有的财产账户以及其他基金财产账户相独立。\r\n （四）基金财产的保管和处分\r\n 基金财产独立于基金管理人、基金托管人和代销机构的固有财产，并由基金托管人保管。基金管理人、基金托管人因基金财产的管理、运用或者其他情形而取得的财产和收益归入基金财产。基金管理人、基金托管人可以按基金合同的约定收取管理费、托管费以及其他基金合同约定的费用。基金管理人、基金托管人以其固有资产承担法律责任，其债权人不得对基金财产行使请求冻结、扣押和其他权利。\r\n 基金管理人、基金托管人因依法解散、被依法撤销或者被依法宣告破产等原因进行清算的，基金财产不属于其清算财产。\r\n 基金财产的债权，不得与基金管理人、基金托管人固有财产的债务相抵销；\r\n 不同基金财产的债权债务，不得相互抵销。\r\n 除依据《基金法》、基金合同及其他有关规定处分外，基金财产不得被处分。\r\n 非因基金财产本身承担的债务，不得对基金财产强制执行。\r\n 十三、基金资产的估值\r\n （一）估值目的\r\n 基金资产估值的目的是客观、准确地反映基金资产是否保值、增值。依据经基金资产估值后确定的基金资产净值而计算出的基金份额净值，是计算基金申购与赎回价格的基础。\r\n （二）估值日\r\n 本基金的估值日为相关的证券交易场所的正常营业日，以及国家法律法规规定需要对外披露基金净值的非营业日。\r\n （三）估值对象\r\n 基金所拥有的股票、权证、债券和银行存款本息、应收款项、其它投资等资产。\r\n （四）估值方法\r\n 1、股票估值方法\r\n （1）上市流通股票按估值日其所在证券交易所的收盘价估值；估值日无交易的，以最近交易日的收盘价估值。\r\n （2）未上市股票的估值\r\n 1）首次发行未上市的股票，按成本计量；\r\n 2）送股、转增股、配股和公开增发新股等发行未上市的股票，按估值日在证券交易所挂牌的同一股票的市价估值；\r\n 3）首次公开发行有明确锁定期的股票，同一股票在交易所上市后，按交易所上市的同一股票的市价估值；\r\n 4）非公开发行有明确锁定期的流通受限股票，按监管机构或行业协会有关规定确定公允价值；\r\n （3）在任何情况下，基金管理人如采用本项第（1）－（2）小项规定的方\r\n 法对基金资产进行估值，均应被认为采用了适当的估值方法。但是，如果基金管理人认为按本项第（1）－（2）小项规定的方法对基金资产进行估值不能客观反\r\n 映其公允价值的，基金管理人可根据具体情况，并与基金托管人商定后，按最能反映公允价值的价格估值；\r\n （4）国家有最新规定的，按其规定进行估值。\r\n 2、债券估值方法\r\n （1）证券交易所市场实行净价交易的债券按估值日收盘价估值，估值日没有交易的，按最近交易日的收盘价估值；\r\n （2）证券交易所市场未实行净价交易的债券按估值日收盘价减去债券收盘价\r\n 中所含的应收利息得到的净价进行估值，估值日没有交易的，以最近交易日的收盘净价估值；\r\n （3）发行未上市债券采用估值技术确定公允价值，在估值技术难以可靠计量\r\n 公允价值的情况下，按成本进行后续计量；\r\n （4）在全国银行间债券市场交易的债券、资产支持证券等固定收益品种，采用估值技术确定公允价值；\r\n （5）同一债券同时在两个或两个以上市场交易的，按债券所处的市场分别估值；\r\n （6）在任何情况下，基金管理人如采用本项第（1）-（5）小项规定的方法\r\n 对基金资产进行估值，均应被认为采用了适当的估值方法。但是，如果基金管理人认为按本项第（1）-（5）小项规定的方法对基金资产进行估值不能客观反映\r\n 其公允价值的，基金管理人在综合考虑市场成交价、市场报价、流动性、收益率曲线等多种因素基础上形成的债券估值，基金管理人可根据具体情况与基金托管人商定后，按最能反映公允价值的价格估值；\r\n （7）国家有最新规定的，按其规定进行估值。\r\n 3、权证估值方法\r\n （1）基金持有的权证，从持有确认日起到卖出日或行权日止，上市交易的权\r\n 证按估值日在证券交易所挂牌的该权证的收盘价估值；估值日没有交易的，按最近交易日的收盘价估值；\r\n 未上市交易的权证采用估值技术确定公允价值；在估值技术难以可靠计量公\r\n 允价值的情况下，按成本计量；\r\n （2）在任何情况下，基金管理人如采用本项第（1）小项规定的方法对基金\r\n 资产进行估值，均应被认为采用了适当的估值方法。但是，如果基金管理人认为按本项第（1）小项规定的方法对基金资产进行估值不能客观反映其公允价值的，基金管理人可根据具体情况，并与基金托管人商定后，按最能反映公允价值的价格估值；\r\n （3）国家有最新规定的，按其规定进行估值。\r\n 4、如基金管理人或基金托管人发现基金估值违反基金合同订明的估值方法、程序及相关法律法规的规定或者未能充分维护基金份额持有人利益时，应立即通知对方，共同查明原因，双方协商解决。\r\n 5、根据有关法律法规，基金资产净值计算和基金会计核算的义务由基金管理人承担。本基金的基金会计责任方由基金管理人担任，因此，就与本基金有关的会计问题，如经相关各方在平等基础上充分讨论后，仍无法达成一致的意见，按照基金管理人对基金资产净值的计算结果对外予以公布。\r\n （五）估值程序\r\n 1、基金份额净值是按照每个开放日闭市后，基金资产净值除以当日基金份\r\n 额的余额数量计算，精确到 0.001 元，小数点后第四位四舍五入。国家另有规定的，从其规定。\r\n 每个工作日计算基金资产净值及基金份额净值，并按规定公告。\r\n 2、基金管理人应每个工作日对基金资产估值。基金管理人每个开放日对基\r\n 金资产估值后，将基金份额净值结果发送基金托管人，经基金托管人复核无误后，由基金管理人对外公布。月末、年中和年末估值复核与基金会计账目的核对同时进行。基金日常估值由基金管理人进行。\r\n （六）估值错误的处理\r\n 基金管理人和基金托管人将采取必要、适当、合理的措施确保基金资产估值的准确性、及时性。当基金份额净值小数点后 3 位以内（含第 3 位）发生差错时，视为基金份额净值错误。\r\n 本基金合同的当事人应按照以下约定处理：\r\n 、差错类型\r\n 本基金运作过程中，如果由于基金管理人或基金托管人、或注册登记机构、或代销机构、或投资人自身的行为造成差错，导致其他当事人遭受损失的，差错的责任人应当对由于该差错遭受损失的当事人（“受损方”）按下述“差错处理原则”给予赔偿承担赔偿责任。上述差错的主要类型包括但不限于：资料申报差错、数据传输差错、数据计算差错、系统故障差错、下达指令差错等；对于因技术原因引起的差错，若系同行业现有技术水平不能预见、不能避免、不能抗拒，则属不可抗力，按照下述规定执行。\r\n 由于不可抗力原因造成投资人的交易资料灭失或被错误处理或造成其他差错，因不可抗力原因出现差错的当事人不对其他当事人承担赔偿责任，但因该差错取得不当得利的当事人仍应负有返还不当得利的义务。\r\n 2、差错处理原则\r\n （1）差错已发生，但尚未给当事人造成损失时，差错责任方应及时协调各方，及时进行更正，因更正差错发生的费用由差错责任方承担；由于差错责任方未及时更正已产生的差错，给当事人造成损失的，由差错责任方对直接损失承担赔偿责任；若差错责任方已经积极协调，并且有协助义务的当事人有足够的时间进行更正而未更正，则其应当承担相应赔偿责任。差错责任方应对更正的情况向有关当事人进行确认，确保差错已得到更正。\r\n （2）差错的责任方对可能导致有关当事人的直接损失负责，不对间接损失负责，并且仅对差错的有关直接当事人负责，不对第三方负责。\r\n （3）因差错而获得不当得利的当事人负有及时返还不当得利的义务。但差错\r\n 责任方仍应对差错负责，如果由于获得不当得利的当事人不返还或不全部返还不当得利造成其他当事人的利益损失（“受损方”），则差错责任方应赔偿受损方的损失，并在其支付的赔偿金额的范围内对获得不当得利的当事人享有要求交付不当得利的权利；如果获得不当得利的当事人已经将此部分不当得利返还给受损方，则受损方应当将其已经获得的赔偿额加上已经获得的不当得利返还的总和超过其实际损失的差额部分支付给差错责任方。\r\n （4）差错调整采用尽量恢复至假设未发生差错的正确情形的方式。\r\n （5）差错责任方拒绝进行赔偿时，如果因基金管理人的行为造成基金资产损失时，基金托管人应为基金的利益向基金管理人追偿，如果因基金托管人的行为造成基金资产损失时，基金管理人应为基金的利益向基金托管人追偿。除基金管理人和托管人之外的第三方造成基金资产的损失，并拒绝进行赔偿时，由基金管理人负责向差错方追偿；追偿过程中产生的有关费用，应列入基金费用，从基金资产中支付。\r\n （6）如果出现差错的当事人未按规定对受损方进行赔偿，并且依据法律法规、基金合同或其他规定，基金管理人自行或依据法院判决、仲裁裁决对受损方承担了赔偿责任，则基金管理人有权向有责任的当事人进行追索，并有权要求其赔偿或补偿由此发生的费用和遭受的损失。\r\n （7）按法律法规规定的其他原则处理差错。\r\n 3、差错处理程序\r\n 差错被发现后，有关的当事人应当及时进行处理，处理的程序如下：\r\n （1）查明差错发生的原因，列明所有的当事人，并根据差错发生的原因确定差错的责任方；\r\n （2）根据差错处理原则或当事人协商的方法对因差错造成的损失进行评估；\r\n （3）根据差错处理原则或当事人协商的方法由差错的责任方进行更正和赔偿损失；\r\n （4）根据差错处理的方法，需要修改基金注册登记机构交易数据的，由基\r\n 金注册登记机构进行更正，并就差错的更正向有关当事人进行确认；\r\n （5）当错误达到或超过基金资产净值的 0.25%时，基金管理公司应当及时\r\n 通知基金托管人并报中国证监会；错误偏差达到基金份额净值的 0.5%时，基金管理人应当公告、通报基金托管人并报中国证监会备案；\r\n 4、基金份额净值差错处理的原则和方法如下：\r\n （1）当基金份额净值后 3 位以内（含第 3 位）发生差错时，视为基金份额\r\n 净值错误；基金份额净值出现错误时，基金管理人应当立即予以纠正，通报基金托管人，并采取合理的措施防止损失进一步扩大；当错误达到或超过基金资产净\r\n 值的 0.25%时，基金管理公司应当及时通知基金托管人并报中国证监会；错误偏\r\n 差达到基金份额净值的 0.5%时，基金管理人应当公告、通报基金托管人并报中国证监会备案；当发生净值计算错误时，由基金管理人负责处理，由此给基金份额持有人和基金造成损失的，应由基金管理人先行赔付，基金管理人按差错情形，有权向其他当事人追偿。\r\n （2）当基金份额净值计算差错给基金和基金份额持有人造成损失需要进行赔偿时，基金管理人和基金托管人应根据实际情况界定双方承担的责任，经确认后按以下条款进行赔偿：\r\n 1）本基金的基金会计责任方由基金管理人担任，与本基金有关的会计问题，如经双方在平等基础上充分讨论后，尚不能达成一致时，按基金会计责任方的建议执行，由此给基金份额持有人和基金造成的损失，由基金管理人负责赔付；\r\n 2）若基金管理人计算的基金份额净值已由基金托管人复核确认后公告，而\r\n 且基金托管人未对计算过程提出疑义或要求基金管理人书面说明，份额净值出错且造成基金份额持有人损失的，应根据法律法规的规定对投资人或基金支付赔偿金，就实际向投资人或基金支付的赔偿金额，其中基金管理人承担 50%，基金托管人承担 50%；\r\n 3）如基金管理人和基金托管人对基金份额净值的计算结果，虽然多次重新\r\n 计算和核对，尚不能达成一致时，为避免不能按时公布基金份额净值的情形，以基金管理人的计算结果对外公布，由此给基金份额持有人和基金造成的损失，由基金管理人负责赔付；\r\n 4）由于基金管理人提供的信息错误（包括但不限于基金申购或赎回金额等），进而导致基金份额净值计算错误而引起的基金份额持有人和基金的损失，由基金管理人负责赔付；\r\n （3）由于证券交易所及登记结算公司发送的数据错误，有关会计制度变化\r\n 或由于其他不可抗力原因，基金管理人和基金托管人虽然已经采取必要、适当、合理的措施进行检查，但是未能发现该错误而造成的基金份额净值计算错误，基金管理人、基金托管人可以免除赔偿责任。但基金管理人、基金托管人应积极采取必要的措施消除由此造成的影响；\r\n （4）基金管理人和基金托管人由于各自技术系统设置而产生的净值计算尾差，以基金管理人计算结果为准；\r\n （5）前述内容如法律法规或者监管部门另有规定的，从其规定。如果行业\r\n 有通行做法，双方当事人应本着平等和保护基金份额持有人利益的原则进行协商。\r\n （七）基金净值的确认\r\n 用于基金信息披露的基金资产净值和基金份额净值由基金管理人负责计算，基金托管人负责进行复核。基金管理人应于每个开放日交易结束后计算当日的基金资产净值并发送给基金托管人。基金托管人对净值计算结果复核确认后发送给基金管理人，由基金管理人对基金净值予以公布。\r\n （八）暂停估值的情形\r\n 1、与本基金投资有关的证券交易场所遇法定节假日或因其他原因暂停营业时；\r\n 2、因不可抗力或其他情形致使基金管理人、基金托管人无法准确评估基金资产价值时；\r\n 3、占基金相当比例的投资品种的估值出现重大转变，而基金管理人为保障\r\n 投资人的利益，已决定延迟估值；\r\n 4、中国证监会和基金合同认定的其他情形。\r\n （九）特殊情形的处理\r\n 1、基金管理人或基金托管人按股票估值方法的第（3）项、债券估值方法的\r\n 第（6）项或权证估值方法的第（2）项进行估值时，所造成的误差不作为基金资产估值错误处理。\r\n 2、由于不可抗力原因，或由于证券交易所及其登记结算公司发送的数据错误，或国家会计政策变更、市场规则变更等，基金管理人和基金托管人虽然已经采取必要、适当、合理的措施进行检查，但是未能发现该错误的，由此造成的基金资产估值错误，基金管理人和基金托管人可以免除赔偿责任。但基金管理人应当积极采取必要的措施消除由此造成的影响。\r\n 十四、基金收益与分配\r\n （一）收益的构成\r\n 本基金合同项下基金收益指基金利润，具体包括：\r\n 1、买卖证券差价；\r\n 2、基金投资所得红利、股息、债券利息；\r\n 3、银行存款利息；\r\n 4、已实现的其他合法收入；\r\n 5、持有期间产生的公允价值变动；\r\n 因运用基金财产带来的成本或费用的节约应计入收益。\r\n （二）收益分配原则\r\n 1、本基金的每份基金份额享有同等分配权；\r\n 2、收益分配时所发生的银行转账或其他手续费用由投资人自行承担。当投\r\n 资人的现金红利小于一定金额，不足以支付银行转账或其他手续费用时，基金注册登记机构可将投资人的现金红利按红利发放日的基金份额净值自动转为基金份额；\r\n 3、本基金收益每年最多分配 12 次，每次基金收益分配比例不低于期末可供分配利润的 10%（期末可供分配利润是指期末资产负债表中未分配利润与未分配利润中已实现收益的孰低数），全年累计基金收益分配比例不低于期末可供分配利润的 30%；\r\n 4、若基金合同生效不满 3 个月则可不进行收益分配；\r\n 5、本基金收益分配方式分为两种：现金分红与红利再投资，投资人可选择现金红利或将现金红利按红利发放日的基金份额净值自动转为基金份额进行再投资；若投资人不选择，本基金默认的收益分配方式是现金分红；\r\n 6、基金红利发放日距离收益分配基准日（即期末可供分配利润计算截止日）\r\n 不得超过 15 个工作日；\r\n 7、基金收益分配基准日的基金份额净值减去每单位基金份额收益分配金额后不能低于面值；\r\n 、法律法规或监管机构另有规定的从其规定。\r\n （三）收益分配方案\r\n 本基金收益方案中应载明基金收益分配对象、分配原则、分配时间、分配数额及比例、分配方式、支付方式等内容。\r\n （四）收益分配方案的确定、公告与实施\r\n 本基金收益分配方案由基金管理人拟定，由基金托管人复核，2 日内指定媒体及基金管理人网站公告并报中国证监会备案。\r\n （五）收益分配中发生的费用\r\n 1、收益分配采用红利再投资方式免收再投资的费用。\r\n 2、收益分配时发生的银行转账等手续费用由基金份额持有人自行承担；如\r\n 果基金份额持有人所获现金红利不足支付前述银行转账等手续费用，注册登记机构自动将该基金份额持有人的现金红利按除权日的基金份额净值转为基金份额。\r\n 十五、基金费用与税收\r\n （一）与基金运作有关的费用\r\n 1、与基金运作有关费用包括：\r\n （1）基金管理人的管理费；\r\n （2）基金托管人的托管费；\r\n （3）基金财产拨划支付的银行费用；\r\n （4）基金合同生效后的信息披露费用；\r\n （5）基金份额持有人大会费用；\r\n （6）基金合同生效后与基金相关的会计师费、律师费；\r\n （7）基金的证券交易费用；\r\n （8）在中国证监会规定允许的前提下，本基金可以从基金财产中计提销售服务费；\r\n （9）依法可以在基金财产中列支的其他费用。\r\n 基金合同终止、本基金财产清算时所发生费用，按实际支出额从基金财产总值中扣除。\r\n 2、基金费用计提方法、计提标准和支付方式\r\n （1）基金管理费\r\n 本基金的管理费按前一日本基金资产净值的 1.5%年费率计提，计算方法如下：\r\n H＝E×1.5%÷当年天数\r\n H 为每日应计提的基金管理费\r\n E 为前一日的基金资产净值\r\n 基金管理费每日计算，逐日累计至每月月末，按月支付，由基金管理人向基金托管人发送基金管理费划款指令，基金托管人复核后于次月首日起 3 个工作日内从基金财产中一次性支付给基金管理人。若遇法定节假日、公休假等，支付日期顺延。\r\n （2）基金托管费\r\n 本基金的托管费按前一日基金资产净值的 0.25%的年费率计提，计算方法如下：\r\n H=E×0.25%÷当年天数\r\n H 为每日应支付的基金托管费\r\n E 为前一日的基金资产净值\r\n 基金托管费每日计算，逐日累计至每月月末，按月支付，由基金管理人向基金托管人发送基金托管费划款指令，基金托管人复核后于次月首日起 3 个工作日内从基金财产中一次性支取。若遇法定节假日、公休假等，支付日期顺延。\r\n （3）上述“1、与基金运作有关费用列示”中（3）－（9）项费用由基金托\r\n 管人根据有关法律法规及相应协议规定，按费用支出金额支付，列入或摊入当期基金费用。\r\n 3、不列入基金费用的项目基金管理人和基金托管人因未履行或未完全履行义务导致的费用支出或基\r\n 金财产的损失，以及处理与基金运作无关的事项发生的费用等不列入基金费用。\r\n 基金募集期间所发生的信息披露费、律师费和会计师费以及其他费用不从基金财产中支付。\r\n 4、基金管理费和托管费的调整基金管理人和基金托管人可根据基金发展情况调整基金管理费率或基金托管费率。调高基金管理费率或基金托管费率，须召开基金份额持有人大会审议；\r\n 调低基金管理费率或基金托管费率，无须召开基金份额持有人大会。基金管理人必须最迟于新的费率实施日前 2 日在至少一种中国证监会指定媒体及基金管理人网站公告并报中国证监会备案。\r\n （二）与基金销售有关的费用\r\n 1、申购费用\r\n 基金申购费的费率水平、计算公式、收取方式和使用方式参见本招募说明书\r\n “八、基金份额的申购与赎回”相应部分。\r\n 2、赎回费用\r\n 基金赎回费的费率水平、计算公式、收取方式和使用方式参见本招募说明书\r\n “八、基金份额的申购与赎回”相应部分。\r\n 、转换费用\r\n 基金转换费用的费率水平、计算公式、收取方式和使用方式参见本招募说明\r\n 书“八、基金份额的申购与赎回”相应部分。\r\n （三）基金税收\r\n 基金和基金份额持有人根据国家法律法规的规定，履行纳税义务。\r\n 十六、基金的会计与审计\r\n （一）基金的会计政策\r\n 1、基金管理人为本基金的基金会计责任方；\r\n 2、基金的会计年度为公历年度的 1 月 1 日至 12 月 31 日；\r\n 3、基金核算以人民币为记帐本位币，以人民币元为记帐单位；\r\n 4、会计制度执行国家有关的会计制度；\r\n 5、本基金独立建账、独立核算；\r\n 6、基金管理人及基金托管人各自保留完整的会计账目、凭证并进行日常的会计核算，按照有关规定编制基金会计报表；\r\n 7、基金托管人每月与基金管理人就基金的会计核算、报表编制等进行核对并以书面方式确认。\r\n （二）基金的审计\r\n 1、本基金管理人聘请具有从事证券相关业务资格的会计师事务所及其注册会计师对本基金年度财务报表及其他规定事项进行审计。会计师事务所及其注册会计师与基金管理人、基金托管人相互独立。\r\n 2、会计师事务所更换经办注册会计师，应事先征得基金管理人同意。\r\n 3、基金管理人（或基金托管人）认为有充足理由更换会计师事务所，须经\r\n 基金托管人（或基金管理人）同意，并报中国证监会备案后可以更换。就更换会计师事务所，基金管理人应当依照《信息披露办法》的有关规定在指定媒体及基金管理人网站上公告。\r\n 十七、基金的信息披露\r\n 本基金的信息披露应符合《基金法》、《运作办法》、《信息披露办法》、基金合同及其他有关规定。\r\n 本基金信息披露义务人按照法律法规和中国证监会的规定披露基金信息，并保证所披露信息的真实性、准确性和完整性。\r\n 本基金信息披露义务人包括基金管理人、基金托管人、召集基金份额持有人大会的基金份额持有人等法律法规和中国证监会规定的自然人、法人和其他组织。\r\n 本基金信息披露义务人应当在中国证监会规定时间内，将应予披露的基金信息通过中国证监会指定的全国性报刊（以下简称“指定报刊”）和基金管理人、基金托管人的网站等媒介披露，并保证基金投资人能够按照基金合同约定的时间和方式查阅或者复制公开披露的信息资料。\r\n 本基金信息披露义务人承诺公开披露的基金信息，不得有下列行为：\r\n 1、虚假记载、误导性陈述或者重大遗漏；\r\n 2、对证券投资业绩进行预测；\r\n 3、违规承诺收益或者承担损失；\r\n 4、诋毁其他基金管理人、基金托管人或者基金份额发售机构；\r\n 5、登载任何自然人、法人或者其他组织的祝贺性、恭维性或推荐性的文字；\r\n 6、中国证监会禁止的其他行为。\r\n 本基金公开披露的信息应采用中文文本。如同时采用外文文本的，基金信息披露义务人应保证两种文本的内容一致。两种文本发生歧义的，以中文文本为准。\r\n 本基金公开披露的信息采用阿拉伯数字；除特别说明外，货币单位为人民币元。\r\n （一）公开披露的基金信息\r\n 公开披露的基金信息包括：\r\n 1、基金招募说明书、基金合同、基金托管协议\r\n 基金募集申请经中国证监会核准后，基金管理人在基金份额发售的 3 日前，将基金招募说明书、基金合同摘要登载在指定报刊和网站上；基金管理人、基金托管人应当将基金合同、基金托管协议登载在网站上。\r\n （1）基金招募说明书应当最大限度地披露影响基金投资人决策的全部事项，说明基金认购、申购和赎回安排、基金投资、基金产品特性、风险揭示、信息披露及基金份额持有人服务等内容。基金合同生效后，基金管理人在每 6 个月结束之日起 45 日内，更新招募说明书并登载在网站上，将更新后的招募说明书摘要登载在指定报刊上；基金管理人在公告的 15 日前向中国证监会报送更新的招募说明书，并就有关更新内容提供书面说明。\r\n （2）基金合同是界定基金合同当事人的各项权利、义务关系，明确基金份\r\n 额持有人大会召开的规则及具体程序，说明基金产品的特性等涉及基金投资人重大利益的事项的法律文件。\r\n （3）基金托管协议是界定基金托管人和基金管理人在基金财产保管及基金\r\n 运作监督等活动中的权利、义务关系的法律文件。\r\n 2、基金份额发售公告\r\n 基金管理人应当就基金份额发售的具体事宜编制基金份额发售公告，并在披露招募说明书的当日登载于指定报刊和网站上。\r\n 3、基金合同生效公告基金管理人应当在基金合同生效的次日在指定报刊和网站上登载基金合同生效公告。基金合同生效公告中将说明基金募集情况。\r\n 4、基金开始申购、赎回公告\r\n 基金管理人应于申购开始日、赎回开始日前 2 日在指定报刊及网站上公告。\r\n 5、基金资产净值、基金份额净值\r\n 基金合同生效后，在开始办理基金份额申购或者赎回前，基金管理人应当至少每周公告一次基金资产净值和基金份额净值。\r\n 在开始办理基金份额申购或者赎回后，基金管理人应当在每个开放日的次日，通过网站、基金份额发售网点以及其他媒介，披露开放日的基金份额净值和基金份额累计净值。\r\n 基金管理人应当公告半年度和年度最后一个市场交易日基金资产净值和基金份额净值。基金管理人应当在前款规定的市场交易日的次日，将基金资产净值、基金份额净值和基金份额累计净值登载在指定报刊和网站上。\r\n 基金管理人应当在基金合同、招募说明书等信息披露文件上载明基金份额申购、赎回价格的计算方式及有关申购、赎回费率，并保证投资人能够在基金份额发售网点查阅或者复制前述信息资料。\r\n 6、基金定期报告，包括基金年度报告、基金半年度报告和基金季度报告\r\n 基金管理人应当在每年结束之日起 90 日内，编制完成基金年度报告，并将年度报告正文登载于网站上，将年度报告摘要登载在指定报刊上。基金年度报告的财务会计报告应当经过审计。\r\n 基金管理人应当在上半年结束之日起 60 日内，编制完成基金半年度报告，并将半年度报告正文登载在网站上，将半年度报告摘要登载在指定报刊上。\r\n 基金管理人应当在每个季度结束之日起 15 个工作日内，编制完成基金季度报告，并将季度报告登载在指定报刊和网站上。\r\n 基金合同生效不足 2 个月的，基金管理人可以不编制当期季度报告、半年度报告或者年度报告。\r\n 基金定期报告在公开披露的第 2 个工作日，分别报中国证监会和基金管理人主要办公场所所在地中国证监会派出机构备案。报备应当采用电子文本和书面报告两种方式。\r\n 7、临时报告\r\n 本基金发生重大事件，有关信息披露义务人应当在 2 日内编制临时报告书，予以公告，并在公开披露日分别报中国证监会和基金管理人主要办公场所所在地中国证监会派出机构备案。\r\n 前款所称重大事件，是指可能对基金份额持有人权益或者基金份额的价格产生重大影响的下列事件：\r\n （1）基金份额持有人大会的召开及决议；\r\n （2）终止《基金合同》；\r\n （3）转换基金运作方式；\r\n （4）更换基金管理人、基金托管人；\r\n （5）基金管理人、基金托管人的法定名称、住所发生变更；\r\n （6）基金管理人股东及其出资比例发生变更；\r\n （7）基金募集期延长；\r\n （8）基金管理人的董事长、总经理及其他高级管理人员、基金经理和基金托管人基金托管部门负责人发生变动；\r\n （9）基金管理人的董事在一年内变更超过 50%；\r\n （10）基金管理人、基金托管人基金托管部门的主要业务人员在一年内变动\r\n 超过 30%；\r\n （11）涉及基金管理人、基金财产、基金托管业务的诉讼；\r\n （12）基金管理人、基金托管人受到监管部门的调查；\r\n （13）基金管理人及其董事、总经理及其他高级管理人员、基金经理受到严\r\n 重行政处罚，基金托管人及其基金托管部门负责人受到严重行政处罚；\r\n （14）重大关联交易事项；\r\n （15）基金收益分配事项；\r\n （16）管理费、托管费等费用计提标准、计提方式和费率发生变更；\r\n （17）基金份额净值计价错误达基金份额净值 0.5%；\r\n （18）基金改聘会计师事务所；\r\n （19）基金变更、增加或减少代销机构；\r\n （20）基金更换注册登记机构；\r\n （21）本基金开始办理申购、赎回；\r\n （22）本基金申购、赎回费率及其收费方式发生变更；\r\n （23）本基金发生巨额赎回并延期支付；\r\n （24）本基金连续发生巨额赎回并暂停接受赎回申请；\r\n （25）本基金暂停接受申购、赎回申请后重新接受申购、赎回；\r\n （26）中国证监会或基金合同规定的其他事项。\r\n 8、澄清公告\r\n 在基金合同存续期限内，任何公共媒体中出现的或者在市场上流传的消息可能对基金份额价格产生误导性影响或者引起较大波动的，相关信息披露义务人知悉后应当立即对该消息进行公开澄清，并将有关情况立即报告中国证监会。\r\n 9、基金份额持有人大会决议\r\n 基金份额持有人大会决定的事项，应当依法报中国证监会核准或者备案，并予以公告。召开基金份额持有人大会的，召集人应当至少提前 30 日公告基金份额持有人大会的召开时间、会议形式、审议事项、议事程序和表决方式等事项。\r\n 基金份额持有人依法自行召集基金份额持有人大会，基金管理人、基金托管人对基金份额持有人大会决定的事项不依法履行信息披露义务的，召集人应当履行相关信息披露义务。\r\n 10、中国证监会规定的其他信息。\r\n 法律法规或中国证监会另有规定的，从其规定。\r\n （二）信息披露事务管理\r\n 基金管理人、基金托管人应当建立健全信息披露管理制度，指定专人负责管理信息披露事务。\r\n 基金信息披露义务人公开披露基金信息，应当符合中国证监会相关基金信息披露内容与格式准则的规定。\r\n 基金托管人应当按照相关法律法规、中国证监会的规定和基金合同的约定，对基金管理人编制的基金资产净值、基金份额净值、基金份额申购赎回价格、基金定期报告和定期更新的招募说明书等公开披露的相关基金信息进行复核、审查，并向基金管理人出具书面文件或者盖章确认。\r\n 基金管理人、基金托管人应当在指定报刊中选择披露信息的报刊。\r\n 基金管理人、基金托管人除依法在指定报刊和网站上披露信息外，还可以根据需要在其他公共媒体披露信息，但是其他公共媒体不得早于指定报刊和网站披露信息，并且在不同媒介上披露同一信息的内容应当一致。\r\n （三）信息披露文件的存放与查阅\r\n 基金合同可印制成册，供投资人在基金管理人、基金托管人、代销机构的办公场所和营业场所查阅；投资人也可按工本费购买基金合同复制件，但内容应以基金合同正本为准。\r\n 招募说明书公布后，应当分别置备于基金管理人、基金托管人和基金份额发售机构的住所，供公众查阅、复制。\r\n 基金定期报告公布后，应当分别置备于基金管理人和基金托管人的住所，供公众查阅、复制。基金管理人和基金托管人应保证文本的内容与所公告的内容完全一致。\r\n 投资人在支付工本费后，可在合理时间内取得上述文件复制件。\r\n 十八、风险揭示\r\n 基金份额持有人须了解并承受以下风险：\r\n （一）风险揭示\r\n 1、市场风险\r\n 证券市场价格因受经济因素、政治因素、投资心理和交易制度等各种因素的影响而引起的波动，将对本基金资产产生潜在风险，主要包括：\r\n （1）政策风险\r\n 货币政策、财政政策、产业政策等国家政策的变化对证券市场产生一定的影响，导致市场价格波动，影响基金收益而产生风险。\r\n （2）经济周期风险\r\n 证券市场是国民经济的晴雨表，而经济运行具有周期性的特点。宏观经济运行状况将对证券市场的收益水平产生影响，从而产生风险。\r\n （3）利率风险\r\n 金融市场利率波动会导致股票市场及债券市场的价格和收益率的变动，同时直接影响企业的融资成本和利润水平。基金投资于股票和债券，收益水平会受到利率变化的影响。\r\n （4）上市公司经营风险\r\n 上市公司的经营状况受多种因素影响，如市场、技术、竞争、管理、财务等都会导致公司盈利发生变化，从而导致基金投资收益变化。\r\n （5）购买力风险\r\n 如果发生通货膨胀，基金投资于证券所获得的收益可能会被通货膨胀抵消，从而影响基金资产的保值增值。\r\n 2、信用风险\r\n 指基金在交易过程发生交收违约，或者基金所投资债券之发行人出现违约、拒绝支付到期本息，导致基金财产损失。\r\n 3、债券收益率曲线变动风险。\r\n 债券收益率曲线变动风险是指与收益率曲线非平行移动有关的风险，单一的久期指标并不能充分反映这一风险的存在。\r\n 、再投资风险\r\n 再投资风险反映了利率下降对固定收益证券利息收入再投资收益的影响，这与利率上升所带来的价格风险（即前面所提到的利率风险）互为消长。具体为当利率下降时，基金从投资的固定收益证券所得的利息收入进行再投资时，将获得较少的收益率。\r\n 5、流动性风险\r\n 指基金资产不能迅速转变成现金，或者不能应付可能出现的投资人大额赎回的风险。在开放式基金交易过程中，可能会发生巨额赎回的情形。巨额赎回可能会产生基金仓位调整的困难，导致流动性风险，甚至影响基金份额净值。\r\n 6、管理风险\r\n 在基金管理运作过程中，可能因基金管理人对经济形势和证券市场等判断有误、获取的信息不全等影响基金的收益水平。基金管理人和基金托管人的管理水平、管理手段和管理技术等对基金收益水平存在影响。\r\n 7、操作或技术风险\r\n 指相关当事人在业务各环节操作过程中，因内部控制存在缺陷或者人为因素造成操作失误或违反操作规程等引致的风险，例如，越权违规交易、会计部门欺诈、交易错误、IT 系统故障等风险。\r\n 在开放式基金的各种交易行为或者后台运作中，可能因为技术系统的故障或者差错而影响交易的正常进行或者导致投资人的利益受到影响。这种技术风险可能来自基金管理公司、注册登记机构、销售机构、证券交易所、证券登记结算机构等等。\r\n 8、合规性风险\r\n 指基金管理或运作过程中，违反国家法律、法规的规定，或者基金投资违反法规及基金合同有关规定的风险。\r\n 9、本基金的管理风险\r\n 在基金管理运作过程中基金管理人的知识、经验、判断、决策、技能等，会影响其对信息的占有和对经济形势、证券价格走势的判断，从而影响基金收益水平。因此，本基金的收益水平与本基金管理人的管理水平、管理手段和管理技术等相关性较大。因此本基金可能因为基金管理人的因素而影响基金收益水平。\r\n 、人才流失风险\r\n 公司主要业务人员的离职等可能会在一定程度上影响工作的连续性，并可能对基金运作产生影响。\r\n 11、其他风险\r\n 战争、自然灾害等不可抗力因素的出现，将会严重影响证券市场的运行，可能导致基金财产的损失。\r\n 金融市场危机、行业竞争、代理商违约、托管行违约等超出基金管理人自身直接控制能力之外的风险，可能导致基金或者基金份额持有人利益受损。\r\n （二）声明\r\n 本基金未经任何一级政府、机构及部门担保。投资人自愿投资于本基金，须自行承担投资风险。\r\n 十九、基金合同的变更、终止与基金财产的清算\r\n （一）基金合同的变更\r\n 1、基金合同变更内容对基金合同当事人权利、义务产生重大影响的，应召\r\n 开基金份额持有人大会，基金合同变更的内容应经基金份额持有人大会决议同意。\r\n （1）转换基金运作方式；\r\n （2）变更基金类别；\r\n （3）变更基金投资目标、投资范围或投资策略；\r\n （4）变更基金份额持有人大会程序；\r\n （5）更换基金管理人、基金托管人；\r\n （6）提高基金管理人、基金托管人的报酬标准。但根据适用的相关规定提高该等报酬标准的除外；\r\n （7）本基金与其他基金的合并；\r\n （8）对基金合同当事人权利、义务产生重大影响的其他事项；\r\n （9）法律法规、基金合同或中国证监会规定的其他情形。\r\n 但出现下列情况时，可不经基金份额持有人大会决议，由基金管理人和基金托管人同意变更后公布，并报中国证监会备案：\r\n （1）调低基金管理费、基金托管费和其他应由基金承担的费用；\r\n （2）在法律法规和本基金合同规定的范围内调低基金的申购费率、赎回费率或变更收费方式；\r\n （3）法律法规要求增加的基金费用的收取；\r\n （4）基金管理人、代销机构、注册登记机构在法律法规规定的范围内调整\r\n 有关基金认购、申购、赎回、转换、非交易过户、转托管等业务的规则；\r\n （5）因相应的法律法规发生变动必须对基金合同进行修改；\r\n （6）对基金合同的修改不涉及本基金合同当事人权利义务关系发生变化；\r\n （7）基金合同的修改对基金份额持有人利益无实质性不利影响；\r\n （8）按照法律法规或本基金合同规定不需召开基金份额持有人大会的其他情形。\r\n 、关于变更基金合同的基金份额持有人大会决议应报中国证监会核准或备案，并于中国证监会核准或出具无异议意见后生效执行，并自生效之日起 2 日内\r\n 在至少一种指定媒体上公告。\r\n （二）本基金合同的终止\r\n 有下列情形之一的，本基金合同经中国证监会核准后将终止：\r\n 1、基金份额持有人大会决定终止的；\r\n 2、基金管理人因解散、破产、撤销等事由，不能继续担任基金管理人的职务，而在 6 个月内无其他适当的基金管理公司承接其原有权利义务；\r\n 3、基金托管人因解散、破产、撤销等事由，不能继续担任基金托管人的职务，而在 6 个月内无其他适当的托管机构承接其原有权利义务；\r\n 4、中国证监会规定的其他情况。\r\n （三）基金财产的清算\r\n 1、基金财产清算组\r\n （1）基金合同终止时，成立基金财产清算组，基金财产清算组在中国证监会的监督下进行基金清算。\r\n （2）基金财产清算组成员由基金管理人、基金托管人、具有从事证券相关\r\n 业务资格的注册会计师、律师以及中国证监会指定的人员组成。基金财产清算组可以聘用必要的工作人员。\r\n （3）基金财产清算组负责基金财产的保管、清理、估价、变现和分配。基金财产清算组可以依法进行必要的民事活动。\r\n 2、基金财产清算程序\r\n 基金合同终止，应当按法律法规和本基金合同的有关规定对基金财产进行清算。基金财产清算程序主要包括：\r\n （1）基金合同终止后，发布基金财产清算公告；\r\n （2）基金合同终止时，由基金财产清算组统一接管基金财产；\r\n （3）对基金财产进行清理和确认；\r\n （4）对基金财产进行估价和变现；\r\n （5）聘请会计师事务所对清算报告进行审计；\r\n （6）聘请律师事务所出具法律意见书；\r\n （7）将基金财产清算结果报告中国证监会；\r\n （8）参加与基金财产有关的民事诉讼；\r\n （9）公布基金财产清算结果；\r\n （10）对基金剩余财产进行分配。\r\n 3、清算费用清算费用是指基金财产清算组在进行基金财产清算过程中发生的所有合理费用，清算费用由基金财产清算组优先从基金财产中支付。\r\n 4、基金财产按下列顺序清偿：\r\n （1）支付清算费用；\r\n （2）交纳所欠税款；\r\n （3）清偿基金债务；\r\n （4）按基金份额持有人持有的基金份额比例进行分配。\r\n 基金财产未按前款（1）-（3）项规定清偿前，不分配给基金份额持有人。\r\n 5、基金财产清算的公告\r\n 基金财产清算公告于基金合同终止并报中国证监会备案后 5 个工作日内由基金财产清算组公告；清算过程中的有关重大事项须及时公告；基金财产清算结\r\n 果经会计师事务所审计，律师事务所出具法律意见书后，由基金财产清算组报中国证监会备案并公告。\r\n 6、基金财产清算账册及文件的保存\r\n 基金财产清算账册及有关文件由基金托管人保存 15 年以上。\r\n 二十、基金合同的内容摘要\r\n （一）基金份额持有人、基金管理人和基金托管人的权利、义务\r\n 1、基金管理人的权利与义务\r\n 根据《基金法》及其他有关法律法规，基金管理人的权利为：\r\n （1）自本基金合同生效之日起，依照有关法律法规和本基金合同的规定独立运用基金财产；\r\n （2）依照基金合同获得基金管理费以及法律法规规定或监管部门批准的其他收入；\r\n （3）发售基金份额；\r\n （4）依照有关规定行使因基金财产投资于证券所产生的权利；\r\n （5）在符合有关法律法规的前提下，制订和调整有关基金认购、申购、赎\r\n 回、转换、非交易过户、转托管等业务的规则，在法律法规和本基金合同规定的范围内决定和调整基金的除调高托管费率和管理费率之外的相关费率结构和收费方式；\r\n （6）根据本基金合同及有关规定监督基金托管人，对于基金托管人违反了\r\n 本基金合同或有关法律法规规定的行为，对基金财产、其他当事人的利益造成重大损失的情形，应及时呈报中国证监会，并采取必要措施保护基金及相关当事人的利益；\r\n （7）在基金合同约定的范围内，拒绝或暂停受理申购和赎回申请；\r\n （8）在法律法规允许的前提下，为基金的利益依法为基金进行融资、融券；\r\n （9）自行担任或选择、更换注册登记机构，获取基金份额持有人名册，并对注册登记机构的代理行为进行必要的监督和检查；\r\n （10）选择、更换代销机构，并依据基金销售服务代理协议和有关法律法规，对其行为进行必要的监督和检查；\r\n （11）选择、更换律师、审计师、证券经纪商或其他为基金提供服务的外部机构；\r\n （12）在基金托管人更换时，提名新的基金托管人；\r\n （13）依法召集基金份额持有人大会；\r\n （14）法律法规和基金合同规定的其他权利。\r\n 根据《基金法》及其他有关法律法规，基金管理人的义务为：\r\n （1）依法募集基金，办理或者委托经中国证监会认定的其他机构代为办理\r\n 基金份额的发售、申购、赎回和登记事宜；\r\n （2）办理基金备案手续；\r\n （3）自基金合同生效之日起，以诚实信用、勤勉尽责的原则管理和运用基金财产；\r\n （4）配备足够的具有专业资格的人员进行基金投资分析、决策，以专业化的经营方式管理和运作基金财产；\r\n （5）建立健全内部风险控制、监察与稽核、财务管理及人事管理等制度，保证所管理的基金财产和管理人的财产相互独立，对所管理的不同基金分别管理，分别记账，进行证券投资；\r\n （6）除依据《基金法》、基金合同及其他有关规定外，不得为自己及任何第\r\n 三人谋取利益，不得委托第三人运作基金财产；\r\n （7）依法接受基金托管人的监督；\r\n （8）计算并公告基金资产净值，确定基金份额申购、赎回价格；\r\n （9）采取适当合理的措施使计算基金份额认购、申购、赎回和注销价格的方法符合基金合同等法律文件的规定；\r\n （10）按规定受理申购和赎回申请，及时、足额支付赎回款项；\r\n （11）进行基金会计核算并编制基金财务会计报告；\r\n （12）编制中期和年度基金报告；\r\n （13）严格按照《基金法》、基金合同及其他有关规定，履行信息披露及报告义务；\r\n （14）保守基金商业秘密，不得泄露基金投资计划、投资意向等，除《基金法》、基金合同及其他有关规定另有规定外，在基金信息公开披露前应予保密，不得向他人泄露；\r\n （15）按照基金合同的约定确定基金收益分配方案，及时向基金份额持有人分配收益；\r\n （16）依据《基金法》、基金合同及其他有关规定召集基金份额持有人大会或配合基金托管人、基金份额持有人依法召集基金份额持有人大会；\r\n （17）保存基金财产管理业务活动的记录、账册、报表和其他相关资料；\r\n （18）以基金管理人名义，代表基金份额持有人利益行使诉讼权利或者实施其他法律行为；\r\n （19）组织并参加基金财产清算小组，参与基金财产的保管、清理、估价、变现和分配；\r\n （20）因违反基金合同导致基金财产的损失或损害基金份额持有人合法权益，应当承担赔偿责任，其赔偿责任不因其退任而免除；\r\n （21）基金托管人违反基金合同造成基金财产损失时，应为基金份额持有人利益向基金托管人追偿；\r\n （22）按规定向基金托管人提供基金份额持有人名册资料；\r\n （23）面临解散、依法被撤销或者被依法宣告破产时，及时报告中国证监会并通知基金托管人；\r\n （24）执行生效的基金份额持有人大会决议；\r\n （25）不从事任何有损基金及其他基金当事人利益的活动；\r\n （26）依照法律法规为基金的利益对被投资公司行使股东权利，为基金的利\r\n 益行使因基金财产投资于证券所产生的权利，不谋求对上市公司的控股和直接管理；\r\n （27）法律法规、中国证监会和基金合同规定的其他义务。\r\n 2、基金托管人的权利与义务\r\n 根据《基金法》及其他有关法律法规，基金托管人的权利为：\r\n （1）依基金合同约定获得基金托管费以及法律法规规定或监管部门批准的其他收入；\r\n （2）监督基金管理人对本基金的投资运作；\r\n （3）自本基金合同生效之日起，依法保管基金资产；\r\n （4）在基金管理人更换时，提名新任基金管理人；\r\n （5）根据本基金合同及有关规定监督基金管理人，对于基金管理人违反本\r\n 基金合同或有关法律法规规定的行为，对基金资产、其他当事人的利益造成重大损失的情形，应及时呈报中国证监会，并采取必要措施保护基金及相关当事人的利益；\r\n （6）依法召集基金份额持有人大会；\r\n （7）按规定取得基金份额持有人名册资料；\r\n （8）法律法规和基金合同规定的其他权利。\r\n 根据《基金法》及其他有关法律法规，基金托管人的义务为：\r\n （1）安全保管基金财产；\r\n （2）设立专门的基金托管部，具有符合要求的营业场所，配备足够的、合\r\n 格的熟悉基金托管业务的专职人员，负责基金财产托管事宜；\r\n （3）对所托管的不同基金财产分别设置账户，确保基金财产的完整与独立；\r\n （4）除依据《基金法》、基金合同及其他有关规定外，不得为自己及任何第\r\n 三人谋取利益，不得委托第三人托管基金财产；\r\n （5）保管由基金管理人代表基金签订的与基金有关的重大合同及有关凭证；\r\n （6）按规定开设基金财产的资金账户和证券账户；\r\n （7）保守基金商业秘密，除《基金法》、基金合同及其他有关规定另有规定外，在基金信息公开披露前应予保密，不得向他人泄露；\r\n （8）对基金财务会计报告、中期和年度基金报告出具意见，说明基金管理人在各重要方面的运作是否严格按照基金合同的规定进行；如果基金管理人有未\r\n 执行基金合同规定的行为，还应当说明基金托管人是否采取了适当的措施；\r\n （9）保存基金托管业务活动的记录、账册、报表和其他相关资料；\r\n （10）按照基金合同的约定，根据基金管理人的投资指令，及时办理清算、交割事宜；\r\n （11）办理与基金托管业务活动有关的信息披露事项；\r\n （12）复核、审查基金管理人计算的基金资产净值和基金份额申购、赎回价格；\r\n （13）按照规定监督基金管理人的投资运作；\r\n （14）按规定制作相关账册并与基金管理人核对；\r\n （15）依据基金管理人的指令或有关规定向基金份额持有人支付基金收益和赎回款项；\r\n （16）按照规定召集基金份额持有人大会或配合基金份额持有人依法自行召集基金份额持有人大会；\r\n （17）因违反基金合同导致基金财产损失，应承担赔偿责任，其赔偿责任不因其退任而免除；\r\n （18）基金管理人因违反基金合同造成基金财产损失时，应为基金向基金管理人追偿；\r\n （19）参加基金财产清算小组，参与基金财产的保管、清理、估价、变现和分配；\r\n （20）面临解散、依法被撤销或者被依法宣告破产时，及时报告中国证监会\r\n 和银行业监督管理机构，并通知基金管理人；\r\n （21）执行生效的基金份额持有人大会决议；\r\n （22）不从事任何有损基金及其他基金当事人利益的活动；\r\n （23）建立并保存基金份额持有人名册；\r\n （24）法律法规、中国证监会和基金合同规定的其他义务。\r\n 3、基金份额持有人的权利与义务\r\n 根据《基金法》及其他有关法律法规，基金份额持有人的权利为：\r\n （1）分享基金财产收益；\r\n （2）参与分配清算后的剩余基金财产；\r\n （3）依法申请赎回其持有的基金份额；\r\n （4）按照规定要求召开基金份额持有人大会；\r\n （5）出席或者委派代表出席基金份额持有人大会，对基金份额持有人大会审议事项行使表决权；\r\n （6）查阅或者复制公开披露的基金信息资料；\r\n （7）监督基金管理人的投资运作；\r\n （8）对基金管理人、基金托管人、基金份额发售机构损害其合法权益的行为依法提起诉讼；\r\n （9）法律法规和基金合同规定的其他权利。\r\n 每份基金份额具有同等的合法权益。\r\n 根据《基金法》及其他有关法律法规，基金份额持有人的义务为：\r\n （1）遵守法律法规、基金合同及其他有关规定；\r\n （2）交纳基金认购、申购款项及法律法规和基金合同所规定的费用；\r\n （3）在持有的基金份额范围内，承担基金亏损或者基金合同终止的有限责任；\r\n （4）不从事任何有损基金及其他基金份额持有人合法权益的活动；\r\n （5）执行生效的基金份额持有人大会决议；\r\n （6）返还在基金交易过程中因任何原因，自基金管理人及基金管理人的代\r\n 理人、基金托管人、代销机构、其他基金份额持有人处获得的不当得利；\r\n （7）法律法规和基金合同规定的其他义务。\r\n （二）基金份额持有人大会召集、议事及表决的程序和规则\r\n 1、基金份额持有人大会由基金份额持有人或基金份额持有人的合法授权代表共同组成。基金份额持有人持有的每一基金份额具有同等的投票权。\r\n 2、召开事由\r\n （1）当出现或需要决定下列事由之一的，经基金管理人、基金托管人或持\r\n 有基金份额 10%以上（含 10%，下同）的基金份额持有人（以基金管理人收到提议当日的基金份额计算，下同）提议时，应当召开基金份额持有人大会：\r\n 1）终止基金合同；\r\n 2）转换基金运作方式；\r\n 3）变更基金类别；\r\n 4）变更基金投资目标、投资范围或投资策略；\r\n 5）变更基金份额持有人大会程序；\r\n 6）更换基金管理人、基金托管人；\r\n 7）提高基金管理人、基金托管人的报酬标准，但法律法规要求提高该等报酬标准的除外；\r\n 8）本基金与其他基金的合并；\r\n 9）对基金合同当事人权利、义务产生重大影响的其他事项；\r\n 10）法律法规、基金合同或中国证监会规定的其他情形。\r\n （2）出现以下情形之一的，可由基金管理人和基金托管人协商后修改基金合同，不需召开基金份额持有人大会：\r\n ）调低基金管理费、基金托管费和其他应由基金承担的费用；\r\n 2）在法律法规和本基金合同规定的范围内调低基金的申购费率、赎回费率或变更收费方式；\r\n 3）法律法规要求增加的基金费用的收取；\r\n 4）基金管理人、代销机构、注册登记机构在法律法规规定的范围内调整有\r\n 关基金认购、申购、赎回、转换、非交易过户、转托管等业务的规则；\r\n 5）因相应的法律法规发生变动必须对基金合同进行修改；\r\n 6）对基金合同的修改不涉及本基金合同当事人权利义务关系发生变化；\r\n 7）基金合同的修改对基金份额持有人利益无实质性不利影响；\r\n 8）按照法律法规或本基金合同规定不需召开基金份额持有人大会的其他情形。\r\n 3、召集人和召集方式\r\n （1）除法律法规或本基金合同另有约定外，基金份额持有人大会由基金管理人召集。基金管理人未按规定召集或者不能召集时，由基金托管人召集。\r\n （2）基金托管人认为有必要召开基金份额持有人大会的，应当向基金管理\r\n 人提出书面提议。基金管理人应当自收到书面提议之日起 10 日内决定是否召集，并书面告知基金托管人。基金管理人决定召集的，应当自出具书面决定之日起\r\n 60 日内召开；基金管理人决定不召集，基金托管人仍认为有必要召开的，应当自行召集。\r\n （3）代表基金份额 10%以上的基金份额持有人认为有必要召开基金份额持\r\n 有人大会的，应当向基金管理人提出书面提议。基金管理人应当自收到书面提议之日起 10 日内决定是否召集，并书面告知提出提议的基金份额持有人代表和基金托管人。基金管理人决定召集的，应当自出具书面决定之日起 60 日内召开；\r\n 基金管理人决定不召集，代表基金份额 10%以上的基金份额持有人仍认为有必要召开的，应当向基金托管人提出书面提议。基金托管人应当自收到书面提议之日\r\n 起 10 日内决定是否召集，并书面告知提出提议的基金份额持有人代表和基金管理人；基金托管人决定召集的，应当自出具书面决定之日起 60 日内召开。\r\n （4）代表基金份额 10%以上的基金份额持有人就同一事项要求召开基金份\r\n 额持有人大会，而基金管理人、基金托管人都不召集的，代表基金份额 10%以上的基金份额持有人有权自行召集基金份额持有人大会，但应当至少提前 30 日向中国证监会备案。\r\n （5）基金份额持有人依法自行召集基金份额持有人大会的，基金管理人、基金托管人应当配合，不得阻碍、干扰。\r\n 4、召开基金份额持有人大会的通知时间、通知内容、通知方式\r\n （1）基金份额持有人大会的召集人（以下简称“召集人”）负责选择确定\r\n 开会时间、地点、方式和权益登记日。召开基金份额持有人大会，召集人必须于会议召开日前 30 日在指定媒体及基金管理人网站公告。基金份额持有人大会通知须至少载明以下内容：\r\n 1）会议召开的时间、地点和出席方式；\r\n 2）会议拟审议的主要事项；\r\n 3）会议形式；\r\n 4）议事程序；\r\n 5）有权出席基金份额持有人大会的基金份额持有人权益登记日；\r\n 6）代理投票的授权委托书的内容要求（包括但不限于代理人身份、代理权限和代理有效期限等）、送达时间和地点；\r\n 7）表决方式；\r\n 8）会务常设联系人姓名、电话；\r\n 9）出席会议者必须准备的文件和必须履行的手续；\r\n 10）召集人需要通知的其他事项。\r\n （2）采用通讯方式开会并进行表决的情况下，由召集人决定通讯方式和书\r\n 面表决方式，并在会议通知中说明本次基金份额持有人大会所采取的具体通讯方式、委托的公证机关及其联系方式和联系人、书面表决意见寄交的截止时间和收取方式。\r\n （3）如召集人为基金管理人，还应另行书面通知基金托管人到指定地点对\r\n 书面表决意见的计票进行监督；如召集人为基金托管人，则应另行书面通知基金管理人到指定地点对书面表决意见的计票进行监督；如召集人为基金份额持有人，则应另行书面通知基金管理人和基金托管人到指定地点对书面表决意见的计票进行监督。基金管理人或基金托管人拒不派代表对书面表决意见的计票进行监督的，不影响计票和表决结果。\r\n 5、基金份额持有人出席会议的方式\r\n （1）会议方式\r\n 1）基金份额持有人大会的召开方式包括现场开会和通讯方式开会。\r\n 2）现场开会由基金份额持有人本人出席或通过授权委托书委派其代理人出席，现场开会时基金管理人和基金托管人的授权代表应当出席，如基金管理人或基金托管人拒不派代表出席的，不影响表决效力。\r\n 3）通讯方式开会指按照本基金合同的相关规定以通讯的书面方式进行表决。\r\n 4）会议的召开方式由召集人确定，但决定转换基金运作方式、基金管理人\r\n 更换或基金托管人的更换、终止基金合同等事宜必须以现场开会方式召开基金份额持有人大会。\r\n （2）召开基金份额持有人大会的条件\r\n 1）现场开会方式\r\n 在同时符合以下条件时，现场会议方可举行：\r\n A、对到会者在权益登记日持有基金份额的统计显示，全部有效凭证所对应\r\n 的基金份额应占权益登记日基金总份额的 50%以上（含 50%，下同）；\r\n B、到会的基金份额持有人身份证明及持有基金份额的凭证、代理人身份证\r\n 明、委托人持有基金份额的凭证及授权委托代理手续完备，到会者出具的相关文件符合有关法律法规和基金合同及会议通知的规定，并且持有基金份额的凭证与基金管理人持有的注册登记资料相符。\r\n 2）通讯开会方式\r\n 在同时符合以下条件时，通讯会议方可举行：\r\n A、召集人按本基金合同规定公布会议通知后，在 2 个工作日内连续公布相关提示性公告；\r\n B、召集人按基金合同规定通知基金托管人或/和基金管理人（分别或共同称为“监督人”）到指定地点对书面表决意见的计票进行监督；\r\n C、召集人在监督人和公证机关的监督下按照会议通知规定的方式收取和统\r\n 计基金份额持有人的书面表决意见，如基金管理人或基金托管人经通知拒不到场监督的，不影响表决效力；\r\n D、本人直接出具书面意见和授权他人代表出具书面意见的基金份额持有人\r\n 所代表的基金份额占权益登记日基金总份额的 50%以上；\r\n E、直接出具书面意见的基金份额持有人或受托代表他人出具书面意见的代\r\n 理人提交的持有基金份额的凭证、授权委托书等文件符合法律法规、基金合同和会议通知的规定，并与注册登记机构记录相符。\r\n 6、议事内容与程序\r\n （1）议事内容及提案权\r\n 1）议事内容为本基金合同规定的召开基金份额持有人大会事由所涉及的内容。\r\n 2）基金管理人、基金托管人、单独或合并持有权益登记日本基金总份额 10%以上的基金份额持有人可以在大会召集人发出会议通知前就召开事由向大会召集人提交需由基金份额持有人大会审议表决的提案。\r\n 3）对于基金份额持有人提交的提案，大会召集人应当按照以下原则对提案\r\n 进行审核：\r\n 关联性。大会召集人对于基金份额持有人提案涉及事项与基金有直接关系，并且不超出法律法规和基金合同规定的基金份额持有人大会职权范围的，应提交大会审议；对于不符合上述要求的，不提交基金份额持有人大会审议。如果召集人决定不将基金份额持有人提案提交大会表决，应当在该次基金份额持有人大会上进行解释和说明。\r\n 程序性。大会召集人可以对基金份额持有人的提案涉及的程序性问题做出决定。如将其提案进行分拆或合并表决，需征得原提案人同意；原提案人不同意变更的，大会主持人可以就程序性问题提请基金份额持有人大会做出决定，并按照基金份额持有人大会决定的程序进行审议。\r\n 4）单独或合并持有权益登记日基金总份额 10%以上的基金份额持有人提交\r\n 基金份额持有人大会审议表决的提案，基金管理人或基金托管人提交基金份额持有人大会审议表决的提案，未获基金份额持有人大会审议通过，就同一提案再次提请基金份额持有人大会审议，其时间间隔不少于 6 个月。法律法规另有规定的除外。\r\n 5）基金份额持有人大会的召集人发出召开会议的通知后，如果需要对原有提案进行修改，应当在基金份额持有人大会召开前 30 日及时公告。否则，会议的召开日期应当顺延并保证至少与公告日期有 30 日的间隔期。\r\n （2）议事程序\r\n 1）现场开会\r\n 在现场开会的方式下，首先由大会主持人按照规定程序宣布会议议事程序及注意事项，确定和公布监票人，然后由大会主持人宣读提案，经讨论后进行表决，经合法执业的律师见证后形成大会决议。\r\n 大会由召集人授权代表主持。基金管理人为召集人的，其授权代表未能主持大会的情况下，由基金托管人授权代表主持；如果基金管理人和基金托管人授权代表均未能主持大会，则由出席大会的基金份额持有人和代理人以所代表的基金\r\n 份额 50%以上多数选举产生一名代表作为该次基金份额持有人大会的主持人。\r\n 召集人应当制作出席会议人员的签名册。签名册载明参加会议人员姓名（或单位名称）、身份证号码、持有或代表有表决权的基金份额数量、委托人姓名（或单位名称）等事项。\r\n 2）通讯方式开会\r\n 在通讯表决开会的方式下，首先由召集人提前 30 日公布提案，在所通知的表决截止日期后第 2 个工作日在公证机关及监督人的监督下由召集人统计全部\r\n 有效表决并形成决议。如监督人经通知但拒绝到场监督，不影响在公证机关监督下形成的决议的有效性。\r\n （3）基金份额持有人大会不得对未事先公告的议事内容进行表决。\r\n 7、决议形成的条件、表决方式、程序\r\n （1）基金份额持有人所持每一基金份额享有平等的表决权。\r\n （2）基金份额持有人大会决议分为一般决议和特别决议：\r\n 1）一般决议\r\n 一般决议须经出席会议的基金份额持有人（或其代理人）所持表决权的 50%以上通过方为有效，除下列 2）所规定的须以特别决议通过事项以外的其他事项\r\n 均以一般决议的方式通过；\r\n 2）特别决议\r\n 特别决议须经出席会议的基金份额持有人（或其代理人）所持表决权的三分之二以上（含三分之二）通过方为有效；涉及更换基金管理人、更换基金托管人、转换基金运作方式、终止基金合同等重大事项必须以特别决议通过方为有效。\r\n （3）基金份额持有人大会决定的事项，应当依法报中国证监会核准或者备案，并予以公告。\r\n （4）采取通讯方式进行表决时，除非在计票时有充分的相反证据证明，否\r\n 则表面符合法律法规和会议通知规定的书面表决意见即视为有效的表决，表决意见模糊不清或相互矛盾的视为弃权表决，但应当计入出具书面意见的基金份额持有人所代表的基金份额总数。\r\n （5）基金份额持有人大会采取记名方式进行投票表决。\r\n （6）基金份额持有人大会的各项提案或同一项提案内并列的各项议题应当\r\n 分开审议、逐项表决。\r\n 8、计票\r\n （1）现场开会\r\n 1）如基金份额持有人大会由基金管理人或基金托管人召集，则基金份额持有人大会的主持人应当在会议开始后宣布在出席会议的基金份额持有人和代理人中推举两名基金份额持有人代表与大会召集人授权的一名监督员共同担任监票人；如大会由基金份额持有人自行召集，基金份额持有人大会的主持人应当在会议开始后宣布在出席会议的基金份额持有人和代理人中推举两名基金份额持\r\n 有人代表与基金管理人、基金托管人授权的一名监督员共同担任监票人；但如果基金管理人和基金托管人的授权代表未出席，则大会主持人可自行选举三名基金份额持有人代表担任监票人。\r\n 2）监票人应当在基金份额持有人表决后立即进行清点，由大会主持人当场公布计票结果。\r\n 3）如大会主持人对于提交的表决结果有异议，可以对投票数进行重新清点；\r\n 如大会主持人未组织监票人进行重新清点，而出席大会的基金份额持有人或代理人对大会主持人宣布的表决结果有异议，其有权在宣布表决结果后立即要求重新清点，大会主持人应当组织监票人立即重新清点并公布重新清点结果。重新清点\r\n 仅限一次。\r\n （2）通讯方式开会在通讯方式开会的情况下，计票方式为：由大会召集人授权的两名监票人在监督人派出的授权代表的监督下进行计票，并由公证机关对其计票过程予以公证；如监督人经通知但拒绝到场监督，则大会召集人可自行授权 3 名监票人进行计票，并由公证机关对其计票过程予以公证。\r\n 9、基金份额持有人大会决议报中国证监会核准或备案后的公告时间、方式\r\n （1）基金份额持有人大会通过的一般决议和特别决议，召集人应当自通过\r\n 之日起 5 日内报中国证监会核准或者备案。基金份额持有人大会决定的事项自中国证监会依法核准或者出具无异议意见之日起生效。\r\n （2）生效的基金份额持有人大会决议对全体基金份额持有人、基金管理人、基金托管人均有约束力。基金管理人、基金托管人和基金份额持有人应当执行生效的基金份额持有人大会决议。\r\n （3）基金份额持有人大会决议应自生效之日起 2 日内在指定媒体公告。如\r\n 果采用通讯方式进行表决，在公告基金份额持有人大会决议时，必须将公证书全文、公证机构、公证员姓名等一同公告。\r\n 10、法律法规或监管部门对基金份额持有人大会另有规定的，从其规定。\r\n （三）基金合同的变更、终止与基金财产的清算\r\n 1、基金合同的变更\r\n （1）基金合同变更内容对基金合同当事人权利、义务产生重大影响的，应\r\n 召开基金份额持有人大会，基金合同变更的内容应经基金份额持有人大会决议同意。\r\n 1）转换基金运作方式；\r\n 2）变更基金类别；\r\n 3）变更基金投资目标、投资范围或投资策略；\r\n 4）变更基金份额持有人大会程序；\r\n 5）更换基金管理人、基金托管人；\r\n 6）提高基金管理人、基金托管人的报酬标准。但根据适用的相关规定提高该等报酬标准的除外；\r\n 7）本基金与其他基金的合并；\r\n ）对基金合同当事人权利、义务产生重大影响的其他事项；\r\n 9）法律法规、基金合同或中国证监会规定的其他情形。\r\n 但出现下列情况时，可不经基金份额持有人大会决议，由基金管理人和基金托管人同意变更后公布，并报中国证监会备案：\r\n 1）调低基金管理费、基金托管费和其他应由基金承担的费用；\r\n 2）在法律法规和本基金合同规定的范围内调低基金的申购费率、赎回费率或变更收费方式；\r\n 3）法律法规要求增加的基金费用的收取；\r\n 4）基金管理人、代销机构、注册登记机构在法律法规规定的范围内调整有\r\n 关基金认购、申购、赎回、转换、非交易过户、转托管等业务的规则；\r\n 5）因相应的法律法规发生变动必须对基金合同进行修改；\r\n 6）对基金合同的修改不涉及本基金合同当事人权利义务关系发生变化；\r\n 7）基金合同的修改对基金份额持有人利益无实质性不利影响；\r\n 8）按照法律法规或本基金合同规定不需召开基金份额持有人大会的其他情形。\r\n （2）关于变更基金合同的基金份额持有人大会决议应报中国证监会核准或备案，并于中国证监会核准或出具无异议意见后生效执行，并自生效之日起 2日内在至少一种指定媒体上公告。\r\n 2、本基金合同的终止\r\n 有下列情形之一的，本基金合同经中国证监会核准后将终止：\r\n （1）基金份额持有人大会决定终止的；\r\n （2）基金管理人因解散、破产、撤销等事由，不能继续担任基金管理人的职务，而在 6 个月内无其他适当的基金管理公司承接其原有权利义务；\r\n （3）基金托管人因解散、破产、撤销等事由，不能继续担任基金托管人的职务，而在 6 个月内无其他适当的托管机构承接其原有权利义务；\r\n （4）中国证监会规定的其他情况。\r\n 3、基金财产的清算\r\n （1）基金财产清算组\r\n 1）基金合同终止时，成立基金财产清算组，基金财产清算组在中国证监会的监督下进行基金清算。\r\n 2）基金财产清算组成员由基金管理人、基金托管人、具有从事证券相关业\r\n 务资格的注册会计师、律师以及中国证监会指定的人员组成。基金财产清算组可以聘用必要的工作人员。\r\n 3）基金财产清算组负责基金财产的保管、清理、估价、变现和分配。基金财产清算组可以依法进行必要的民事活动。\r\n （2）基金财产清算程序\r\n 基金合同终止，应当按法律法规和本基金合同的有关规定对基金财产进行清算。基金财产清算程序主要包括：\r\n 1）基金合同终止后，发布基金财产清算公告；\r\n 2）基金合同终止时，由基金财产清算组统一接管基金财产；\r\n 3）对基金财产进行清理和确认；\r\n 4）对基金财产进行估价和变现；\r\n 5）聘请会计师事务所对清算报告进行审计；\r\n 6）聘请律师事务所出具法律意见书；\r\n 7）将基金财产清算结果报告中国证监会；\r\n 8）参加与基金财产有关的民事诉讼；\r\n 9）公布基金财产清算结果；\r\n 10）对基金剩余财产进行分配。\r\n （3）清算费用清算费用是指基金财产清算组在进行基金财产清算过程中发生的所有合理费用，清算费用由基金财产清算组优先从基金财产中支付。\r\n （4）基金财产按下列顺序清偿：\r\n 1）支付清算费用；\r\n 2）交纳所欠税款；\r\n 3）清偿基金债务；\r\n 4）按基金份额持有人持有的基金份额比例进行分配。\r\n 基金财产未按前款 1）-3）项规定清偿前，不分配给基金份额持有人。\r\n （5）基金财产清算的公告基金财产清算公告于基金合同终止并报中国证监会备案后 5 个工作日内由基金财产清算组公告；清算过程中的有关重大事项须及时公告；基金财产清算结\r\n 果经会计师事务所审计，律师事务所出具法律意见书后，由基金财产清算组报中国证监会备案并公告。\r\n （6）基金财产清算账册及文件的保存\r\n 基金财产清算账册及有关文件由基金托管人保存 15 年以上。\r\n （四）争议的处理\r\n 对于因基金合同的订立、内容、履行和解释或与基金合同有关的争议，基金合同当事人应尽量通过协商、调解途径解决。不愿或者不能通过协商、调解解决的，任何一方均有权将争议提交中国国际经济贸易仲裁委员会，按照中国国际经济贸易仲裁委员会届时有效的仲裁规则进行仲裁。仲裁地点为北京市。仲裁裁决是终局的，对各方当事人均有约束力，仲裁费用由败诉方承担。\r\n 争议处理期间，基金合同当事人应恪守各自的职责，继续忠实、勤勉、尽责地履行基金合同规定的义务，维护基金份额持有人的合法权益。\r\n 本基金合同受中国法律管辖。\r\n （五）基金合同存放地和投资人取得基金合同的方式\r\n 基金合同可印制成册，在编制完成后，将存放于基金管理人所在地、基金托管人所在地，供公众查阅。投资人在支付工本费后，可在合理时间内取得上述文件复制件或复印件。投资人也可在基金管理人指定的网站上进行查阅。\r\n 二十一、基金托管协议的内容摘要\r\n （一）托管协议当事人\r\n 1、基金管理人\r\n 名称：长信基金管理有限责任公司\r\n 住所：上海市浦东新区银城中路 68 号时代金融中心 9 楼\r\n 办公地址：上海市浦东新区银城中路 68 号时代金融中心 9 楼\r\n 邮政编码： 200120\r\n 法定代表人：田丹\r\n 批准成立时间：2003 年 4 月 28 日\r\n 批准设立机关及批准设立文号：中国证监会证监基字【2003】63 号\r\n 组织形式：有限责任公司\r\n 注册资本：壹亿伍仟万元人民币\r\n 存续期间：持续经营\r\n 经营范围：基金管理业务、发起设立基金及中国证券监督管理委员会批准的其他业务（涉及许可经营的凭许可证经营）。\r\n 2、基金托管人\r\n 名称：中国建设银行股份有限公司（简称：中国建设银行）\r\n 住所：北京市西城区金融大街 25 号\r\n 办公地址：北京市西城区闹市口大街 1 号院 1 号楼\r\n 邮政编码：100033\r\n 法定代表人：王洪章\r\n 成立日期：2004 年 09 月 17 日\r\n 基金托管业务批准文号：中国证监会证监基字[1998]12 号\r\n 组织形式：股份有限公司\r\n 注册资本：贰仟伍佰亿壹仟零玖拾柒万柒仟肆佰捌拾陆元整\r\n 存续期间：持续经营\r\n 经营范围：吸收公众存款；发放短期、中期、长期贷款；办理国内外结算；\r\n 办理票据承兑与贴现；发行金融债券；代理发行、代理兑付、承销政府债券；买卖政府债券、金融债券；从事同业拆借；买卖、代理买卖外汇；从事银行卡业务；\r\n 提供信用证服务及担保；代理收付款项及代理保险业务；提供保管箱服务；经中国银行业监督管理机构等监管部门批准的其他业务。\r\n （二）基金托管人与基金管理人之间的业务监督、核查\r\n 1、基金托管人对基金管理人的业务监督和核查\r\n 基金托管人根据有关法律法规的规定及基金合同的约定，对基金投资范围、投资对象进行监督。基金合同明确约定基金投资风格或证券选择标准的，基金管理人应按照基金托管人要求的格式提供投资品种池和交易对手库，以便基金托管人运用相关技术系统，对基金实际投资是否符合基金合同关于证券选择标准的约定进行监督，对存在疑义的事项进行核查。\r\n 本基金的投资范围为具有良好流动性的金融工具，包括国内依法发行的股票、债券、权证、资产支持证券以及法律法规或中国证监会允许基金投资的其他金融工具。如法律法规或监管机构以后允许基金投资的其他品种，基金管理人在履行适当程序后，可以将其纳入投资范围。\r\n 本基金投资于股票资产的比例范围是基金资产的 60%-95%，投资于现金、债券资产以及中国证监会允许基金投资的其他证券品种的比例范围是基金资产的\r\n 5%-40%，现金及到期日在一年以内的政府债券不低于基金资产净值的 5%。本基\r\n 金投资优势产业的资产不低于股票资产的 80%。投资于权证、资产支持证券或其它衍生金融工具的比例遵守法律法规或中国证监会的有关规定。\r\n 2、基金托管人根据有关法律法规的规定及基金合同的约定，对基金投资、融资比例进行监督。基金托管人按下述比例和调整期限进行监督：\r\n （1）本基金持有一家上市公司的股票，其市值不超过基金资产净值的 10%；\r\n （2）本基金持有的全部权证，其市值不得超过基金资产净值的 3%；\r\n （3）本基金进入全国银行间同业市场进行债券回购的资金余额不得超过基\r\n 金资产净值的 40%；\r\n （4）本基金投资于股票资产的比例范围是基金资产的 60%-95%，投资于现\r\n 金、债券资产以及中国证监会允许基金投资的其他证券品种的比例范围是基金资\r\n 产的 5%-40%，现金及到期日在一年以内的政府债券不低于基金资产净值的 5%。\r\n 本基金投资优势产业的资产不低于股票资产的 80%；\r\n （5）本基金投资于同一原始权益人的各类资产支持证券的比例，不得超过\r\n 基金资产净值的 10%；\r\n （6）本基金持有的全部资产支持证券，其市值不得超过基金资产净值的\r\n 20%；\r\n （7）本基金持有的同一（指同一信用级别）资产支持证券的比例，不得超\r\n 过该资产支持证券规模的 10%；\r\n （8）本基金应投资于信用级别评级为 BBB 以上（含 BBB）的资产支持证券。\r\n 基金持有资产支持证券期间，如果其信用等级下降、不再符合投资标准，应在评级报告发布之日起 3 个月内予以全部卖出；\r\n （9）基金财产参与股票发行申购，本基金所申报的金额不超过本基金的总资产，本基金所申报的股票数量不超过拟发行股票公司本次发行股票的总量；\r\n （10）本基金在任何交易日买入权证的总金额，不得超过上一交易日基金资\r\n 产净值的 0.5%；\r\n （11）如果法律法规对上述投资组合比例限制进行变更的，以变更后的规定为准。法律法规或监管部门取消上述限制，如适用于本基金，则本基金投资不再受相关限制。\r\n 因证券市场波动、上市公司合并、基金规模变动、股权分置改革中支付对价等基金管理人之外的因素致使基金投资比例不符合上述规定投资比例的，基金管理人应当在 10 个交易日内进行调整。法律法规或者监管部门另有规定，从其规定。\r\n 对于因基金份额拆分、大比例分红等集中持续营销活动引起的基金净资产规\r\n 模在 10 个交易日内增加 10 亿元以上的情形，而导致证券投资比例低于基金合同约定的，基金管理人履行相关程序后可将调整时限从 10 个交易日延长到 3 个月。\r\n 法律法规如有变更，从其变更。除投资资产配置比例外，基金托管人对基金的投资比例限制的监督与检查自本基金合同生效之日起开始。基金管理人应当自基金合同生效之日起 6 个月内使基金的投资组合比例符合基金合同的有关约定。\r\n 3、基金托管人根据有关法律法规的规定及基金合同的约定，对本托管协议\r\n 第十五条第九款基金投资禁止行为进行监督。基金托管人通过事后监督方式对基金管理人基金投资禁止行为和关联交易进行监督。根据法律法规有关基金禁止从事关联交易的规定，基金管理人和基金托管人应事先相互提供与本机构有控股关系的股东、与本机构有其他重大利害关系的公司名单及有关关联方发行的证券名单。基金管理人和基金托管人有责任确保关联交易名单的真实性、准确性、完整性，并负责及时将更新后的名单发送给对方。\r\n 若基金托管人发现基金管理人与关联交易名单中列示的关联方进行法律法\r\n 规禁止基金从事的关联交易时，基金托管人应及时提醒基金管理人采取必要措施阻止该关联交易的发生，如基金托管人采取必要措施后仍无法阻止关联交易发生时，基金托管人有权向中国证监会报告。对于基金管理人已成交的关联交易，基金托管人事前无法阻止该关联交易的发生，只能进行事后结算，基金托管人不承担由此造成的损失，并向中国证监会报告。\r\n 4、基金托管人根据有关法律法规的规定及基金合同的约定，对基金管理人参与银行间债券市场进行监督。基金管理人应在基金投资运作之前向基金托管人提供符合法律法规及行业标准的、经慎重选择的、本基金适用的银行间债券市场交易对手名单，并约定各交易对手所适用的交易结算方式。基金管理人应严格按照交易对手名单的范围在银行间债券市场选择交易对手。基金托管人监督基金管理人是否按事前提供的银行间债券市场交易对手名单进行交易。基金管理人可以每半年对银行间债券市场交易对手名单及结算方式进行更新，新名单确定前已与本次剔除的交易对手所进行但尚未结算的交易，仍应按照协议进行结算。如基金管理人根据市场情况需要临时调整银行间债券市场交易对手名单及结算方式的，应向基金托管人说明理由，并在与交易对手发生交易前 3 个工作日内与基金托管人协商解决。\r\n 基金管理人负责对交易对手的资信控制，按银行间债券市场的交易规则进行交易，并负责解决因交易对手不履行合同而造成的纠纷及损失，基金托管人不承担由此造成的任何法律责任及损失。若未履约的交易对手在基金托管人与基金管理人确定的时间前仍未承担违约责任及其他相关法律责任的，基金管理人可以对相应损失先行予以承担，然后再向相关交易对手追偿。基金托管人则根据银行间债券市场成交单对合同履行情况进行监督。如基金托管人事后发现基金管理人没有按照事先约定的交易对手或交易方式进行交易时，基金托管人应及时提醒基金管理人，基金托管人不承担由此造成的任何损失和责任。\r\n 5、本基金投资流通受限证券，基金管理人应事先根据中国证监会相关规定，与基金托管人就相关事项签订补充协议，明确基金投资流通受限证券的比例。基金管理人应制订严格的投资决策流程和风险控制制度，防范流动性风险、法律风险和操作风险等各种风险。基金托管人对基金管理人是否遵守相关制度、流动性风险处置预案以及相关投资额度和比例等的情况进行监督。\r\n 6、基金托管人根据有关法律法规的规定及基金合同的约定，对基金资产净\r\n 值计算、基金份额净值计算、应收资金到账、基金费用开支及收入确定、基金收益分配、相关信息披露、基金宣传推介材料中登载基金业绩表现数据等进行监督和核查。\r\n 7、基金托管人发现基金管理人的上述事项及投资指令或实际投资运作违反\r\n 法律法规、基金合同和本托管协议的规定，应及时以电话提醒或书面提示等方式通知基金管理人限期纠正。基金管理人应积极配合和协助基金托管人的监督和核查。基金管理人收到书面通知后应在下一工作日前及时核对并以书面形式给基金托管人发出回函，就基金托管人的疑义进行解释或举证，说明违规原因及纠正期限，并保证在规定期限内及时改正。在上述规定期限内，基金托管人有权随时对通知事项进行复查，督促基金管理人改正。基金管理人对基金托管人通知的违规事项未能在限期内纠正的，基金托管人应报告中国证监会。\r\n 8、基金管理人有义务配合和协助基金托管人依照法律法规、基金合同和本\r\n 托管协议对基金业务执行核查。对基金托管人发出的书面提示，基金管理人应在规定时间内答复并改正，或就基金托管人的疑义进行解释或举证；对基金托管人按照法律法规、基金合同和本托管协议的要求需向中国证监会报送基金监督报告的事项，基金管理人应积极配合提供相关数据资料和制度等。\r\n 9、若基金托管人发现基金管理人依据交易程序已经生效的指令违反法律、行政法规和其他有关规定，或者违反基金合同约定的，应当立即通知基金管理人，由此造成的损失由基金管理人承担。\r\n 10、基金托管人发现基金管理人有重大违规行为，应及时报告中国证监会，同时通知基金管理人限期纠正，并将纠正结果报告中国证监会。基金管理人无正当理由，拒绝、阻挠对方根据本托管协议规定行使监督权，或采取拖延、欺诈等手段妨碍对方进行有效监督，情节严重或经基金托管人提出警告仍不改正的，基金托管人应报告中国证监会。\r\n 11、基金管理人对基金托管人的业务核查\r\n （1）基金管理人对基金托管人履行托管职责情况进行核查，核查事项包括\r\n 基金托管人安全保管基金财产、开设基金财产的资金账户和证券账户、复核基金管理人计算的基金资产净值和基金份额净值、根据基金管理人指令办理清算交收、相关信息披露和监督基金投资运作等行为。\r\n （2）基金管理人发现基金托管人擅自挪用基金财产、未对基金财产实行分\r\n 账管理、未执行或无故延迟执行基金管理人资金划拨指令、泄露基金投资信息等违反《基金法》、基金合同、本协议及其他有关规定时，应及时以书面形式通知基金托管人限期纠正。基金托管人收到通知后应及时核对并以书面形式给基金管理人发出回函，说明违规原因及纠正期限，并保证在规定期限内及时改正。在上述规定期限内，基金管理人有权随时对通知事项进行复查，督促基金托管人改正。\r\n 基金托管人应积极配合基金管理人的核查行为，包括但不限于：提交相关资料以供基金管理人核查托管财产的完整性和真实性，在规定时间内答复基金管理人并改正。\r\n （3）基金管理人发现基金托管人有重大违规行为，应及时报告中国证监会，同时通知基金托管人限期纠正，并将纠正结果报告中国证监会。基金托管人无正当理由，拒绝、阻挠对方根据本协议规定行使监督权，或采取拖延、欺诈等手段妨碍对方进行有效监督，情节严重或经基金管理人提出警告仍不改正的，基金管理人应报告中国证监会。\r\n （三）基金财产的保管\r\n 1、基金财产保管的原则\r\n （1）基金财产应独立于基金管理人、基金托管人的固有财产。\r\n （2）基金托管人应安全保管基金财产。\r\n （3）基金托管人按照规定开设基金财产的资金账户和证券账户。\r\n （4）基金托管人对所托管的不同基金财产分别设置账户，确保基金财产的完整与独立。\r\n （5）基金托管人根据基金管理人的指令，按照基金合同和本协议的约定保\r\n 管基金财产，如有特殊情况双方可另行协商解决。\r\n （6）对于因为基金投资产生的应收资产，应由基金管理人负责与有关当事\r\n 人确定到账日期并通知基金托管人，到账日基金财产没有到达基金账户的，基金托管人应及时通知基金管理人采取措施进行催收。由此给基金财产造成损失的，基金管理人应负责向有关当事人追偿基金财产的损失，基金托管人对此不承担任何责任。\r\n （7）除依据法律法规和基金合同的规定外，基金托管人不得委托第三人托管基金财产。\r\n 2、基金募集期间及募集资金的验资\r\n （1）基金募集期间募集的资金应存于中国证券登记结算有限责任公司的“基金认购专户”。\r\n （2）基金募集期满或基金停止募集时，募集的基金份额总额、基金募集金\r\n 额、基金份额持有人人数符合《基金法》、《运作办法》等有关规定后，基金管理人应将属于基金财产的全部资金划入基金托管人开立的基金银行账户，同时在规定时间内，聘请具有从事证券相关业务资格的会计师事务所进行验资，出具验资报告。出具的验资报告由参加验资的 2 名或 2 名以上中国注册会计师签字方为有效。\r\n （3）若基金募集期限届满，未能达到基金合同生效的条件，由基金管理人按规定办理退款等事宜。\r\n 3、基金银行账户的开立和管理\r\n （1）基金托管人可以基金的名义在其营业机构开立基金的银行账户，并根据基金管理人合法合规的指令办理资金收付。本基金的银行预留印鉴由基金托管人保管和使用。\r\n （2）基金银行账户的开立和使用，限于满足开展本基金业务的需要。基金托管人和基金管理人不得假借本基金的名义开立任何其他银行账户；亦不得使用基金的任何账户进行本基金业务以外的活动。\r\n （3）基金银行账户的开立和管理应符合银行业监督管理机构的有关规定。\r\n （4）在符合法律法规规定的条件下，基金托管人可以通过基金托管人专用账户办理基金资产的支付。\r\n 4、基金证券账户的开立和管理\r\n （1）基金托管人在中国证券登记结算有限责任公司上海分公司、深圳分公司为基金开立基金托管人与基金联名的证券账户。\r\n （2）基金证券账户的开立和使用，仅限于满足开展本基金业务的需要。基\r\n 金托管人和基金管理人不得出借或未经对方同意擅自转让基金的任何证券账户，亦不得使用基金的任何账户进行本基金业务以外的活动。\r\n （3）基金证券账户的开立和证券账户卡的保管由基金托管人负责，账户资产的管理和运用由基金管理人负责。\r\n （4）基金托管人以基金托管人的名义在中国证券登记结算有限责任公司开\r\n 立结算备付金账户，并代表所托管的基金完成与中国证券登记结算有限责任公司\r\n 的一级法人清算工作，基金管理人应予以积极协助。结算备付金、结算互保基金、交收价差资金等的收取按照中国证券登记结算有限责任公司的规定执行。\r\n （5）若中国证监会或其他监管机构在本托管协议订立日之后允许基金从事\r\n 其他投资品种的投资业务，涉及相关账户的开立、使用的，若无相关规定，则基金托管人比照上述关于账户开立、使用的规定执行。\r\n 5、债券托管专户的开设和管理\r\n 基金合同生效后，基金托管人根据中国人民银行、中央国债登记结算有限责任公司的有关规定，在中央国债登记结算有限责任公司开立债券托管账户，并代表基金进行银行间市场债券的结算。基金管理人和基金托管人共同代表基金签订全国银行间债券市场债券回购主协议。\r\n 6、其他账户的开立和管理\r\n （1）因业务发展需要而开立的其他账户，可以根据法律法规和基金合同的规定，由基金托管人负责开立。新账户按有关规定使用并管理。\r\n （2）法律法规等有关规定对相关账户的开立和管理另有规定的，从其规定办理。\r\n 7、基金财产投资的有关有价凭证等的保管基金财产投资的有关实物证券等有价凭证由基金托管人存放于基金托管人的保管库，也可存入中央国债登记结算有限责任公司、中国证券登记结算有限责任公司上海分公司/深圳分公司或票据营业中心的代保管库，保管凭证由基金托管人持有。实物证券等有价凭证的购买和转让，由基金管理人和基金托管人共同办理。基金托管人对由基金托管人以外机构实际有效控制的证券不承担保管责任。\r\n 8、与基金财产有关的重大合同的保管\r\n 与基金财产有关的重大合同的签署，由基金管理人负责。由基金管理人代表基金签署的、与基金财产有关的重大合同的原件分别由基金管理人、基金托管人保管。除本协议另有规定外，基金管理人代表基金签署的与基金财产有关的重大合同包括但不限于基金年度审计合同、基金信息披露协议及基金投资业务中产生的重大合同，基金管理人应保证基金管理人和基金托管人至少各持有一份正本的原件。基金管理人应在重大合同签署后及时以加密方式将重大合同传真给基金托管人，并在三十个工作日内将正本送达基金托管人处。重大合同的保管期限为基金合同终止后 15 年。\r\n （四）基金资产净值计算与复核\r\n 1、基金资产净值基金资产净值是指基金资产总值减去负债后的金额。\r\n 基金份额净值是指基金资产净值除以基金份额总数，基金份额净值的计算，精确到 0.001 元，小数点后第四位四舍五入，国家另有规定的，从其规定。\r\n 基金管理人每个工作日计算基金资产净值及基金份额净值，经基金托管人复核，按规定公告。\r\n 2、复核程序\r\n 基金管理人每开放日对基金资产进行估值后，将基金份额净值结果发送基金托管人，经基金托管人复核无误后，由基金管理人对外公布。\r\n 3、根据有关法律法规，基金资产净值计算和基金会计核算的义务由基金管理人承担。本基金的基金会计责任方由基金管理人担任，因此，就与本基金有关的会计问题，如经相关各方在平等基础上充分讨论后，仍无法达成一致意见的，按照基金管理人对基金资产净值的计算结果对外予以公布。\r\n （五）基金份额持有人名册的登记与保管基金份额持有人名册至少应包括基金份额持有人的名称和持有的基金份额。\r\n 基金份额持有人名册由基金注册登记机构根据基金管理人的指令编制和保管，基金管理人和基金托管人应分别保管基金份额持有人名册，保存期不少于 15 年。\r\n 如不能妥善保管，则按相关法规承担责任。\r\n 在基金托管人要求或编制半年报和年报前，基金管理人应将有关资料送交基金托管人，不得无故拒绝或延误提供，并保证其的真实性、准确性和完整性。基金托管人不得将所保管的基金份额持有人名册用于基金托管业务以外的其他用途，并应遵守保密义务。\r\n （六）争议解决方式\r\n 因本协议产生或与之相关的争议，双方当事人应通过协商、调解解决，协商、调解不能解决的，任何一方均有权将争议提交中国国际经济贸易仲裁委员会，仲裁地点为北京市，按照中国国际经济贸易仲裁委员会届时有效的仲裁规则进行仲裁。仲裁裁决是终局的，对当事人均有约束力，仲裁费用由败诉方承担。\r\n 争议处理期间，双方当事人应恪守基金管理人和基金托管人职责，各自继续忠实、勤勉、尽责地履行基金合同和本托管协议规定的义务，维护基金份额持有人的合法权益。\r\n 本协议适用中华人民共和国法律并从其解释。\r\n （七）托管协议的变更、终止\r\n 1、托管协议的变更程序\r\n 本协议双方当事人经协商一致，可以对协议进行修改。修改后的新协议，其内容不得与基金合同的规定有任何冲突。基金托管协议的变更报中国证监会核准或备案后生效。\r\n 2、基金托管协议终止出现的情形\r\n （1）基金合同终止；\r\n （2）基金托管人解散、依法被撤销、破产或由其他基金托管人接管基金资产；\r\n （3）基金管理人解散、依法被撤销、破产或由其他基金管理人接管基金管理权；\r\n （4）发生法律法规或基金合同规定的终止事项。\r\n 二十二、基金份额持有人服务长信基金管理有限责任公司（以下简称“本公司”，或“基金管理人”，或“公司”）将为基金份额持有人提供一系列的服务，并根据基金份额持有人的需要和市场的变化，增加或变更服务项目。基金份额持有人可以通过销售网点、客户服务中心、网站等渠道享受全方位、全过程的服务。以下是主要的服务方式和内容：\r\n 基金份额持有人服务内容\r\n 序号 类型 明细 内容详述\r\n （一） 账户服务对账单寄送服务\r\n 每次交易结束后，投资者可在 T＋2 个工作日后通过销售机构的网点查询和打印确认单；\r\n 每月结束后的 15 个工作日内，基金管理人向本月发生的新开户并进行基金交易的投资者以书面或电子形式寄送交易对账单；\r\n 根据投资者的对账单定制需求以书面或电子形式寄送交易对账单。\r\n 其他资料 基金管理人将按基金份额持有人的需求不定期向其邮寄相关公司介绍和产品介绍的资料。\r\n （二） 查询服务网络在线查询客户通过基金账户号码或开户证件号码和查询密码登录基\r\n 金管理人网站“账户查询”栏目，可享有账户查询、短信/邮件信息定制、资料修改、在线咨询等多项在线服务。\r\n 交易信息查询\r\n 在一笔交易结束后，投资者可在 T+2 个工作日开始通过销售\r\n 机构的网点或登录基金管理人网站“账户查询”栏目查询交易情况，包括客户购买总金额、基金购买份额、基金分红份额、历史交易信息等等。\r\n 客户账户信息的修改基金份额持有人可以直接登录基金管理人网站修改账户的\r\n 非重要信息，如联系地址、电话、电子邮箱等等。也可以亲自到网点或致电客户服务专线，由人工坐席提供相关服务。\r\n 为了维护投资者的利益，投资者重要信息的更改手续办理如下：\r\n 1、代销客户：由代销渠道提交办理（具体提供材料请咨询代销机构）。以配号方式开立的开放式基金账户资料中的投资者名称、证件类型、证件号码的变更业务，在一个工作日内，对单个开放式基金账户只能够修改其中一项关键信息。\r\n 2、直销客户：非正常变更需要提供本人身份证复印件、公安机关证明原件以及开放式基金账户业务申请表；正常变更\r\n 需要提供身份证复印件和开放式基金账户业务申请表，邮寄到本公司。\r\n （三）基金投资服务网上交易投资者除可通过销售机构和基金管理人的直销网点办理申\r\n 购、赎回及信息查询外，还可通过基金管理人的网站（www.cxfund.com.cn）享受网上交易服务。具体业务规则详见本公司网站说明。\r\n 定期定额投资计划\r\n 通过定期定额投资计划，投资人可以通过固定的渠道，定期定额申购基金份额。定期定额投资计划的有关规则和开放时间另行公告。\r\n 定期不定额投资计划\r\n 通过定期不定额投资计划，投资者可以通过我公司“长金通”网上直销平台提交交易申请，约定“基金品种”、“每月扣款日期”（或“每月转换日期”）、“基准金额”（或“基准份额”）、“标的指数”、“均线种类”、“级差”等参数，用指定的计算方法计算出投资金额（或投资份额），委托我公司完成申购或转换交易的一种基金投资方式。\r\n 定期不定额转换通过定期不定额转换投资计划，投资者可以通过本公司“长金通”网上直销平台提交交易申请，约定“基金品种”、“每月转换日期”、“基准份额”、“标的指数”、“均线种类”、“级差”等参数，用指定的计算方法计算出投资份额，委托本公司完成交易的一种基金投资方式。\r\n 红利再投资\r\n 本基金收益分配时，经投资者选择，基金管理人将为持有人提供红利再投资服务，其分红资金按除息日的基金份额净值自动转成相应的基金份额。红利再投资免收申购费用。基金份额持有人可以随时（权益登记日申请设置的分红方式对当次分红无效）选择更改基金分红方式。\r\n （四）客户服务中心电话服务\r\n 客户服务中心自动语音系统提供 7×24 小时基金净值信息、账户交易情况、基金产品与服务等信息查询。\r\n 客户服务中心人工坐席提供每周 5 天的坐席服务，投资者可以通过该专线获得业务咨询、信息查询、服务投诉、信息定制、资料修改等专项服务。\r\n （五）客户投诉受理服务客户投诉处理流程\r\n 本公司客户投诉受理由客户服务中心统一管理，指定专人负责，设定专门的投诉管理工作流程，并由监察稽核部负责督促投诉的处理情况。\r\n 客户投诉方式\r\n 投资者可通过本公司的客户服务专线（400-700-5566）、代销机构、公司网站（www.cxfund.com.cn）、电子邮件(service@cxfund.com.cn)、信件、传真(021-61009800) 、各销售机构网点柜台等多种形式对本公司所提供的服务以及公司的政策规定进行投诉。客户投诉都将被定期汇总登记并存档，通过拨打客户服务专线进行的投诉将被电话录音存档，本公司将采取适当措施，及时妥善处理客户投诉，总结相关问题，完善内控制度。\r\n （六） 增值服务信息定制服务基金份额持有人可以在基金管理人网站或致电客户服务专\r\n 线定制自己所需要的信息，包括产品净值、交易确认、分红公告、公司新闻、基金信息、账户信息等方面的内容。基金管理人按照要求，将以手机短信或者电子邮件的方式定期向投资者发送信息，投资者也可以直接登录基金管理人网站浏览相关信息。\r\n 个性化理财服务\r\n 随着公司的发展，本公司将酌情为基金份额持有人提供个性化理财服务，如配备理财顾问为基金份额持有人提供理财建议以及相关的理财计划等形式。\r\n 组织投资者交流会\r\n 本公司将不定期地举行投资者交流会，为基金份额持有人提供基金、投资、理财等方面的讲座，使得本公司基金份额持有人能得到更多的理财信息和其他增值服务。另外，本公司基金经理也将通过多种方式不定期地与基金份额持有人交流，让基金份额持有人了解更多基金运作情况。\r\n （七）投资者教育服务\r\n 为了进一步做好投资者服务，让投资者了解证券市场和各类\r\n 证券投资品种的特点和风险，熟悉证券市场的法律法规，树立正确的投资理念，增强风险防范意识，依法维护自身合法权益，本公司将开展普及证券知识、宣传政策法规、揭示市场风险、引导依法维权等投资者教育活动。\r\n （八）公开信息披露服务披露公司信息\r\n 为方便社会公众了解公司的信息，包括本公司的发展概况、组织结构、公司文化、经营理念、经营管理层、经营情况等公开信息，本公司开通了全国统一的客户服务专线\r\n 400-700-5566 （ 免 长 话 费 ） 和 公 司 网 站（www.cxfund.com.cn），以方便投资者查询。\r\n 披露基金信息本公司将按规定在中国证监会指定的信息披露媒体上披露\r\n 法定的文件、公告信息。\r\n 本公司将通过客户服务中心、公司网站、代销机构及相关基金宣传资料来披露本基金相关信息，包括本基金的概况、投资理念、投资对象、风险收益特征、净值及其变化情况、基金经理介绍等多方面的信息。\r\n 本公司将通过客户服务中心、公司网站、代销机构等途径来告知认购、申购、赎回的手续、流程、费用和其他注意事项，基金投资者可以通过登陆公司网站下载相关表格。\r\n 其他信息的披露本公司客户服务中心和网站除为投资者提供上述信息咨询外，还提供其他信息咨询，包括托管人的情况、基金知识、市场新闻和行情、产品信息等多方面内容。\r\n （九）客户服务联络方式客户服务\r\n 专线 4007005566（免长话费）、工作时间内可转人工坐席。\r\n 传真 021-61009800-1000/1001\r\n 公司网址 http://www.cxfund.com.cn\r\n 电子信箱 service@cxfund.com.cn二十三、其它应披露的事项\r\n 序号 公告事项 法定披露方式 法定披露日期\r\n 1 （2013 年第【1】号）及摘要 上证报、中证报 2013-3-15\r\n 2 长信恒利优势股票型证券投资基金 2012年年报及摘要（仅摘要见报） 中证报、上证报 2013-3-26\r\n 3长信基金管理有限责任公司关于旗下部分开放式基金参加中国工商银行股份有限公司个人电子银行基金申购费率优惠活动的公告\r\n 上证报、中证报、证券时报 2013-3-28\r\n 4长信基金管理有限责任公司关于旗下部分开放式基金在上海好买基金销售有限公司基金电子交易平台开通定期定额投资业\r\n 务、及参加定期定额投资申购费率优惠活动的公告\r\n 上证报、中证报、证券时报、证券日报 2013-3-28\r\n 5长信基金管理有限责任公司关于增加和讯信息科技有限公司为旗下部分开放式基金\r\n 代销机构并开通定期定额投资业务、参加申购（含定投申购）费率优惠活动及开通转换业务的公告\r\n 上证报、中证报、证券时报、证券日报 2013-4-17\r\n 6 长信恒利优势股票型证券投资基金 2013年第 1季度报告 中证报、上证报 2013-4-22\r\n 7长信基金管理有限责任公司关于旗下部分开放式基金继续参加重庆银行基金网上申购和定期定额投资业务费率优惠活动的公告\r\n 上证报、中证报、证券时报 2013-5-21\r\n 8 长信基金管理有限责任公司关于旗下基金2013 年半年度末资产净值的公告\r\n 上证报、中证报、证券时报 2013-7-1\r\n 9长信基金管理有限责任公司关于旗下部分开放式基金参加交通银行股份有限公司网\r\n 上银行、手机银行基金申购费率优惠活动的公告\r\n 上证报、中证报、证券时报 2013-7-2\r\n 10 长信恒利优势股票型证券投资基金 2013年第 2季度报告 中证报、上证报 2013-7-18\r\n 11长信基金管理有限责任公司关于旗下部分开放式基金在申银万国证券股份有限公司\r\n 开通定期定额投资业务、及参加网上银行、电话委托申购基金申购费率优惠活动的公告\r\n 上证报、中证报、证券时报、证券日报 2013-7-19本信息披露事项截止时间为 2013 年 7 月 30 日二十四、招募说明书存放及查阅方式\r\n 本基金招募说明书（更新）存放在基金管理人、基金代销机构和注册登记机构的办公场所，投资人可在办公时间查阅。在支付工本费后，可在合理时间内取得上述文件的复制件或复印件。\r\n 基金管理人和基金托管人保证文本的内容与公告的内容完全一致。\r\n 二十五、备查文件\r\n 本基金备查文件包括：\r\n （一）中国证监会批准长信恒利优势股票型证券投资基金募集的文件；\r\n （二）《长信恒利优势股票型证券投资基金基金合同》；\r\n （三）《长信恒利优势股票型证券投资基金托管协议》；\r\n （四）法律意见书；\r\n （五）基金管理人业务资格批件、营业执照；\r\n （六）基金托管人业务资格批件、营业执照；\r\n （七）基金销售代理人业务资格批件和营业执照；\r\n （八）中国证监会要求的其他文件。\r\n 长信基金管理有限责任公司\r\n 二○一三年九月十二日\r\n ", "url": "无", "title": ["无"], "source_domain": "集团"} +{"raw_content": " 招商安本增利债券型证券投资基金2008年度第4季度报告\r\n\r\n §1 重要提示\r\n 基金管理人的董事会及董事保证本报告所载资料不存在虚假记载、误导性陈述或重大遗漏，并对其内容的真实性、准确性和完整性承担个别及连带责任。\r\n 基金托管人中国光大银行股份有限公司根据本基金合同规定，于复核了本报告中的财务指标、净值表现和投资组合报告等内容，保证复核内容不存在虚假记载、误导性陈述或者重大遗漏。　　\r\n 基金管理人承诺以诚实信用、勤勉尽责的原则管理和运用基金资产，但不保证基金一定盈利。　　\r\n 基金的过往业绩并不代表其未来表现。投资有风险，投资者在作出投资决策前应仔细阅读本基金的招募说明书。\r\n 本报告中财务资料未经审计。\r\n 本报告期自起至止。\r\n §2 基金产品概况\r\n基金简称 招商安本增利\r\n交易代码 217008\r\n基金运作方式 契约型开放式\r\n基金合同生效日 2006年07月11日\r\n报告期末基金份额总额 1,792,264,404.34份\r\n投资目标 在严格控制风险、维护基金本金安全的基础上，为基金份\r\n 额持有人谋求长期、稳定的资本增值。\r\n投资策略 1、资产配置 本基金对债券等固定收益类品种的投资比例\r\n 为80％-100％（其中现金或到期日在一年以内的政府债券\r\n 不低于5％）；股票等权益类品种的投资比例为0％-20％。\r\n 2、组合构建\r\n （1）货币市场工具投资：将以严谨的市场价值分析为基\r\n 础，采用稳健的投资组合策略，通过对短期金融工具的\r\n 组合操作，在保持资产流动性的同时，追求稳定的投资\r\n 收益。\r\n （2）债券（不含可转债）投资：将采取积极主动的投\r\n 资策略，以中长期利率趋势分析为基础,结合中长期\r\n 的经济周期、宏观政策方向及收益率曲线分析,实施积\r\n 极的债券投资组合管理，以获取较高的债券组合投资\r\n 收益。\r\n （3）可转债投资：主要采用可转债相对价值分析策\r\n 略。通过分析不同市场环境下其股性和债性的\r\n 相对价值，把握可转债的价值走向，选择相应券种，\r\n 从而获取较高投资收益。\r\n （4）股票投资：在参与股票一级市场投资的过程中，\r\n 将全面深入地把握上市公司基本面，深入发掘新股内\r\n 在价值，结合市场估值水平和股市投资环境，充分发\r\n 挥本基金作为机构投资者在新股询价发行过程中的对\r\n 新股定价所起的积极作用，积极参与新股的申购、询\r\n 价与配售，有效识别并防范风险，以获取较好收益。\r\n 当本基金管理人判断市场出现明显的投资机会，或\r\n 行业（个股）的投资价值被明显低估时，本基金可\r\n 以直接参与股票二级市场投资，努力获取超额收益。\r\n 在构建二级市场股票投资组合时，本基金管理人强\r\n 调将定量的股票筛选和定性的公司研究有机结合，挖\r\n 掘价值被低估的股票。\r\n业绩比较基准 三年期银行定期存款税后利率＋20bp\r\n风险收益特征 本基金为流动性好、低风险、稳健收益类产品，可以满足\r\n 追求本金安全基础上持续稳定收益的个人和机构投资者\r\n 的投资需求。\r\n基金管理人 招商基金管理有限公司\r\n基金托管人 中国光大银行股份有限公司\r\n §3主要财务指标和基金净值表现\r\n 3.1主要财务指标\r\n 单位：人民币元\r\n主要财务指标 报告期(2008年10月01日-2008年12月31日)\r\n1.本期已实现收益 29,018,135.41\r\n2.本期利润 79,493,373.28\r\n3.加权平均基金份额本期利润 0.0389\r\n4.期末基金资产净值 2,057,797,943.24\r\n5.期末基金份额净值 1.1482\r\n 注：1、上述基金业绩指标不包括持有人认购或交易基金的各项费用，计入费用后实际收益水平要低于所列数字。\r\n 2、本期已实现收益指基金本期利息收入、投资收益、其他收入（不含公允价值变动收益）扣除相关费用后的余额，本期利润为本期已实现收益加上本期公允价值变动收益。\r\n 3.2 基金净值表现\r\n 本报告期基金份额净值增长率及其与同期业绩比较基准收益率的比较\r\n阶段 净值增 净值增 业绩比 业绩比较 ①-③ ②-④\r\n- 长率① 长率标 较基准 基准收益\r\n- - 准差② 收益率③ 率标准差④\r\n过去三个月 3.57% 0.14% 1.18% 0.01% 2.39% 0.13%\r\n 自基金合同生效以来基金累计份额净值增长率变动及其与同期业绩比较基准收益率变动的比较\r\n 注：根据基金合同第十二条（三）投资方向的规定：本基金对债券等固定收益类品种的投资比例为80%-100%（其中现金或到期日在一年以内的政府债券不低于5%）；股票等权益类品种的投资比例为0%-20%。截至,本基金投资于股票等权益类品种占基金净资产为0.02%，投资于债券等固定收益类品种占基金净资产为97.94%，现金或到期日在一年以内的政府债券占基金净资产为5.82% ，符合上述规定的要求。\r\n §4 管理人报告\r\n 4.1 基金经理（或基金经理小组）简介\r\n姓名 职务 任本基金的基金经理期限 证券从 说明\r\n 任职日期 离任日期 业年限\r\n汪仪 本基金及招 2008年12月31日 - 4.00 汪仪，男，中国国\r\n 商安泰系列 籍，中南财经政法大\r\n 开放式证券 学经济学硕士。曾任\r\n 投资基金基 职于广州市商业银行\r\n 金经理 股份有限公司金融同\r\n 业部、国投瑞银基金\r\n 管理有限公司，从事\r\n 资金管理、资金及债\r\n 券交易以及固定收益\r\n 投资研究工作；2007\r\n 年加入招商基金管理\r\n 有限公司，曾任固定\r\n 收益投资部高级研究\r\n 员，现任招商安泰系\r\n 列开放式证券投资基\r\n 金及招商安本增利债\r\n 券型证券投资基金基\r\n 金经理。\r\n胡军华 本基金及招 2006年07月11日 2008年12月31日 16.00 胡军华，女，中国国\r\n 商安泰系列 籍，中国人民大学经\r\n 开放式证券 济学硕士。曾任职于\r\n 投资基金基 中国经济开发信托投\r\n 金经理；本 资公司、华鑫证券深\r\n 公司固定收 圳证券营业、南方证\r\n 益投资部 券任债券业务总部，\r\n 总监 一直从事证券研究及\r\n 投资管理相关工作；\r\n 2003年3月起加盟招\r\n 商基金管理有限公\r\n 司，曾任固定收益投\r\n 资部高级研究员，副\r\n 总监及总监。胡军华\r\n 女士于2005年8月\r\n 24日起至2008年12月\r\n 30日担任本基金基\r\n 金经理。\r\n 4.2 管理人对报告期内本基金运作遵规守信情况的说明\r\n 本报告期内，本基金管理人严格遵守《中华人民共和国证券投资基金法》、《证券投资基金运作管理办法》、《招商安本增利债券型证券投资基金基金合同》等有关法律法规及其各项实施准则的规定，本着诚实信用、勤勉尽责的原则管理和运用基金资产，在严格控制风险的前提下，为基金持有人谋求最大利益。本报告期内，基金运作整体合法合规，无损害基金持有人利益的行为。基金的投资范围、投资比例及投资组合符合有关法律法规及基金合同的规定。\r\n 4.3 公平交易专项说明\r\n 公平交易制度的执行情况\r\n 基金管理人（以下简称“公司”）已建立较完善的研究方法和投资决策流程，确保各投资组合享有公平的投资决策机会。公司建立了所有组合适用的投资对象备选库，制定明确的备选库建立、维护程序。公司拥有健全的投资授权制度，明确投资决策委员会、投资总监、投资组合经理等各投资决策主体的职责和权限划分，投资组合经理在授权范围内可以自主决策，超过投资权限的操作需要经过严格的审批程序。公司固定收益投资研究成果向内部所有投资组合开放，在投资研究层面不存在各投资组合间不公平的问题。交易层面，公司交易部在至期间，对所有组合的各条指令，均在中央交易员的统一分派下，执行了公平交易。\r\n 本投资组合与其他投资风格相似的投资组合之间的业绩比较\r\n 报告期内，本组合不存在与公司管理的其他投资风格相似的组合之间的业绩表现差异超过5%的情况。\r\n 异常交易行为的专项说明\r\n 报告期间内，本组合未发生任何重大异常交易情况。\r\n 4.4 报告期内基金的投资策略和业绩表现说明\r\n 行情回顾及运作分析\r\n (1)股票市场\r\n 2008年四季度股票市场呈现探底弱势反弹继而下跌的震荡走势，四季度上证指数下跌20.75％，行业、板块间分化加剧。\r\n 分析四季度的股市运行环境，出于谨慎性考虑，本报告期本基金回避了股票市场的投资。\r\n (2)债券市场\r\n 2008年第四季度债券市场在宏观经济快速恶化，CPI大幅回落和央行不断减息的利好支持下，债券收益率继续下降，债市继续大幅上涨，以中债总指数为例，第四季度的涨幅为7.10%，长期限的债券回报居前。\r\n 关于本基金操作，我们在第四季度适时加大了组合的久期，较好的把握了市场的上涨行情。\r\n 基金表现\r\n 截至报告期末，本基金份额净值为1.1482元，本报告期份额净值增长率3.57%，同期业绩比较基准指数增长率为1.18%，超越指数2.39%，主要原因在于本基金较好的捕捉到市场热点板块，适时加大了组合久期，同时，回避了权益投资。\r\n 市场展望与投资策略\r\n (1) 股票市场\r\n 市场展望：\r\n 对于股票市场，2008年是不堪回首的一年。经过2008年的大幅下跌，我们认为2009年证券市场的环境将好于2008年，市场再次出现大幅下跌的概率较小，但是影响市场的利好、利空因素交织在一起，股票市场将以弱势震荡的方式运行。\r\n 不能回避的是，如果外围经济，尤其是美国经济如能在下半年开始企稳，并恢复低速增长，中国经济形势则将会开始好转，这将对股市构成实质性利好。\r\n 基于对市场的判断，本基金2009年在股票仓位方面计划将采取更加灵活的方式；在组合构建方面，将以稳定增长性行业构建组合的核心仓位、保证组合的防御性，对稳定增长个股、预期盈利改观以及盈利处于周期底部区域且估值处于价值投资区域的个股，进行重点配置。\r\n (2) 债券市场\r\n 市场展望：\r\n 展望2009年的债券市场，基于目前较为严峻的宏观经济形势，和处于低位的债券收益率，我们认为2009年债券市场将在一个高位水平进行盘整，大幅上涨和下跌的机会都不是很大。2009年将更为挑战债券基金的投资能力。2009年我们将重点关注国内外宏观经济数据和走势、政府保证经济增长的政策效果、外围经济能否企稳等因素，在利率下行的时候增配利率产品，在宏观经济见底和企业盈利好转的时候增配信用产品，挖掘超额收益。\r\n 投资策略：\r\n 基于对市场的判断，本基金2009年在固定收益组合构建方面计划将采取更加灵活的方式；若上半年经济数据继续呈现恶化态势的情况下，加大配置利率产品，以享受央行采取继续降息等宽松货币政策而带来的超额收益，若下半年经济企稳，企业赢利状况得到改善后，利率产品收益率很难再有大幅下降的空间时，加大对信用债券的配置力度，以获取超额收益。\r\n 站在目前的时点，展望2009年，无论是对股票投资而言，还是对债券投资，不确定性明显大于2008年，集中体现在对宏观经济走势的判断上。“这是最好的年代，也是最差的年代”，对于股票而言，可能投资的机会大于投机，而债券则恰恰相反，处于低位的收益率形同鸡肋，可能投机的机会大于投资。\r\n 不确定性的加大将对本基金管理人的投资能力提出严峻的挑战，我们也将更加努力的工作，以理想的业绩增长来回报持有人的支持。\r\n §5投资组合报告\r\n 5.1报告期末基金资产组合情况\r\n序号 项目 金额（元） 占基金总资\r\n 产的比例（%）\r\n1 权益投资 431,230.50 0.02\r\n 其中：股票 431,230.50 0.02\r\n2 固定收益投资 2,015,504,811.16 97.77\r\n 其中：债券 1,997,766,811.16 96.91\r\n 资产支持证券 17,738,000.00 0.86\r\n3 金融衍生品投资 - -\r\n4 买入返售金融资产 - -\r\n 其中：买断式回购的买入返售金融资产 - -\r\n5 银行存款和结算备付金合计 17,275,255.95 0.84\r\n6 其他资产 28,183,264.02 1.37\r\n7 合计 2,061,394,561.63 100.00\r\n 5.2 报告期末按行业分类的股票投资组合\r\n代码 行业类别 公允价值（元） 占基金资产净值比例(%)\r\nA 农、林、牧、渔业 - -\r\nB 采掘业 - -\r\nC 制造业 - -\r\nC0 食品、饮料 - -\r\nC1 纺织、服装、皮毛 - -\r\nC2 木材、家具 - -\r\nC3 造纸、印刷 - -\r\nC4 石油、化学、塑胶、塑料 - -\r\nC5 电子 - -\r\nC6 金属、非金属 - -\r\nC7 机械、设备、仪表 - -\r\nC8 医药、生物制品 - -\r\nC99 其他制造业 - -\r\nD 电力、煤气及水的生产和供应业 - -\r\nE 建筑业 - -\r\nF 交通运输、仓储业 - -\r\nG 信息技术业 431,230.50 0.02\r\nH 批发和零售贸易 - -\r\nI 金融、保险业 - -\r\nJ 房地产业 - -\r\nK 社会服务业 - -\r\nL 传播与文化产业 - -\r\nM 综合类 - -\r\n 合计 431,230.50 0.02\r\n 5.3 报告期末按公允价值占基金资产净值比例大小排序的前十名股票投资明细\r\n序号 股票代码 股票名称 数量（股） 公允价值（元） 占基金资产净值比例（％）\r\n1 002261 拓维信息 16,911 431,230.50 0.02\r\n 5.4报告期末按债券品种分类的债券投资组合\r\n序号 债券品种 公允价值（元） 占基金资产净值比例（％）\r\n1 国家债券 278,468,329.40 13.53\r\n2 央行票据 624,684,000.00 30.36\r\n3 金融债券 587,345,000.00 28.54\r\n- 其中：政策性金融债 577,238,000.00 28.05\r\n4 企业债券 392,840,356.70 19.09\r\n5 企业短期融资券 91,395,000.00 4.44\r\n6 可转债 23,034,125.06 1.12\r\n7 其他 - -\r\n8 合计 1,997,766,811.16 97.08\r\n 5.5 报告期末按公允价值占基金资产净值比例大小排名的前五名债券投资明细\r\n序号 债券代码 债券名称 数量（张） 公允价值（元） 占基金资产\r\n 净值比例（％）\r\n1 0801047 08央行票据47 2,000,000 213,800,000.00 10.39\r\n2 080022 08国债22 1,500,000 155,325,000.00 7.55\r\n3 080419 08农发19 1,500,000 155,280,000.00 7.55\r\n4 0801053 08央行票据53 1,000,000 107,010,000.00 5.20\r\n5 080218 08国开18 1,000,000 104,710,000.00 5.09\r\n 5.6 报告期末按公允价值占基金资产净值比例大小排名的前十名资产支持证券投资明细\r\n序号 证券代码 证券名称 数量（份） 公允价值(元) 占基金资产净值比例(%)\r\n1 0734011 07浦元1A 200,000 12,738,000.00 0.62\r\n2 119010 天电收益 100,000 5,000,000.00 0.24\r\n 5.7 报告期末按公允价值占基金资产净值比例大小排名的前五名权证投资明细\r\n 本基金本报告期末未持有权证。\r\n 5.8 投资组合报告附注\r\n 5.8.1 声明本基金投资的前十名证券的发行主体本期是否出现被监管部门立案调查，或在报告编制日前一年内受到公开谴责、处罚的情形。 本报告期内基金投资的前十名证券的发行主体未被监管部门立案调查，在报告编制日前一年内未受到过公开谴责、处罚。\r\n 5.8.2 声明基金投资的前十名股票是否超出基金合同规定的备选股票库。 基金投资的前十名股票中，未有投资于超出备选股票库之外的股票。\r\n 其他资产构成\r\n序号 名称 金额（元）\r\n1 存出保证金 750,000.00\r\n2 应收证券清算款 -\r\n3 应收股利 -\r\n4 应收利息 26,469,558.50\r\n5 应收申购款 963,705.52\r\n6 其他应收款 -\r\n7 待摊费用 -\r\n8 其他 -\r\n9 合计 28,183,264.02\r\n 报告期末持有的处于转股期的可转换债券明细\r\n序号 债券代码 债券名称 公允价值(元) 占基金资产净值比例（％）\r\n1 110002 南山转债 23,034,125.06 1.12\r\n 报告期末前十名股票中存在流通受限情况的说明\r\n 本基金本报告期末前十名股票中不存在流通受限的情况。\r\n 5.8.6 投资组合报告附注的其他文字描述部分\r\n 报告期内本基金未投资控股股东主承销的证券，未从二级市场主动投资分离交易可转债附送的权证，投资流通受限证券未违反相关法规或本基金管理有限公司的规定。\r\n §6 开放式基金份额变动\r\n 单位：份\r\n报告期期初基金份额总额 2,378,048,371.67\r\n报告期期间基金总申购份额 39,849,679.80\r\n报告期期间基金总赎回份额 625,633,647.13\r\n报告期期间基金拆分变动份额（份额减少以\"-\"填列） -\r\n报告期期末基金份额总额 1,792,264,404.34\r\n §7 影响投资者决策的其他重要信息\r\n 无。\r\n §8 备查文件目录\r\n 8.1 备查文件目录\r\n 1、中国证监会批准招商安本增利债券型证券投资基金设立的文件；\r\n 2、基金管理人业务资格批件、营业执照和公司章程；\r\n 3、基金托管人业务资格批件和营业执照；\r\n 4、《招商安本增利债券型证券投资基金基金合同》；\r\n 5、《招商安本增利债券型证券投资基金招募说明书》；\r\n 6、《招商安本增利债券型证券投资基金托管协议》；\r\n 7、《招商安本增利债券型证券投资基金2008年第4季度报告》。\r\n 8.2 存放地点\r\n 招商基金管理有限公司\r\n 地址：中国深圳深南大道7088号招商银行大厦\r\n 8.3 查阅方式\r\n 基金选定的信息披露报纸名称:上海证券报\r\n 登载季度报告的管理人互联网网址：http://www.cmfchina.com\r\n 招商基金管理有限公司\r\n", "url": "无", "title": ["无"], "source_domain": "集团"} +{"raw_content": " 关于嘉实丰和灵活配置混合型证券投资基金\r\n 开放日常申购、赎回、转换及定投业务的公告公告送出日期：2017 年 5 月 26 日\r\n 1 公告基本信息\r\n 基金名称 嘉实丰和灵活配置混合型证券投资基金\r\n 基金简称 嘉实丰和灵活配置混合\r\n 基金主代码 004355\r\n 基金运作方式 契约型、开放式基金合同生效日 2017 年 3 月 20 日\r\n 基金管理人名称 嘉实基金管理有限公司\r\n 基金托管人名称 中国农业银行股份有限公司\r\n 基金注册登记机构名称 嘉实基金管理有限公司\r\n 公告依据 《中华人民共和国证券投资基金法》、《证券投资基金运作管理办法》等法律法规以及《嘉实丰和灵活配置混合型证券投资基金基金合同》、《嘉实丰和灵活配置混合型证券投资基金招募说明书》\r\n 申购起始日 2017 年 5 月 31 日\r\n 赎回起始日 2017 年 5 月 31 日\r\n 转换转入起始日 2017 年 5 月 31 日\r\n 转换转出起始日 2017 年 5 月 31 日\r\n 定期定额投资起始日 2017 年 5 月 31 日\r\n 注：投资者范围：符合法律法规规定的可投资于证券投资基金的个人投资者、机构投资者和合格境外机构投资者以及法律法规或中国证监会允许购买证券投资基金的其他投资人。\r\n 2日常申购、赎回、转换和定期定额投资业务的办理时间\r\n 本基金自 2017 年 5 月 31 日开始办理基金份额的申购、赎回、转换和定投业务。本基金在开放日办理基金份额的申购、赎回、转换和定投，具体办理时间为上海证券交易所、深圳证券交易所的正常交易日的交易时间，但基金管理人根据法律法规、中国证监会的要求或本基金合同的规定公告暂停申购、赎回、转换和定投业务时除外。\r\n 基金合同生效后，若出现新的证券交易市场、证券交易所交易时间变更或其他特殊情况，基金管理人将视情况对前述开放日及开放时间进行相应的调整，但应在实施日前依照《信息披露办法》的有关规定在指定媒介上公告。\r\n 基金管理人不得在基金合同约定之外的日期或者时间办理基金份额的申购或者赎回或者转换。投资人在基金合同约定之外的日期和时间提出申购、赎回或转换申请且登记机构确认接收的，其基金份额申购、赎回、转换价格为下一开放日基金份额申购、赎回、转换的价格，并按照下一个开放日的申请处理。\r\n 3 日常申购业务\r\n 3.1 申购金额限制\r\n 投资者通过代销机构或嘉实基金管理有限公司网上直销首次申购单笔最低限额为人民币 10 元，追加申购单笔最低限额为人民币 10 元；投资者通过直销中心柜台首次申购单笔最低限额为人民币 20，000 元，追加\r\n 申购单笔最低限额为人民币 10 元。\r\n 投资者将当期分配的基金收益转购基金份额或采用定期定额投资计划时，不受最低申购金额的限制。\r\n 投资者可多次申购，对单个投资者累计持有份额不设上限限制。法律法规、中国证监会另有规定的除外。\r\n 3.2 申购费率\r\n 本基金基金份额前端申购费率按照申购金额递减，即申购金额越大，所适用的申购费率越低。投资者\r\n 在一天之内如果有多笔申购，适用费率按单笔分别计算。具体如下：\r\n 申购金额（含申购费） 申购费率\r\n M＜50 万元 1.5%\r\n 50 万元≤M＜200 万元 1.2%\r\n 200 万元≤M＜500 万元 0.8%\r\n M≥500 万元 按笔收取，单笔 1000 元\r\n 个人投资者通过本基金管理人直销网上交易系统申购本基金业务实行申购费率优惠，其申购费率不按申购金额分档，统一优惠为申购金额的 0.6％，但中国银行长城借记卡、中国农业银行借记卡持卡人，申购本基金的申购费率优惠按照相关公告规定的费率执行；机构投资者通过本基金管理人直销网上交易系统申购本基金，其申购费率不按申购金额分档，统一优惠为申购金额的 0.6％。优惠后费率如果低于 0.6％，则\r\n 按 0.6％执行。基金招募说明书及相关公告规定的相应申购费率低于 0.6%时，按实际费率收取申购费。个人\r\n 投资者于本公司网上直销系统通过汇款方式申购本基金的，前端申购费率按照相关公告规定的优惠费率执行。\r\n 注：2014年 9月 2日，本基金管理人发布了《嘉实基金管理有限公司关于增加开通后端收费基金产品的公告》，自 2017年 5月 31日起，增加开通本基金在本公司基金网上直销系统的后端收费模式（包括申购、定期定额投资、基金转换等业务）、并对通过本公司基金网上直销系统交易的后端收费进行费率优惠，本基金优惠后的费率见下表：\r\n 持有期限（T）基金网上直销后端申购优惠费率\r\n 0》及其他中国证监会及中国证券投资基金业协会颁布的相关规定。\n 本财务报表以本基金持续经营为基础列报。\n 7.4.3遵循企业会计准则及其他有关规定的声明\n 第24页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 本财务报表符合企业会计准则的要求，真实、完整地反映了本基金于2020年12月31日的财务状况以及2020年度的经营成果和净值变动情况。\n 7.4.4重要会计政策和会计估计\n 本基金财务报表所载财务信息依照企业会计准则、《证券投资基金会计核算业务指引》和其他相关规定所厘定的主要会计政策和会计估计编制。\n 7.4.4.1会计年度\n 本基金会计年度采用公历年度，即每年自1月1日起至12月31日止。\n 7.4.4.2记账本位币\n 本基金记账本位币和编制本财务报表所采用的货币均为人民币。除有特别说明外，均以人民币元为单位表示。\n 7.4.4.3金融资产和金融负债的分类\n 金融工具是指形成本基金的金融资产（或负债），并形成其他单位的金融负债（或资产）或权益工具的合同。\n (1)金融资产分类\n 本基金的金融资产于初始确认时分为以下两类：以公允价值计量且其变动计入当期损益的金融\n 资产、贷款和应收款项；\n 本基金持有的以公允价值计量且其变动计入当期损益的金融资产主要包括股票投资、债券投资和衍生工具等；\n (2)金融负债分类本基金的金融负债于初始确认时归类为以公允价值计量且其变动计入当期损益的金融负债和其他金融负债。本基金目前持有的金融负债划分为其他金融负债。\n 7.4.4.4金融资产和金融负债的初始确认、后续计量和终止确认\n 本基金于成为金融工具合同的一方时确认一项金融资产或金融负债。\n 划分为以公允价值计量且其变动计入当期损益的金融资产的股票、债券等，以及不作为有效套期工具的衍生工具，按照取得时的公允价值作为初始确认金额，相关的交易费用在发生时计入当期损益；\n 第25页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 在持有以公允价值计量且其变动计入当期损益的金融资产期间取得的利息或现金股利，应当确认为当期收益。每日，本基金将以公允价值计量且其变动计入当期损益的金融资产或金融负债的公允价值变动计入当期损益；\n 处置该金融资产或金融负债时，其公允价值与初始入账金额之间的差额应确认为投资收益，同时调整公允价值变动收益；\n 当收取该金融资产现金流量的合同权利终止，或该收取金融资产现金流量的权利已转移，且符合金融资产转移的终止确认条件的，金融资产将终止确认；\n 当金融负债的现时义务全部或部分已经解除的，该金融负债或其一部分将终止确认；\n 本基金已将金融资产所有权上几乎所有的风险和报酬转移给转入方的，终止确认该金融资产；\n 保留了金融资产所有权上几乎所有的风险和报酬的，不终止确认该金融资产；本基金既没有转移也没有保留金融资产所有权上几乎所有的风险和报酬的，分别下列情况处理：放弃了对该金融资产控制的，终止确认该金融资产并确认产生的资产和负债；未放弃对该金融资产控制的，按照其继续涉入所转移金融资产的程度确认有关金融资产，并相应确认有关负债。\n 本基金主要金融工具的成本计价方法具体如下：\n (1)股票投资\n 买入股票于成交日确认为股票投资，股票投资成本按成交日应支付的全部价款扣除交易费用入账；\n 卖出股票于成交日确认股票投资收益，卖出股票的成本按移动加权平均法于成交日结转；\n (2)债券投资买入债券于成交日确认为债券投资。债券投资成本按成交日应支付的全部价款扣除交易费用入账，其中所包含的债券应收利息单独核算，不构成债券投资成本；\n 买入零息债券视同到期一次性还本付息的附息债券，根据其发行价、到期价和发行期限按直线法推算内含票面利率后，按上述会计处理方法核算；\n 卖出债券于成交日确认债券投资收益，卖出债券的成本按移动加权平均法结转；\n (3)权证投资买入权证于成交日确认为权证投资。权证投资成本按成交日应支付的全部价款扣除交易费用后入账；\n 卖出权证于成交日确认衍生工具投资收益，卖出权证的成本按移动加权平均法于成交日结转；\n (4)股指期货投资\n 第26页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告买入或卖出股指期货投资于成交日确认为股指期货投资。股指期货初始合约价值按成交金额确认；\n 股指期货平仓于成交日确认衍生工具投资收益，股指期货的初始合约价值按移动加权平均法于成交日结转；\n (5)分离交易可转债\n 申购新发行的分离交易可转债于获得日，按可分离权证公允价值占分离交易可转债全部公允价值的比例将购买分离交易可转债实际支付全部价款的一部分确认为权证投资成本，按实际支付的全部价款扣减可分离权证确定的成本确认债券成本；\n 上市后，上市流通的债券和权证分别按上述(2)、(3)中相关原则进行计算；\n (6)回购协议\n 本基金持有的回购协议（封闭式回购），以成本列示，按实际利率（当实际利率与合同利率差异较小时，也可以用合同利率）在实际持有期间内逐日计提利息。\n 7.4.4.5金融资产和金融负债的估值原则\n 公允价值，是指市场参与者在计量日发生的有序交易中，出售一项资产所能收到或者转移一项负债所需支付的价格。本基金以公允价值计量相关资产或负债，假定出售资产或者转移负债的有序交易在相关资产或负债的主要市场进行；不存在主要市场的，本基金假定该交易在相关资产或负债的最有利市场进行。主要市场（或最有利市场）是本基金在计量日能够进入的交易市场。本基金采用市场参与者在对该资产或负债定价时为实现其经济利益最大化所使用的假设。\n 在财务报表中以公允价值计量或披露的资产和负债，根据对公允价值计量整体而言具有重要意义的最低层次输入值，确定所属的公允价值层次：第一层次输入值，在计量日能够取得的相同资产或负债在活跃市场上未经调整的报价；第二层次输入值，除第一层次输入值外相关资产或负债直接或间接可观察的输入值；第三层次输入值，相关资产或负债的不可观察输入值。\n 每个资产负债表日，本基金对在财务报表中确认的持续以公允价值计量的资产和负债进行重新评估，以确定是否在公允价值计量层次之间发生转换。\n 本基金持有的金融工具按如下原则确定公允价值并进行估值：\n (1)存在活跃市场的金融工具，按照估值日能够取得的相同资产或负债在活跃市场上未经调整的\n 报价作为公允价值；估值日无报价且最近交易日后未发生影响公允价值计量的重大事件的，应采用最近交易日的报价确定公允价值。有充足证据表明估值日或最近交易日的报价不能真实反映公允价值的，应对报价进行调整，确定公允价值。\n 第27页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 与上述投资品种相同，但具有不同特征的，应以相同资产或负债的公允价值为基础，并在估值技术中考虑不同特征因素的影响。特征是指对资产出售或使用的限制等，如果该限制是针对资产持有者的，那么在估值技术中不应将该限制作为特征考虑。此外，基金管理人不应考虑因大量持有相关资产或负债所产生的溢价或折价；\n (2)不存在活跃市场的金融工具，应采用在当前情况下适用并且有足够可利用数据和其他信息支\n 持的估值技术确定公允价值。采用估值技术确定公允价值时，应优先使用可观察输入值，只有在无法取得相关资产或负债可观察输入值或取得不切实可行的情况下，才使用不可观察输入值；\n (3)如有确凿证据表明按上述方法进行估值不能客观反映其公允价值的，基金管理人可根据具体\n 情况与基金托管人商定后，按最能反映公允价值的方法估值；\n (4)如有新增事项，按国家最新规定估值。\n 7.4.4.6金融资产和金融负债的抵销\n 当本基金具有抵销已确认金融资产和金融负债的法定权利，且该种法定权利现在是可执行的，同时本基金计划以净额结算或同时变现该金融资产和清偿该金融负债时，金融资产和金融负债以相互抵销后的金额在资产负债表内列示。除此以外，金融资产和金融负债在资产负债表内分别列示，不予相互抵销。\n 7.4.4.7实收基金\n 实收基金为对外发行的基金份额总额所对应的金额。由于申购和赎回引起的实收基金变动分别于基金申购确认日及基金赎回确认日确认。上述申购和赎回分别包括基金转换所引起的转入基金的实收基金增加和转出基金的实收基金减少。\n 7.4.4.8损益平准金\n 损益平准金包括已实现损益平准金和未实现损益平准金。已实现损益平准金指在申购或赎回基金份额时，申购或赎回款项中包含的按累计未分配的已实现收益/（损失）占基金净值比例计算的金额。未实现损益平准金指在申购或赎回基金份额时，申购或赎回款项中包含的按累计未实现利得（/损失）占基金净值比例计算的金额。损益平准金于基金申购确认日或基金赎回确认日确认。\n 未实现损益平准金与已实现损益平准金均在“损益平准金”科目中核算，并于期末全额转入“未分配利润/（累计亏损）”。\n 7.4.4.9收入/(损失)的确认和计量\n 第28页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n (1)存款利息收入按存款的本金与适用的利率逐日计提的金额入账。若提前支取定期存款，按协\n 议规定的利率及持有期重新计算存款利息收入，并根据提前支取所实际收到的利息收入与账面已确认的利息收入的差额确认利息损失，列入利息收入减项，存款利息收入以净额列示；\n (2)债券利息收入按债券票面价值与票面利率或内含票面利率计算的金额扣除应由债券发行企\n 业代扣代缴的个人所得税后的净额确认，在债券实际持有期内逐日计提；\n (3)资产支持证券利息收入按证券票面价值与票面利率计算的金额，扣除应由资产支持证券发行\n 企业代扣代缴的个人所得税后的净额确认，在证券实际持有期内逐日计提；\n (4)买入返售金融资产收入，按买入返售金融资产的成本及实际利率（当实际利率与合同利率差异较小时，也可以用合同利率），在回购期内逐日计提；\n (5)股票投资收益/（损失）于卖出股票成交日确认，并按卖出股票成交金额与其成本的差额入账；\n (6)债券投资收益/（损失）于成交日确认，并按成交总额与其成本、应收利息的差额入账；\n (7)资产支持证券投资收益/(损失)于成交日确认，并按成交总额与其成本、应收利息的差额入账；\n (8)权证收益/(损失)于卖出权证成交日确认，并按卖出权证成交金额与其成本的差额入账；\n (9)股指期货投资收益/(损失)于平仓日确认，并按平仓成交金额与其初始合约价值的差额入账；\n (10)股利收益于除息日确认，并按上市公司宣告的分红派息比例计算的金额扣除应由上市公司代扣代缴的个人所得税后的净额入账；\n (11)公允价值变动收益/（损失）系本基金持有的以公允价值计量且其变动计入当期损益的金融\n 资产、以公允价值计量且其变动计入当期损益的金融负债等公允价值变动形成的应计入当期损益的利得或损失；\n (12)其他收入在主要风险和报酬已经转移给对方，经济利益很可能流入且金额可以可靠计量的时候确认。\n 7.4.4.10费用的确认和计量\n 本基金的管理人报酬和托管费等在费用涵盖期间按基金合同或相关公告约定的费率和计算方法逐日确认。\n 其他金融负债在持有期间确认的利息支出按实际利率法计算，实际利率法与直线法差异较小的则按直线法计算。\n 7.4.4.11基金的收益分配政策\n 第29页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 在存续期内，本基金（包括华安中证证券份额、华安中证证券 A 份额、华安中证证券 B 份额）不进行收益分配。\n 7.4.5会计政策和会计估计变更以及差错更正的说明\n 7.4.5.1会计政策变更的说明\n 本基金本报告期无会计政策变更。\n 7.4.5.2会计估计变更的说明\n 本基金本报告期无会计估计变更。\n 7.4.5.3差错更正的说明\n 本基金本报告期无重大会计差错的内容和更正金额。\n 7.4.6税项\n 7.4.6.1印花税\n 经国务院批准，财政部、国家税务总局研究决定，自2008年4月24日起，调整证券（股票）交易印花税税率，由原先的3‰调整为1‰；\n 经国务院批准，财政部、国家税务总局研究决定，自2008年9月19日起，调整由出让方按证券（股票）交易印花税税率缴纳印花税，受让方不再征收，税率不变；\n 根据财政部、国家税务总局财税[2005]103号文《关于股权分置试点改革有关税收政策问题的通知》的规定，股权分置改革过程中因非流通股股东向流通股股东支付对价而发生的股权转让，暂免征收印花税。\n 7.4.6.2增值税\n 根据财政部、国家税务总局财税[2016]36号文《关于全面推开营业税改增值税试点的通知》的规定，经国务院批准，自2016年5月1日起在全国范围内全面推开营业税改征增值税试点，金融业纳入试点范围，由缴纳营业税改为缴纳增值税。对证券投资基金（封闭式证券投资基金，开放式证券投资基金）管理人运用基金买卖股票、债券的转让收入免征增值税；国债、地方政府债利息收入以及金融同业往来利息收入免征增值税；存款利息收入不征收增值税；\n 根据财政部、国家税务总局财税[2016]46号文《关于进一步明确全面推开营改增试点金融业有关政策的通知》的规定，金融机构开展的质押式买入返售金融商品业务及持有政策性金融债券取得的利息收入属于金融同业往来利息收入；\n 第30页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告根据财政部、国家税务总局财税[2016]70号文《关于金融机构同业往来等增值税政策的补充通知》的规定，金融机构开展的买断式买入返售金融商品业务、同业存款、同业存单以及持有金融债券取得的利息收入属于金融同业往来利息收入；\n 根据财政部、国家税务总局财税[2016]140号文《关于明确金融、房地产开发、教育辅助服务等增值税政策的通知》的规定，本基金运营过程中发生的增值税应税行为，以本基金的基金管理人为增值税纳税人；\n 根据财政部、国家税务总局财税[2017]56号文《关于资管产品增值税有关问题的通知》的规定，自2018年1月1日起，本基金的基金管理人运营本基金过程中发生的增值税应税行为，暂适用简易计税方法，按照3%的征收率缴纳增值税。对本基金在2018年1月1日前运营过程中发生的增值税应税行为，未缴纳增值税的，不再缴纳；已缴纳增值税的，已纳税额从本基金的基金管理人以后月份的增值税应纳税额中抵减。\n 7.4.6.3城市维护建设税、教育费附加、地方教育附加\n 根据《中华人民共和国城市维护建设税暂行条例（2011年修订）》、《征收教育费附加的暂行\n 规定（2011年修订）》及相关地方教育附加的征收规定，凡缴纳消费税、增值税、营业税的单位和个人，都应当依照规定缴纳城市维护建设税、教育费附加（除按照相关规定缴纳农村教育事业费附加的单位外）及地方教育费附加。\n 7.4.6.4企业所得税\n 根据财政部、国家税务总局财税[2004]78号文《关于证券投资基金税收政策的通知》的规定，自2004年1月1日起，对证券投资基金（封闭式证券投资基金，开放式证券投资基金）管理人运用基金买卖股票、债券的差价收入，继续免征企业所得税；\n 根据财政部、国家税务总局财税[2005]103号文《关于股权分置试点改革有关税收政策问题的通知》的规定，股权分置改革中非流通股股东通过对价方式向流通股股东支付的股份、现金等收入，暂免征收流通股股东应缴纳的企业所得税；\n 根据财政部、国家税务总局财税[2008]1号文《关于企业所得税若干优惠政策的通知》的规定，对证券投资基金从证券市场中取得的收入，包括买卖股票、债券的差价收入，股权的股息、红利收入，债券的利息收入及其他收入，暂不征收企业所得税。\n 7.4.6.5个人所得税根据财政部、国家税务总局财税[2005]103号文《关于股权分置试点改革有关税收政策问题的通知》的规定，股权分置改革中非流通股股东通过对价方式向流通股股东支付的股份、现金等收入，暂免征收流通股股东应缴纳的个人所得税；\n 第31页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告根据财政部、国家税务总局财税[2008]132号文《财政部、国家税务总局关于储蓄存款利息所得有关个人所得税政策的通知》的规定，自2008年10月9日起，对储蓄存款利息所得暂免征收个人所得税；\n 根据财政部、国家税务总局、中国证监会财税[2012]85号文《关于实施上市公司股息红利差别化个人所得税政策有关问题的通知》的规定，自2013年1月1日起，证券投资基金从公开发行和转让市场取得的上市公司股票，持股期限在1个月以内（含1个月）的，其股息红利所得全额计入应纳税所得额；持股期限在1个月以上至1年（含1年）的，暂减按50%计入应纳税所得额；持股期限超过1年的，暂减按25%计入应纳税所得额。上述所得统一适用20%的税率计征个人所得税；\n 根据财政部、国家税务总局、中国证监会财税[2015]101号文《关于上市公司股息红利差别化个人所得税政策有关问题的通知》的规定，自2015年9月8日起，证券投资基金从公开发行和转让市场取得的上市公司股票，持股期限超过1年的，股息红利所得暂免征收个人所得税。\n 7.4.7重要财务报表项目的说明\n 7.4.7.1银行存款\n 单位：人民币元本期末上年度末项目\n 2020年12月31日2019年12月31日\n 活期存款37153342.4417266791.39\n 定期存款--\n 其中：存款期限1个月以内--\n 存款期限1-3个月--\n 存款期限3个月以上--\n 其他存款--\n 合计37153342.4417266791.39\n 7.4.7.2交易性金融资产\n 单位：人民币元本期末项目2020年12月31日成本公允价值公允价值变动\n 第32页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 股票546622734.23577659593.8831036859.65\n 贵金属投资-金交所黄\n ---金合约\n 交易所市场---\n 债券银行间市场---\n 合计---\n 资产支持证券---\n 基金---\n 其他---\n 合计546622734.23577659593.8831036859.65上年度末项目2019年12月31日成本公允价值公允价值变动\n 股票205918076.60211287284.585369207.98\n 贵金属投资-金交所黄\n ---金合约\n 交易所市场---\n 债券银行间市场---\n 合计---\n 资产支持证券---\n 基金---\n 其他---\n 合计205918076.60211287284.585369207.98\n 7.4.7.3衍生金融资产/负债\n 本基金本报告期末及上年度末均无衍生金融资产/负债余额。\n 7.4.7.4买入返售金融资产\n 7.4.7.4.1各项买入返售金融资产期末余额\n 本基金本报告期末及上年度末均未持有买入返售金融资产。\n 第33页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 7.4.7.4.2期末买断式逆回购交易中取得的债券\n 本基金本报告期末及上年度末均未持有买断式逆回购交易中取得的债券。\n 7.4.7.5应收利息\n 单位：人民币元本期末上年度末项目\n 2020年12月31日2019年12月31日\n 应收活期存款利息3875.283693.88\n 应收定期存款利息--\n 应收其他存款利息--\n 应收结算备付金利息88.4037.90\n 应收债券利息--\n 应收资产支持证券利息--\n 应收买入返售证券利息--\n 应收申购款利息46.3939.97\n 应收黄金合约拆借孳息--\n 其他59.408.70\n 合计4069.473780.45\n 7.4.7.6其他资产\n 本基金本报告期末及上年度末均无其他资产余额。\n 7.4.7.7应付交易费用\n 单位：人民币元本期末上年度末项目\n 2020年12月31日2019年12月31日\n 交易所市场应付交易费用773407.28152425.18\n 银行间市场应付交易费用--\n 合计773407.28152425.18\n 7.4.7.8其他负债\n 第34页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 单位：人民币元本期末上年度末项目\n 2020年12月31日2019年12月31日\n 应付券商交易单元保证金--\n 应付赎回费26701.7542014.68\n 预提信息披露费120000.00120000.00\n 预提审计费60000.0060000.00\n 指数使用费50000.0050000.00\n 应退替代款--\n 可退替代款--\n 预提账户维护费--\n 预提费用--\n 预提上市年费--\n 合计256701.75272014.68\n 7.4.7.9实收基金\n 金额单位：人民币元本期项目2020年1月1日至2020年12月31日\n 基金份额（份）账面金额\n 上年度末223939415.68340447022.11\n 本期申购1200898163.531825681573.73\n 本期赎回（以“-”号填列）-972591837.03-1478594474.34\n -基金拆分/份额折算前452245742.18687534121.50\n 基金拆分/份额折算调整10755667.11—\n 本期申购77777160.19115495255.44\n 本期赎回（以“-”号填列）-37421760.04-55569497.53\n 本期末503356809.44747459879.41\n 注：(1) 本基金的基金份额包括华安中证证券份额、华安中证证券 A 份额和华安中证证券 B 份额。\n (2)申购含配对转换入份额，赎回含配对转换出份额。\n (3)根据《华安中证全指证券公司指数分级证券投资基金基金合同》规定，每年的基金份额定期\n 折算基准日 12 月 15 日（遇节假日顺延），本基金将对华安中证证券 A 份额和华安中证证券份额进\n 第35页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 行定期份额折算。当华安中证证券份额的基金份额净值大于或等于 1.5000 元时；当华安中证证券 B份额的基金份额参考净值小于或等于 0.2500 元时，本基金将分别对华安中证证券 A 份额、华安中证证券 B 份额和华安中证证券份额进行不定期份额折算。\n (4) 本基金于 2020 年 12 月 15 日进行定期份额折算，对华安中证证券 A 份额和华安中证证券份额按照基金合同规定的定期份额折算方法进行份额调整。根据基金管理人于2020年12月17日发布的《关于华安中证全指证券公司指数分级证券投资基金定期份额折算结果及恢复交易的公告》，折算前华安中证证券份额为 436372944.18 份，华安中证证券 A 份额为 7936399.00 份；折算后华安中证证券份额为 447128611.29 份，华安中证证券 A 份额为 7936399.00 份，华安中证证券 A 份额经份额折算后产生的新增份额为华安中证证券份额。\n 7.4.7.10未分配利润\n 单位：人民币元项目已实现部分未实现部分未分配利润合计\n 上年度末-170446121.1554062300.29-116383820.86\n 本期利润49503227.1525667651.6775170878.82本期基金份额交易产生的\n -192005847.2295846781.26-96159065.96变动数\n 其中：基金申购款-919599451.67445199948.97-474399502.70\n 基金赎回款727593604.45-349353167.71378240436.74\n 本期已分配利润---\n 本期末-312948741.22175576733.22-137372008.00\n 7.4.7.11存款利息收入\n 单位：人民币元本期上年度可比期间项目2020年1月1日至2020年12月2019年1月1日至2019年12\n 31日月31日\n 活期存款利息收入147720.19111724.00\n 定期存款利息收入--\n 其他存款利息收入--\n 第36页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 结算备付金利息收入6135.073245.21\n 其他5040.321218.47\n 合计158895.58116187.68\n 7.4.7.12股票投资收益\n 单位：人民币元本期上年度可比期间项目2020年1月1日至2020年122019年1月1日至2019年12月31日月31日\n 卖出股票成交总额386639215.54175079702.46\n 减：卖出股票成本总额338632610.27176906614.52\n 买卖股票差价收入48006605.27-1826912.06\n 7.4.7.13债券投资收益\n 7.4.7.13.1债券投资收益项目构成\n 单位：人民币元本期上年度可比期间项目2020年1月1日至2020年122019年1月1日至2019年12月31日月31日债券投资收益——买卖债券（债转股及债券\n 566023.2328321.19到期兑付）差价收入\n 债券投资收益——赎回差价收入--\n 债券投资收益——申购差价收入--\n 合计566023.2328321.19\n 7.4.7.13.2债券投资收益——买卖债券差价收入\n 单位：人民币元本期上年度可比期间项目2020年1月1日至2020年12月2019年1月1日至2019年12月\n 31日31日\n 卖出债券（债转股及债券到期兑付）成交总\n 3494892.30710941.00\n 额\n 减：卖出债券（债转股及债券到期兑付）成\n 2928500.00682000.00\n 本总额\n 第37页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 减：应收利息总额369.07619.81\n 买卖债券差价收入566023.2328321.19\n 7.4.7.14资产支持证券投资收益\n 本基金本报告期及上年度可比期间均无资产支持证券投资收益。\n 7.4.7.15贵金属投资收益\n 7.4.7.15.1贵金属投资收益项目构成\n 本基金本报告期及上年度可比期间均无贵金属投资收益。\n 7.4.7.15.2贵金属投资收益——买卖贵金属差价收入\n 本基金本报告期及上年度可比期间均无贵金属投资收益——买卖贵金属差价收入。\n 7.4.7.15.3贵金属投资收益——赎回差价收入\n 本基金本报告期及上年度可比期间均无贵金属投资收益——赎回差价收入。\n 7.4.7.15.4贵金属投资收益——申购差价收入\n 本基金本报告期及上年度可比期间均无贵金属投资收益——申购差价收入。\n 7.4.7.16衍生工具收益\n 7.4.7.16.1衍生工具收益——买卖权证差价收入\n 本基金本报告期及上年度可比期间均无衍生工具收益——买卖权证差价收入。\n 7.4.7.16.2衍生工具收益——其他投资收益\n 本基金本报告期及上年度可比期间均无衍生工具收益——其他投资收益。\n 7.4.7.17股利收益\n 单位：人民币元本期上年度可比期间项目\n 2020年1月1日至2020年12月31日2019年1月1日至2019年12月31日\n 股票投资产生的股利收益4660945.222357928.36\n 基金投资产生的股利收益--\n 合计4660945.222357928.36\n 第38页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 7.4.7.18公允价值变动收益\n 单位：人民币元本期上年度可比期间项目名称\n 2020年1月1日至2020年12月31日2019年1月1日至2019年12月31日\n 1.交易性金融资产25667651.6748146803.87\n ——股票投资25667651.6748139631.87\n ——债券投资-7172.00\n ——资产支持证券投资--\n ——基金投资--\n ——贵金属投资--\n ——其他--\n 2.衍生工具--\n ——权证投资--\n 3.其他--\n 减：应税金融商品公允价值变\n 动产生的预估增值税--\n 合计25667651.6748146803.87\n 7.4.7.19其他收入\n 单位：人民币元本期上年度可比期间项目\n 2020年1月1日至2020年12月31日2019年1月1日至2019年12月31日\n 基金赎回费收入2596073.591052385.60\n 其他86.49-\n 合计2596160.081052385.60\n 7.4.7.20交易费用\n 单位：人民币元本期上年度可比期间项目\n 2020年1月1日至2020年12月31日2019年1月1日至2019年12月31日\n 交易所市场交易费用1504087.46615154.67\n 第39页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 银行间市场交易费用--\n 交易基金产生的费用--\n 其中：申购费--\n 赎回费--\n 合计1504087.46615154.67\n 7.4.7.21其他费用\n 单位：人民币元本期上年度可比期间项目2020年1月1日至2020年12月2019年1月1日至2019年12月31日\n 31日\n 审计费用60000.0060000.00\n 信息披露费120000.00120000.00\n 律师费35000.00-\n 指数使用费200000.00200000.00\n 公证费15000.00-\n 银行汇划费6595.343861.96\n 上市年费60000.0060000.00\n 合计496595.34443861.96\n 7.4.8或有事项、资产负债表日后事项的说明\n 7.4.8.1或有事项\n 截至资产负债表日，本基金无需要说明的重大或有事项。\n 7.4.8.2资产负债表日后事项\n 1、本基金于资产负债表日之后、年度报告批准报出日之前批准、公告或实施的分级份额终止运\n 作的情况如下：\n 根据《中华人民共和国证券投资基金法》、《公开募集证券投资基金运作管理办法》、《关于规范金融机构资产管理业务的指导意见》和《华安中证全指证券公司指数分级证券投资基金基金合同》等有关规定，本基金的基金管理人制定了本基金的分级份额终止运作实施方案，并于资产负债表日后实施完成。主要内容如下：\n 第40页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n (1) 华安中证证券 A 份额和华安中证证券 B 份额的最后交易日为 2020 年 12 月 31 日，终止上市日为2021年1月4日；\n (2) 本基金以 2020 年 12 月 31 日为折算基准日，将华安中证证券 A 份额和华安中证证券 B 份额折算为华安中证证券份额。根据基金管理人于2021年1月5日发布的《关于华安中证全指证券公司指数分级证券投资基金之华安中证证券 A 份额和华安中证证券 B 份额基金份额折算结果的公告》，折算前华安中证证券份额为 488271125.44 份，华安中证证券 A 份额为 7542842.00 份，华安中证证券 B 份额为 7542842.00 份；折算后华安中证证券份额为 503356808.44 份，华安中证证券 A 份额为 0.00 份，华安中证证券 B 份额为 0.00 份；\n (3) 华安中证证券 A 份额和华安中证证券 B 份额终止运作后，《华安中证全指证券公司指数分级证券投资基金基金合同》将修订为《华安中证全指证券公司指数型证券投资基金基金合同》。《华安中证全指证券公司指数型证券投资基金基金合同》于2021年1月1日生效，《华安中证全指证券公司指数分级证券投资基金基金合同》同时失效。\n 2、根据华安基金管理有限公司于2021年3月9日发布的公告，经华安基金管理有限公司股东\n 会第六十六次会议决议及中国证券监督管理委员会证监许可[2021]669号文核准，华安基金管理有限\n 公司股东上海锦江国际投资管理有限公司将其持有的华安基金管理有限公司8%的股权转让给国泰君安证券股份有限公司。\n 截至财务报表批准日，除上述事项外，本基金无其他需要披露的资产负债表日后事项。\n 7.4.9关联方关系\n 关联方名称与本基金的关系\n 华安基金管理有限公司(“华安基金公司”)基金管理人、基金销售机构\n 中国建设银行股份有限公司(“中国建设银行”)基金托管人、基金销售机构国泰君安证券股份有限公司基金管理人的股东上海上国投资产管理有限公司基金管理人的股东\n 上海工业投资(集团)有限公司基金管理人的股东上海锦江国际投资管理有限公司基金管理人的股东国泰君安投资管理股份有限公司基金管理人的股东\n 华安资产管理（香港）有限公司基金管理人的全资子公司\n 华安未来资产管理（上海）有限公司基金管理人的全资子公司\n 注：下述关联交易均在正常业务范围内按一般商业条款订立。\n 7.4.10本报告期及上年度可比期间的关联方交易\n 7.4.10.1通过关联方交易单元进行的交易\n 第41页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 7.4.10.1.1股票交易\n 金额单位：人民币元本期上年度可比期间\n 2020年1月1日至2020年12月31日2019年1月1日至2019年12月31日\n 关联方名称占当期股占当期股成交金额票成交总成交金额票成交总额的比例额的比例国泰君安证券股份\n 30466439.772.91%30845095.077.54%\n 有限公司\n 7.4.10.1.2权证交易\n 本基金本报告期及上年度可比期间均未通过关联方交易单元进行权证交易。\n 7.4.10.1.3债券交易\n 金额单位：人民币元本期上年度可比期间\n 2020年1月1日至2020年12月31日2019年1月1日至2019年12月31日\n 关联方名称占当期债占当期债成交金额券成交总成交金额券成交总额的比例额的比例国泰君安证券股份有\n 1017500.0015.84%--\n 限公司\n 7.4.10.1.4债券回购交易\n 本基金本报告期及上年度可比期间均未通过关联方交易单元进行回购交易。\n 7.4.10.1.5应支付关联方的佣金\n 金额单位：人民币元本期\n 2020年1月1日至2020年12月31日\n 关联方名称当期占当期佣金占期末应付佣期末应付佣金余额佣金总量的比例金总额的比例\n 第42页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告国泰君安证券股份有\n 26085.012.70%14806.691.91%\n 限公司上年度可比期间\n 2019年1月1日至2019年12月31日\n 关联方名称当期占当期佣金占期末应付佣期末应付佣金余额佣金总量的比例金总额的比例国泰君安证券股份有\n 28109.317.42%--\n 限公司\n 注：上述佣金按市场佣金率计算，以扣除由中国证券登记结算有限责任公司收取证管费、经手费和由券商承担的证券结算风险基金后的净额列示。该类佣金协议的服务范围还包括佣金收取方为本基金提供的证券投资研究成果和市场信息服务。\n 7.4.10.2关联方报酬\n 7.4.10.2.1基金管理费\n 单位：人民币元本期上年度可比期间项目2020年1月1日至2020年122019年1月1日至2019年12月31日月31日\n 当期发生的基金应支付的管理费3676294.532149804.61\n 其中：支付销售机构的客户维护费1191218.14708293.36\n 注：基金管理费按前一日的基金资产净值的1.00%的年费率计提。计算方法如下：\n H=E×基金管理费年费率/当年天数\n H 为每日应计提的基金管理费\n E 为前一日的基金资产净值\n 基金管理费每日计提，逐日累计至每月月末，按月支付。\n 7.4.10.2.2基金托管费\n 单位：人民币元本期上年度可比期间项目2020年1月1日至2020年122019年1月1日至2019年12月31日月31日\n 第43页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 当期发生的基金应支付的托管费808784.79472956.98\n 注：基金托管费按前一日的基金资产净值的0.22%的年费率计提。计算方法如下：\n H=E×基金托管费年费率/当年天数\n H 为每日应计提的基金托管费\n E 为前一日的基金资产净值\n 基金托管费每日计提，逐日累计至每月月末，按月支付。\n 7.4.10.3与关联方进行银行间同业市场的债券(含回购)交易\n 本基金本报告期及上年度可比期间均未与关联方进行银行间同业市场的债券(含回购)交易。\n 7.4.10.4各关联方投资本基金的情况\n 7.4.10.4.1报告期内基金管理人运用固有资金投资本基金的情况\n 份额单位：份本期上年度可比期间项目2020年1月1日至2020年12月312019年1月1日至2019年12月31日日\n 期初持有的基金份额--\n 期间申购/买入总份额22329873.00-\n 期间因拆分变动份额531067.00-\n 减：期间赎回/卖出总份额--\n 期末持有的基金份额22860940.00-期末持有的基金份额占基金总份\n 4.54%-\n 额比例\n 7.4.10.4.2报告期末除基金管理人之外的其他关联方投资本基金的情况\n 本基金除基金管理人之外的其他关联方于本报告期末及上年度末均未投资本基金。\n 7.4.10.5由关联方保管的银行存款余额及当期产生的利息收入\n 单位：人民币元本期上年度可比期间关联方名称\n 2020年1月1日至2020年12月31日2019年1月1日至2019年12月31日\n 第44页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告期末余额当期利息收入期末余额当期利息收入\n 中国建设银行37153342.44147720.1917266791.39111724.00\n 7.4.10.6本基金在承销期内参与关联方承销证券的情况\n 金额单位：人民币元本期\n 2020年1月1日至2020年12月31日\n 基金在承销期内买入关联方名称证券代码证券名称发行方式数量（单位：总金额股/张）国泰君安证\n 券股份有限688981中芯国际网下申购68556.001891960.50公司国泰君安证\n 券股份有限688256寒武纪网下申购2413.00156149.94公司国泰君安证\n 券股份有限688286敏芯股份网下申购1681.00105875.01公司国泰君安证\n 券股份有限605066天正电气网下申购1278.0012805.56公司国泰君安证\n 券股份有限688339亿华通网下申购2761.00212688.80公司国泰君安证\n 券股份有限688229博睿数据网下申购1643.00108682.97公司国泰君安证\n 券股份有限605388均瑶健康网下申购1230.0016518.90公司国泰君安证\n 券股份有限688017绿的谐波网下申购4138.00145803.67公司国泰君安证\n 券股份有限688095福昕软件网下申购834.00199928.69公司国泰君安证\n 券股份有限603112华翔股份网下申购920.007194.40公司\n 第45页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告国泰君安证\n 券股份有限003003天元股份网下申购756.007930.44公司国泰君安证老股东配\n 券股份有限110073国投转债9130.00913000.00售公司上年度可比期间\n 2019年1月1日至2019年12月31日\n 基金在承销期内买入关联方名称证券代码证券名称发行方式数量（单位：总金额股/张）\n ------\n 7.4.10.7其他关联交易事项的说明\n 7.4.10.7.1其他关联交易事项的说明\n 本基金本报告期末投资于基金管理人股东国泰君安证券股份有限公司发行的股票——国泰君安（代码601211）1445466股，期末公允价值为人民币25339018.98元，占本基金期末基金资产净值比例为4.15%。\n 本基金上年度末投资于基金管理人股东国泰君安证券股份有限公司发行的股票——国泰君安（代码601211）716666股，期末公允价值为人民币13251154.34元，占本基金期末基金资产净值比例为5.91%。\n 7.4.11利润分配情况\n 本基金本报告期未进行利润分配。\n 7.4.12期末（2020年12月31日）本基金持有的流通受限证券\n 7.4.12.1因认购新发/增发证券而于期末持有的流通受限证券\n 金额单位：人民币元\n 7.4.12.1.1受限证券类别：股票\n 流通期末期末\n 证券证券成功可流受认购期末估数量(单成本估值备注\n 代码名称认购日通日限类价格值单价位：股)总额总额型\n 30086锋尚2020-02021-0新股138.02126.67159.002194520140-\n 第46页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 0文化8-062-24限售.18.53\n 30086美畅2020-02021-0新股2218625907\n 43.7651.10507.00-\n 1股份8-062-24限售.32.70\n 30086南大2020-02021-0新股1190315081\n 71.7190.85166.00-\n 4环境8-132-25限售.86.10\n 30086杰美2020-02021-0新股1778318653\n 41.2643.28431.00-\n 8特8-122-24限售.06.68\n 30086康泰2020-02021-0新股8757.95578\n 10.16110.88862.00-\n 9医学8-122-24限售92.56\n 30087回盛2020-02021-0新股2077023916\n 33.6138.70618.00-\n 1生物8-132-24限售.98.60\n 30087天阳2020-02021-0新股1702930196\n 21.3437.84798.00-\n 2科技8-142-25限售.32.32\n 30087海晨2020-02021-0新股1486819998\n 30.7241.32484.00-\n 3股份8-143-02限售.48.88\n 30087金春2020-02021-0新股1737728933\n 30.5450.85569.00-\n 7股份8-173-03限售.26.65\n 30087维康2020-02021-0新股1657719536\n 41.3448.72401.00-\n 8药业8-173-03限售.34.72\n 30087大叶2020-02021-0新股4930.12637\n 10.5827.12466.00-\n 9股份8-253-01限售28.92\n 30088盛德2020-02021-0新股4123.9402.\n 14.1732.31291.00-\n 1鑫泰8-253-05限售4721\n 30088狄耐2020-12021-0新股1241016721\n 24.8733.51499.00-\n 4克1-055-12限售.13.49\n 30088谱尼2020-02021-0新股9338.15649\n 44.4774.52210.00-\n 7测试9-093-16限售70.20\n 30088爱克2020-02021-0新股1339714427\n 27.9730.12479.00-\n 9股份9-093-16限售.63.48\n 30089惠云2020-02021-0新股1143.4160.18745\n 3.6416.40-\n 1钛业9-103-17限售0052.20\n 30089品渥2020-02021-0新股7251.16322\n 26.6660.01272.00-\n 2食品9-113-24限售52.72\n 30089松原2020-02021-0新股3717.9682.\n 13.4735.08276.00-\n 3股份9-173-24限售7208\n 30089铜牛2020-02021-0新股3579.12819\n 12.6545.30283.00-\n 5信息9-173-24限售95.90\n 30089爱美2020-02021-0新股1856894727\n 118.27603.36157.00-\n 6客9-213-29限售.39.52\n 30089熊猫2020-12021-0新股5346.16382\n 10.7833.03496.00-\n 8乳品0-094-16限售88.88\n 30090广联2020-12021-0新股1570729147\n 17.8733.16879.00-\n 0航空0-194-29限售.73.64\n 30090国安2020-12021-0新股15.3824.26551.008474.13367-\n 第47页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 2达0-224-29限售38.26\n 30090康平2020-12021-0新股6763.12700\n 14.3026.85473.00-\n 7科技1-115-18限售90.05\n 30090仲景2020-12021-0新股1132517225\n 39.7460.44285.00-\n 8食品1-135-24限售.90.40\n 30091瑞丰2020-12021-0新股1434322690\n 30.2647.87474.00-\n 0新材1-205-27限售.24.38\n 30091兆龙2020-12021-0新股8322.15082\n 13.2123.94630.00-\n 3互连1-276-07限售30.20\n 60199中金2020-12021-0新股24951718081714\n 28.7868.71-\n 5公司0-225-06限售.009.78383.21\n 68812蓝特2020-02021-0新股5322.8201217881\n 15.4133.60-\n 7光学9-143-22限售00.029.20\n 68815先惠2020-02021-0新股2403.9316416162\n 38.7767.26-\n 5技术8-032-18限售00.315.78\n 68816埃夫2020-02021-0新股201091276926342\n 6.3513.10-\n 5特7-071-15限售.002.157.90\n 68817阿拉2020-12021-0新股3944.7663125336\n 19.4364.24-\n 9丁0-164-26限售00.922.56\n 68821瑞晟2020-02021-0新股1229.4268356681\n 34.7346.12-\n 5智能8-203-01限售00.17.48\n 68827天智2020-02021-0新股8810.1060733063\n 12.0437.53-\n 7航6-241-07限售002.409.30\n 68828圣湘2020-02021-0新股4253.2146946714\n 50.48109.84-\n 9生物8-203-01限售001.449.52\n 68833亿华2020-02021-0新股2761.2116371219\n 76.65257.95-\n 9通7-312-10限售000.659.95\n 68838泛亚2020-12021-0新股3217.5237215251\n 16.2847.41-\n 6微透0-094-16限售00.767.97\n 68852芯原2020-02021-0新股7200.2774156152\n 38.5377.99-\n 1股份8-112-18限售006.008.00\n 68857浙海2020-02021-0新股1503.4979463141\n 33.1342.01-\n 7德曼9-083-16限售00.39.03\n 注：以上“可流通日”是根据上市公司公告估算的流通日期，最终日期以上市公司公告为准。\n 7.4.12.2期末持有的暂时停牌等流通受限股票\n 本基金本报告期末未持有暂时停牌等流通受限股票。\n 7.4.12.3期末债券正回购交易中作为抵押的债券\n 7.4.12.3.1银行间市场债券正回购\n 截至本报告期末，本基金无因从事银行间市场债券正回购交易形成的卖出回购金融资产款余额。\n 第48页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 7.4.12.3.2交易所市场债券正回购\n 截至本报告期末，本基金无因从事交易所市场债券正回购交易形成的卖出回购金融资产款余额。\n 7.4.13金融工具风险及管理\n 本基金为股票型基金，属于较高风险、较高预期收益的基金品种，其风险和预期收益高于混合型基金、债券型基金和货币市场基金。从本基金所分离的两类基金份额来看，华安中证证券 A 份额具有低风险、预期收益相对稳定的特征；华安中证证券 B 份额具有高风险、高预期收益的特征。本基金在日常经营活动中面临的与这些金融工具相关的风险主要包括信用风险、流动性风险及市场风险。本基金通过被动的指数化投资管理，紧密跟踪标的指数，追求跟踪偏离度和跟踪误差最小化。\n 7.4.13.1风险管理政策和组织架构\n 本基金的基金管理人奉行全面风险管理体系的建设，建立了以风险控制委员会为核心的、由督察长、风险控制委员会、监察稽核部、风险管理部等相关业务部门构成的风险管理架构体系。本基金的基金管理人在董事会下设立风险控制委员会，负责制定风险管理的宏观政策，审议通过风险控制的总体措施等；在管理层层面设立合规与风险管理委员会，讨论和制定公司日常经营过程中风险防范和控制措施；在业务操作层面风险管理职责主要由监察稽核部和风险管理部负责，协调并与各部门合作完成运作风险管理和投资风险管理、绩效评估等。监察稽核部和风险管理部对公司执行总裁负责，并由督察长分管。\n 本基金的基金管理人对于金融工具的风险管理方法主要是通过定性分析和定量分析的方法去估\n 测各种风险产生的可能损失。从定性分析的角度出发，判断风险损失的严重程度和出现同类风险损失的频度。从定量分析的角度出发，根据本基金的投资目标，结合基金资产所运用金融工具，通过特定的风险量化指标、模型，形成常规的量化报告，确定风险损失的限度和相应置信程度，及时可靠地对各种风险进行监督、检查和评估，并通过相应决策，将风险控制在可承受的范围内。\n 7.4.13.2信用风险\n 信用风险是指基金在交易过程中因交易对手未履行合约责任，或者基金所投资证券之发行人出现违约、拒绝支付到期本息等情况，导致基金资产损失和收益变化的风险。\n 本基金的基金管理人在交易前对交易对手的资信状况进行了充分的评估。本基金的银行存款存放在经中国人民银行和中国证券监督管理委员会所批准的具有基金托管资格和基金代销资格的银行，并根据本基金的基金管理人管理交易对手的经验进行筛选，因而与这些银行存款相关的信用风险不重大。本基金在银行间同业市场进行交易前均对交易对手进行信用评估并对证券交割方式进行限制\n 第49页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告以控制相应的信用风险；在交易所进行的交易均以中国证券登记结算有限责任公司为交易对手完成\n 证券交收和款项清算，违约风险可能性很小。\n 本基金的基金管理人建立了信用风险管理流程，通过对投资品种信用等级评估来控制证券发行人的信用风险，且通过分散化投资以分散信用风险。\n 7.4.13.3流动性风险\n 流动性风险是指基金在履行与金融负债有关的义务时遇到资金短缺的风险。本基金的流动性风险一方面来自于基金份额持有人可随时要求赎回其持有的基金份额，另一方面来自于投资品种所处的交易市场不活跃而带来的变现困难或因投资集中而无法在市场出现剧烈波动的情况下以合理的价格变现。\n 针对兑付赎回资金的流动性风险，本基金的基金管理人每日对本基金的申购赎回情况进行严密监控并预测流动性需求，保持基金投资组合中的可用现金头寸与之相匹配。本基金的基金管理人在基金合同中设计了巨额赎回条款，约定在非常情况下赎回申请的处理方式，控制因开放申购赎回模式带来的流动性风险，有效保障基金份额持有人利益。\n 7.4.13.3.1报告期内本基金组合资产的流动性风险分析\n 本基金的基金管理人在基金运作过程中严格按照《公开募集证券投资基金运作管理办法》及《公开募集开放式证券投资基金流动性风险管理规定》(自2017年10月1日起施行)等法规的要求对本基\n 金组合资产的流动性风险进行管理，通过独立的风险管理部门对本基金的组合持仓集中度指标、流通受限制的投资品种比例以及组合在短时间内变现能力的综合指标等流动性指标进行持续的监测和分析。\n 本基金投资于一家公司发行的证券市值不超过基金资产净值的10%，且本基金与由本基金的基金管理人管理的其他基金共同持有一家公司发行的证券不得超过该证券的10%。本基金与由本基金的基金管理人管理的其他开放式基金共同持有一家上市公司发行的可流通股票不得超过该上市公司\n 可流通股票的15%，本基金与由本基金的基金管理人管理的全部投资组合持有一家上市公司发行的可流通股票，不得超过该上市公司可流通股票的30%。\n 本基金所持部分证券在证券交易所上市，其余亦可在银行间同业市场交易，部分基金资产流通暂时受限制不能自由转让的情况参见附注7.4.12。此外，本基金可通过卖出回购金融资产方式借入短期资金应对流动性需求，其上限一般不超过基金持有的债券投资的公允价值。本基金主动投资于流动性受限资产的市值合计不得超过基金资产净值的15%。\n 第50页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告本基金的基金管理人每日对基金组合资产中7个工作日可变现资产的可变现价值进行审慎评估与测算，确保每日确认的净赎回申请不得超过7个工作日可变现资产的可变现价值。\n 同时，本基金的基金管理人通过合理分散逆回购交易的到期日与交易对手的集中度；按照穿透原则对交易对手的财务状况、偿付能力及杠杆水平等进行必要的尽职调查与严格的准入管理，以及对不同的交易对手实施交易额度管理并进行动态调整等措施严格管理本基金从事逆回购交易的流动\n 性风险和交易对手风险。此外，本基金的基金管理人建立了逆回购交易质押品管理制度：根据质押品的资质确定质押率水平；持续监测质押品的风险状况与价值变动以确保质押品按公允价值计算足额；并在与私募类证券资管产品及中国证监会认定的其他主体为交易对手开展逆回购交易时，可接受质押品的资质要求与基金合同约定的投资范围保持一致。\n 综合上述各项流动性指标的监测结果及流动性风险管理措施的实施，本基金在本报告期内流动性情况良好。\n 7.4.13.4市场风险\n 市场风险是指基金所持金融工具的公允价值或未来现金流量因所处市场各类价格因素的变动而\n 发生波动的风险，包括利率风险、外汇风险和其他价格风险。\n 7.4.13.4.1利率风险\n 利率风险是指金融工具的公允价值或现金流量受市场利率变动而发生波动的风险。利率敏感性金融工具均面临由于市场利率上升而导致公允价值下降的风险，其中浮动利率类金融工具还面临每个付息期间结束根据市场利率重新定价时对于未来现金流影响的风险。\n 本基金的基金管理人定期对本基金面临的利率敏感性缺口进行监控，并通过调整投资组合的久期等方法对上述利率风险进行管理。\n 本基金的生息资产主要为银行存款、结算备付金和存出保证金等。\n 7.4.13.4.1.1利率风险敞口\n 单位：人民币元本期末\n 2020年12月311年以内1-5年5年以上不计息合计\n 日资产\n 银行存款37153342.44---37153342.44\n 第51页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 结算备付金196440.64---196440.64\n 存出保证金131848.75---131848.75\n 577659593.8\n 交易性金融资产---577659593.88\n 8\n 应收证券清算款-----\n 应收利息---4069.474069.47\n 应收申购款147678.82--3817448.323965127.14\n 619110422.3\n 资产总计37629310.65--581481111.67\n 2\n 负债\n 短期借款-----\n 交易性金融负债-----\n 衍生金融负债-----卖出回购金融资\n -----产款\n 应付证券清算款-----\n 应付赎回款---7436076.637436076.63\n 应付管理人报酬---456037.09456037.09\n 应付托管费---100328.16100328.16\n 应付销售服务费-----\n 应付交易费用---773407.28773407.28\n 应交税费-----\n 应付利息-----\n 应付利润-----\n 递延所得税负债-----\n 其他负债---256701.75256701.75\n 9022550.9\n 负债总计---9022550.91\n 610087871.4\n 利率敏感度缺口37629310.65--572458560.76\n 1\n 上年度末\n 2019年12月311年以内1-5年5年以上不计息合计\n 日资产\n 银行存款17266791.39---17266791.39\n 结算备付金84410.67---84410.67\n 存出保证金19283.51---19283.51\n 交易性金融资产---211287284.58211287284.5\n 第52页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 8\n 应收证券清算款---6145390.116145390.11\n 应收利息---3780.453780.45\n 应收申购款---1447763.861447763.86\n 236254704.5\n 资产总计17370485.57--218884219.00\n 7\n 负债\n 短期借款-----\n 应付赎回款---11522537.3311522537.33\n 应付管理人报酬---200431.27200431.27\n 应付托管费---44094.8644094.86\n 应付交易费用---152425.18152425.18\n 其他负债---272014.68272014.68\n 负债总计---12191503.3212191503.32\n 224063201.2\n 利率敏感度缺口17370485.57--206692715.68\n 5\n 注：表中所示为本基金资产及负债的公允价值，并按照合约规定的重新定价日或到期日孰早者进行了分类。\n 7.4.13.4.1.2利率风险的敏感性分析\n 本基金于2020年12月31日及2019年12月31日未持有债券资产，且持有的银行存款均为活期存款，因此市场利率的变动对于本基金资产净值无重大影响。\n 7.4.13.4.2外汇风险\n 外汇风险是指金融工具的公允价值或未来现金流量因外汇汇率变动而发生波动的风险。本基金的所有资产及负债以人民币计价，因此无重大外汇风险。\n 7.4.13.4.3其他价格风险\n 其他价格风险是指基金所持金融工具的公允价值或未来现金流量因除市场利率和外汇汇率以外的市场价格因素变动而发生波动的风险。本基金主要投资于证券交易所上市或银行间同业市场交易的股票和债券，所面临的其他价格风险来源于单个证券发行主体自身经营情况或特殊事项的影响，也可能来源于证券市场整体波动的影响。\n 本基金的基金管理人定期结合宏观及微观环境的变化，对投资策略、资产配置、投资组合进行修正，来主动应对可能发生的市场价格风险。\n 第53页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 本基金通过投资组合的分散化降低其他价格风险。基金的投资组合比例为：投资于股票资产的比例不低于基金资产的90%，投资于中证全指证券公司指数成分股及其备选成分股的比例不低于非现金基金资产的80%，每个交易日日终在扣除股指期货保证金以后，本基金保留的现金或到期日在一年以内的政府债券投资比例不低于基金资产净值的5％，其中现金不包括结算备付金、存出保证金、应收申购款等。权证、股指期货及其他金融工具的投资比例依照法律法规或监管机构的规定执行。\n 此外，本基金的基金管理人每日对本基金所持有的证券价格实施监控，定期运用多种定量方法对基金进行风险度量，包括 VaR(Value at Risk)指标等来测试本基金面临的潜在价格风险，及时可靠地对风险进行跟踪和控制。\n 7.4.13.4.3.1其他价格风险敞口\n 金额单位：人民币元本期末上年度末\n 2020年12月31日2019年12月31日\n 占基金占基金项目资产净资产净公允价值公允价值值比例值比例\n （%）（%）\n 交易性金融资产－股票投资577659593.8894.68211287284.5894.30\n 交易性金融资产—基金投资----\n 交易性金融资产－贵金属投资----\n 衍生金融资产－权证投资----\n 合计577659593.8894.68211287284.5894.30\n 7.4.13.4.3.2其他价格风险的敏感性分析\n 基金的市场价格风险决定于业绩比较基准的变动假设除上述基准以外的其他市场变量保持不变对资产负债表日基金资产净值的\n 影响金额（单位：人民币元）相关风险变量的变动分析本期末上年度末\n 2020年12月31日2019年12月31日\n 业绩比较基准增加5%增加约29487712.98增加约10964685.71\n 第54页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 业绩比较基准减少5%减少约29487712.98减少约10964685.71\n 7.4.14有助于理解和分析会计报表需要说明的其他事项\n 7.4.14.1公允价值\n 7.4.14.1.1不以公允价值计量的金融工具\n 不以公允价值计量的金融资产和负债主要包括贷款和应收款项以及其他金融负债，其因剩余期限不长，公允价值与账面价值相若。\n 7.4.14.1.2以公允价值计量的金融工具\n 7.4.14.1.2.1各层次金融工具公允价值\n 于2020年12月31日，本基金持有的以公允价值计量且其变动计入当期损益的金融资产中属于\n 第一层次的余额为人民币572098442.71元，属于第二层次的余额为人民币0.00元，属于第三层次的余额为人民币5561151.17元（于2019年12月31日，本基金持有的以公允价值计量且其变动计入当期损益的金融资产中属于第一层次的余额为人民币210648595.77元，属于第二层次的余额为人民币200724.33元，属于第三层次的余额为人民币437964.48元）。\n 7.4.14.1.2.2公允价值所属层次间的重大变动\n 对于证券交易所上市的股票和可转换债券，若出现重大事项停牌、交易不活跃、或属于非公开发行等情况，本基金分别于停牌日至交易恢复活跃日期间、交易不活跃期间及限售期间不将相关股票和可转换债券的公允价值列入第一层次；并根据估值调整中采用的不可观察输入值对于公允价值\n 的影响程度，确定相关股票和可转换债券公允价值应属第二层次或第三层次。\n 7.4.14.1.2.3第三层次公允价值余额和本期变动金额\n 本基金持有的以公允价值计量且其变动计入当期损益的金融工具第三层次公允价值本期变动情\n 况如下：\n 单位：人民币元交易性金融资产\n 上年度末437964.48\n 转入第三层次-\n 转出第三层次423987.36\n 当期计入损益的利得或损失总额3354287.90\n 第55页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 购买2373213.35\n 出售180327.20\n 本期末5561151.17\n 本期末持有的资产计入损益的当期未实现利得或损失的变动3187937.82\n 7.4.14.2承诺事项\n 截至资产负债表日，本基金无需要说明的承诺事项。\n 7.4.14.3其他事项\n 截至资产负债表日，本基金无需要说明的其他重要事项。\n 7.4.14.4财务报表的批准\n 本财务报表已于2021年3月24日经本基金的基金管理人批准。\n §8投资组合报告\n 8.1期末基金资产组合情况\n 金额单位：人民币元\n 序号项目金额占基金总资产的比例（%）\n 1权益投资577659593.8893.30\n 其中：股票577659593.8893.30\n 2基金投资--\n 3固定收益投资--\n 其中：债券--\n 资产支持证券--\n 4贵金属投资--\n 5金融衍生品投资--\n 6买入返售金融资产--\n 其中：买断式回购的买入返\n --售金融资产银行存款和结算备付金合\n 737349783.086.03\n 计\n 8其他各项资产4101045.360.66\n 第56页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 9合计619110422.32100.00\n 8.2期末按行业分类的股票投资组合\n 8.2.1指数投资期末按行业分类的股票投资组合\n 金额单位：人民币元占基金资产净值代码行业类别公允价值比例（％）\n A 农、林、牧、渔业 - -\n --\n B 采矿业\n C 制造业 - -\n D 电力、热力、燃气及水生产和供应业 - -\n E 建筑业 - -\n F 批发和零售业 - -\n G 交通运输、仓储和邮政业 - -\n H 住宿和餐饮业 - -\n I 信息传输、软件和信息技术服务业 - -\n J 金融业 572098442.71 93.77\n K 房地产业 - -\n L 租赁和商务服务业 - -\n M 科学研究和技术服务业 - -\n N 水利、环境和公共设施管理业 - -\n O 居民服务、修理和其他服务业 - -\n P 教育 - -\n Q 卫生和社会工作 - -\n R 文化、体育和娱乐业 - -\n S 综合 - -\n 合计572098442.7193.77\n 第57页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 8.2.2积极投资期末按行业分类的股票投资组合\n 金额单位：人民币元占基金资产净值代码行业类别公允价值比例（％）\n A 农、林、牧、渔业 - -\n --\n B 采矿业\n C 制造业 2901668.75 0.48\n D 电力、热力、燃气及水生产和供应业 - -\n E 建筑业 - -\n F 批发和零售业 16322.72 0.00\n G 交通运输、仓储和邮政业 19998.88 0.00\n H 住宿和餐饮业 - -\n I 信息传输、软件和信息技术服务业 604544.22 0.10\n J 金融业 1714383.21 0.28\n K 房地产业 - -\n L 租赁和商务服务业 - -\n M 科学研究和技术服务业 269011.76 0.04\n N 水利、环境和公共设施管理业 15081.10 0.00\n O 居民服务、修理和其他服务业 - -\n P 教育 - -\n Q 卫生和社会工作 - -\n R 文化、体育和娱乐业 20140.53 0.00\n S 综合 - -\n 合计5561151.170.91\n 8.3期末按公允价值占基金资产净值比例大小排序的所有股票投资明细\n 8.3.1期末指数投资按公允价值占基金资产净值比例大小排序的所有股票投资明细\n 金额单位：人民币元\n 第58页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告占基金资产净\n 序号股票代码股票名称数量(股)公允价值\n 值比例(％)\n 1600030中信证券273156480307981.6013.16\n 2300059东方财富220880068472800.0011.22\n 3601688华泰证券188697433984401.745.57\n 4600837海通证券247655831848535.885.22\n 5600999招商证券118938427760222.564.55\n 6601211国泰君安144546625339018.984.15\n 7600958东方证券133850615566824.782.55\n 8000776广发证券94848715441368.362.53\n 9000166申万宏源288871115252394.082.50\n 10601377兴业证券171737314906797.642.44\n 11601901方正证券131889013676889.302.24\n 12600109国金证券77545912616717.932.07\n 13002736国信证券92383112601054.842.07\n 14601788光大证券62603311594131.161.90\n 15000783长江证券124097910424223.601.71\n 16601108财通证券80530010187045.001.67\n 17601555东吴证券9952009812672.001.61\n 18002797第一创业9429189382034.101.54\n 19601099太平洋21835508908884.001.46\n 20601878浙商证券5788278856053.101.45\n 21601456国联证券3722007939026.001.30\n 22000728国元证券8390637518004.481.23\n 23600061国投资本5418197493356.771.23\n 24000750国海证券12213867157321.961.17\n 25601066中信建投1667227002324.001.15\n 26601162天风证券10682806516508.001.07\n 27002500山西证券6902666177880.701.01\n 28601198东兴证券4418215885055.720.96\n 29601990南京证券4723285795464.560.95\n 30600369西南证券10641615725186.180.94\n 31002673西部证券5610265688803.640.93\n 32600909华安证券6963845571072.000.91\n 33002926华西证券4205175248052.160.86\n 34601881中国银河4130455167192.950.85\n 35600155华创阳安3899115142926.090.84\n 36002670国盛金控3716455110118.750.84\n 37600918中泰证券2451004534350.000.74\n 38000686东北证券4497824416859.240.72\n 39601236红塔证券2306004286854.000.70\n 40600621华鑫股份1696083861974.160.63\n 41002939长城证券2980003835260.000.63\n 第59页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 42601375中原证券4421883104159.760.51\n 43600864哈投股份3325002822925.000.46\n 44601696中银证券976002701568.000.44\n 45000712锦龙股份1432742265161.940.37\n 46600095湘财股份1200001752000.000.29\n 47002945华林证券862001299034.000.21\n 48603093南华期货36800676752.000.11\n 49002961瑞达期货19300463200.000.08\n 8.3.2期末积极投资按公允价值占基金资产净值比例大小排序的所有股票投资明细\n 金额单位：人民币元占基金资产净\n 序号股票代码股票名称数量(股)公允价值\n 值比例(％)\n 1601995中金公司24951.001714383.210.28\n 2688339亿华通2761.00712199.950.12\n 3688521芯原股份7200.00561528.000.09\n 4688289圣湘生物4253.00467149.520.08\n 5688277天智航8810.00330639.300.05\n 6688165埃夫特20109.00263427.900.04\n 7688179阿拉丁3944.00253362.560.04\n 8688127蓝特光学5322.00178819.200.03\n 9688155先惠技术2403.00161625.780.03\n 10688386泛亚微透3217.00152517.970.02\n 11300869康泰医学862.0095578.560.02\n 12300896爱美客157.0094727.520.02\n 13688577浙海德曼1503.0063141.030.01\n 14688215瑞晟智能1229.0056681.480.01\n 15300872天阳科技798.0030196.320.00\n 16300900广联航空879.0029147.640.00\n 17300877金春股份569.0028933.650.00\n 18300861美畅股份507.0025907.700.00\n 19300871回盛生物618.0023916.600.00\n 20300910瑞丰新材474.0022690.380.00\n 21300860锋尚文化159.0020140.530.00\n 22300873海晨股份484.0019998.880.00\n 23300878维康药业401.0019536.720.00\n 24300891惠云钛业1143.0018745.200.00\n 25300868杰美特431.0018653.680.00\n 26300908仲景食品285.0017225.400.00\n 27300884狄耐克499.0016721.490.00\n 28300898熊猫乳品496.0016382.880.00\n 29300892品渥食品272.0016322.720.00\n 30300887谱尼测试210.0015649.200.00\n 第60页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 31300913兆龙互连630.0015082.200.00\n 32300864南大环境166.0015081.100.00\n 33300889爱克股份479.0014427.480.00\n 34300902国安达551.0013367.260.00\n 35300895铜牛信息283.0012819.900.00\n 36300907康平科技473.0012700.050.00\n 37300879大叶股份466.0012637.920.00\n 38300893松原股份276.009682.080.00\n 39300881盛德鑫泰291.009402.210.00\n 8.4报告期内股票投资组合的重大变动\n 8.4.1累计买入金额超出期初基金资产净值2％或前20名的股票明细\n 金额单位：人民币元占期初基金资序号股票代码股票名称本期累计买入金额产净值比例\n （％）\n 1600030中信证券100054759.8544.65\n 2601688华泰证券54930015.1824.52\n 3600837海通证券51678859.9223.06\n 4300059东方财富50375739.5922.48\n 5601211国泰君安32467847.8714.49\n 6600999招商证券27748726.1212.38\n 7000776广发证券19641967.208.77\n 8000166申万宏源17689788.897.89\n 9600958东方证券15616501.316.97\n 10601377兴业证券13456019.006.01\n 11002736国信证券13328833.755.95\n 12601990南京证券12692398.605.66\n 13601901方正证券11830459.355.28\n 14601788光大证券11607060.005.18\n 15600109国金证券11572667.465.16\n 16601162天风证券10890362.354.86\n 17601555东吴证券10814157.844.83\n 18601066中信建投10770408.554.81\n 19000783长江证券10677146.254.77\n 20601108财通证券9879054.964.41\n 21600061国投资本9364547.414.18\n 22601099太平洋9023396.054.03\n 23002797第一创业8996923.014.02\n 24601878浙商证券8858459.003.95\n 25000728国元证券8686093.203.88\n 第61页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 26601456国联证券7481272.243.34\n 27601198东兴证券7419679.823.31\n 28000750国海证券7151264.753.19\n 29601881中国银河7110932.323.17\n 30600155华创阳安6987082.003.12\n 31002926华西证券6920871.343.09\n 32002500山西证券6881057.723.07\n 33600909华安证券6527806.002.91\n 34002673西部证券6486853.082.90\n 35600369西南证券6291151.002.81\n 36002939长城证券5881305.412.62\n 37601236红塔证券5501110.002.46\n 38000686东北证券4550265.322.03\n 注：本项“买入金额”均按买卖成交金额（成交单价乘以成交数量）填列，不考虑相关交易费用。\n 8.4.2累计卖出金额超出期初基金资产净值2％或前20名的股票明细\n 金额单位：人民币元占期初基金资序号股票代码股票名称本期累计卖出金额产净值比例\n （％）\n 1600030中信证券55486502.8024.76\n 2600837海通证券32600562.0314.55\n 3601688华泰证券30538460.4613.63\n 4300059东方财富23376557.6410.43\n 5601211国泰君安18067469.418.06\n 6600999招商证券14071496.736.28\n 7000776广发证券11442827.035.11\n 8000166申万宏源9209532.674.11\n 9600958东方证券6609229.682.95\n 10601066中信建投6546337.002.92\n 11601990南京证券6194994.512.76\n 12002736国信证券6012674.482.68\n 13601901方正证券5887973.972.63\n 14688981中芯国际5703493.122.55\n 15601377兴业证券5512065.002.46\n 16600061国投资本5387273.002.40\n 17600109国金证券5362583.152.39\n 18000783长江证券5255731.702.35\n 19601555东吴证券4870497.442.17\n 20601788光大证券4829479.962.16\n 21601198东兴证券4550018.082.03\n 第62页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 注：本项“卖出金额”均按买卖成交金额（成交单价乘以成交数量）填列，不考虑相关交易费用。\n 8.4.3买入股票的成本总额及卖出股票的收入总额\n 单位：人民币元\n 买入股票的成本（成交）总额679337267.90\n 卖出股票的收入（成交）总额386639215.54\n 注：本项“买入股票的成本（成交）总额”和“卖出股票的收入（成交）总额”均按买卖成交金额（成交单价乘以成交数量）填列，不考虑相关交易费用。\n 8.5期末按债券品种分类的债券投资组合\n 本基金本报告期末未持有债券投资。\n 8.6期末按公允价值占基金资产净值比例大小排序的前五名债券投资明细\n 本基金本报告期末未持有债券。\n 8.7期末按公允价值占基金资产净值比例大小排序的所有资产支持证券投资明细\n 本基金本报告期末未持有资产支持证券。\n 8.8报告期末按公允价值占基金资产净值比例大小排序的前五名贵金属投资明细\n 本基金本报告期末未持有贵金属投资。\n 8.9期末按公允价值占基金资产净值比例大小排序的前五名权证投资明细\n 本基金本报告期末未持有权证投资。\n 8.10报告期末本基金投资的股指期货交易情况说明\n 8.10.1报告期末本基金投资的股指期货持仓和损益明细\n 本基金本报告期末未持有股指期货。\n 8.10.2本基金投资股指期货的投资政策无。\n 8.11报告期末本基金投资的国债期货交易情况说明\n 8.11.1本期国债期货投资政策\n 第63页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告无。\n 8.11.2报告期末本基金投资的国债期货持仓和损益明细\n 本基金本报告期末未持有国债期货。\n 8.11.3本期国债期货投资评价无。\n 8.12投资组合报告附注\n 8.12.1本报告期内，本基金投资的前十名证券的发行主体没有被监管部门立案调查的，也没有在报\n 告编制日前一年内受到公开谴责、处罚的情况。\n 8.12.2本基金投资的前十名股票中，不存在投资于超出基金合同规定备选股票库之外的股票。\n 8.12.3期末其他各项资产构成\n 单位：人民币元序号名称金额\n 1存出保证金131848.75\n 2应收证券清算款-\n 3应收股利-\n 4应收利息4069.47\n 5应收申购款3965127.14\n 6其他应收款-\n 7待摊费用-\n 8其他-\n 9合计4101045.36\n 8.12.4期末持有的处于转股期的可转换债券明细\n 本基金本报告期末未持有处于转股期的可转换债券。\n 8.12.5期末前十名股票中存在流通受限情况的说明\n 8.12.5.1期末指数投资前十名股票中存在流通受限情况的说明\n 本基金本报告期末未持有存在流通受限情况的股票。\n 8.12.5.2期末积极投资前五名股票中存在流通受限情况的说明\n 第64页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 金额单位：人民币元流通受限部分的公允占基金资产净序号股票代码股票名称流通受限情况说明\n 价值值比例(%)\n 1601995中金公司1714383.210.28处于锁定期\n 2688339亿华通712199.950.12处于锁定期\n 3688521芯原股份561528.000.09处于锁定期\n 4688289圣湘生物467149.520.08处于锁定期\n 5688277天智航330639.300.05处于锁定期\n 8.12.6投资组合报告附注的其他文字描述部分无。\n §9基金份额持有人信息\n 9.1期末基金份额持有人户数及持有人结构\n 份额单位：份持有人结构持有人户户均持有的机构投资者个人投资者份额级别\n 数(户)基金份额占总份占总份持有份额持有份额额比例额比例华安中证全指证\n 券公司指数分级13755057.24242800.003.22%7300042.0096.78%\n A华安中证全指证\n 100.00\n 券公司指数分级10886932.760.000.00%7542842.00\n %\n B华安中证全指证\n 742946572.1588852511.4218.20%399418614.0281.80%\n 券公司指数分级\n 合计755196665.3089095311.4217.70%414261498.0282.30%\n 第65页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 9.2期末上市基金前十名持有人\n 华安中证全指证券公司指数分级 A\n 序号持有人名称持有份额（份）占上市总份额比例\n 1陈建燕824833.0010.94%\n 2胡园园715022.009.48%\n 3刘文茵692952.009.19%\n 4丁碧霞608247.008.06%\n 5傅越千412144.005.46%\n 6谢琳娜330300.004.38%\n 7王回生305319.004.05%\n 8樊孝鹏242400.003.21%\n 9穆荣才223151.002.96%\n 10王武世220000.002.92%\n 华安中证全指证券公司指数分级B\n 序号持有人名称持有份额（份）占上市总份额比例\n 1王武世677000.008.98%\n 2邓意超573400.007.60%\n 3李霞566530.007.51%\n 4朱洪宏476380.006.32%\n 5钟贵雄373410.004.95%\n 6王泽稷360000.004.77%\n 7林辉218401.002.90%\n 8欧静虹196593.002.61%\n 9钱中明154569.002.05%\n 10杨芳133736.001.77%\n 9.3期末基金管理人的从业人员持有本基金的情况\n 项目份额级别持有份额总数（份）占基金总份额比例华安中证全指证券公司\n 100023.001.33%\n 指数分级 A华安中证全指证券公司\n 基金管理人所有从业人0.000.00%\n 指数分级 B员持有本基金华安中证全指证券公司\n 13117.170.00%\n 指数分级\n 合计113140.170.02%\n 9.4期末基金管理人的从业人员持有本开放式基金份额总量区间的情况\n 本公司高级管理人员、基金投资和研究部门负责人持有该只基金份额总量的数量区间为0。\n 本基金的基金经理持有本基金份额总量的数量区间为0。\n 第66页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 9.5发起式基金发起资金持有份额情况\n §10开放式基金份额变动\n 单位：份华安中证全指证券华安中证全指证券华安中证全指证券项目\n 公司指数分级 A 公司指数分级 B 公司指数分级基金合同生效日（2015年6月9\n 101534190.00101534190.0046316587.95日）基金份额总额\n 本报告期期初基金份额总额16678941.0016678941.00190581533.68\n 本报告期基金总申购份额--1278675323.72\n 减：本报告期基金总赎回份额--1010013597.07\n 本报告期基金拆分变动份额-9136099.00-9136099.0029027865.11\n 本报告期期末基金份额总额7542842.007542842.00488271125.44拆分变动份额含本基金三级份额之间的配对转换份额和基金份额折算后的调整份额。\n §11重大事件揭示\n 11.1基金份额持有人大会决议\n 本基金于从2020年9月16日起至2020年10月30日期间以通讯方式召开了基金份额持有人大会，根据计票结果，本人直接或委托授权代表出具有效表决意见的基金份额持有人所代表的华安证券分级份额未达到在权益登记日基金份额总数的1/2（含1/2），未达到法定的持有人会议召开条件，故本次基金份额持有人大会召开失败。本次持有人大会详情请见本基金管理人于2020年11月4日发布的《关于华安中证全指证券公司指数分级证券投资基金基金份额持有人大会会议情况的公告》。\n 11.2基金管理人、基金托管人的专门基金托管部门的重大人事变动\n 1、本报告期内本基金管理人重大人事变动如下：\n （1）2020年8月15日，基金管理人发布了《华安基金管理有限公司关于总经理变更的公告》，童威先生离任本基金管理人的总经理。\n （2）2020年8月22日，基金管理人发布了《华安基金管理有限公司关于督察长变更的公告》，薛珍女士离任本基金管理人的督察长。\n （3）2020年10月17日，基金管理人发布了《华安基金管理有限公司关于总经理变更的公告》，张霄岭先生新任本基金管理人的总经理。\n （4）2020年12月18日，基金管理人发布了《华安基金管理有限公司关于首席信息官任职的公告》，范伟隽先生新任本基金管理人的首席信息官。\n 第67页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 2、本报告期内本基金托管人的专门基金托管部门重大人事变动如下：\n 无。\n 11.3涉及基金管理人、基金财产、基金托管业务的诉讼\n 报告期内无涉及本基金财产、基金托管业务的诉讼。报告期内基金管理人无涉及本基金财产的诉讼。\n 11.4基金投资策略的改变\n 本报告期内无基金投资策略的改变。\n 11.5为基金进行审计的会计师事务所情况\n 本报告期内本基金未改聘为基金审计的会计师事务所。\n 报告年度应支付给聘任会计师事务所的报酬为人民币60000.00元。\n 目前的审计机构已提供审计服务的连续年限：自基金合同生效日起至今。\n 11.6管理人、托管人及其高级管理人员受稽查或处罚等情况\n 本报告期内无管理人、托管人及其高级管理人员受稽查或处罚等情况。\n 11.7基金租用证券公司交易单元的有关情况\n 11.7.1基金租用证券公司交易单元进行股票投资及佣金支付情况\n 金额单位：人民币元股票交易应支付该券商的佣金交易占当期股占当期佣券商名称单元备注成交金额票成交总佣金金总量的数量额的比例比例\n 瑞银证券1-----\n 中银国际1-----\n 中山证券1-----\n 万联证券1-----\n 财信证券1-----\n 粤开证券1-----\n 财通证券1-----\n 华安证券1-----\n 西南证券1-----\n 财达证券1-----\n 中信证券(华\n 2-----\n 南)\n 湘财证券1-----\n 国盛证券1231764999.8822.15%213323.9522.12%-\n 第68页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 广发证券1136392763.8213.03%127021.9913.17%-\n 天风证券2112111093.2510.71%103257.7410.71%-\n 申万宏源391155986.938.71%83070.538.61%-\n 兴业证券188424493.948.45%82350.378.54%-\n 中信建投144164645.974.22%41130.344.26%-\n 中信证券142538590.154.07%39616.354.11%-\n 华泰证券139008710.363.73%36329.433.77%-\n 上海证券334782468.043.32%32392.373.36%-\n 方正证券333133311.393.17%30194.623.13%-\n 中泰证券132977376.413.15%30052.523.12%-\n 光大证券228644989.142.74%26104.222.71%-\n 国泰君安证券130466439.772.91%26085.012.70%-\n 海通证券120895015.602.00%19459.682.02%-\n 国信证券118844358.531.80%17549.761.82%-\n 长江证券215582282.411.49%14395.351.49%-\n 银河证券115252532.241.46%14204.921.47%-\n 东北证券112443700.551.19%11588.761.20%-\n 招商证券38385509.160.80%7809.590.81%-\n 中金公司14729182.090.45%4404.450.46%-\n 新时代证券24704467.560.45%4205.280.44%-\n 注：1、券商专用交易单元选择标准：\n 基金管理人负责选择证券经营机构，选用其交易单元供本基金证券买卖专用，选择标准为：\n （1）内部管理规范、严谨；具备健全的内控制度，并能满足基金运作高度保密的要求；\n （2）研究实力较强，有固定的研究机构和专门研究人员，能够针对本基金业务需要，提供高质量的研究报告和较为全面的服务；\n （3）具有战略规划和定位，能够积极推动多边业务合作，最大限度地调动整体资源，为基金投资赢取机会；\n （4）其他有利于基金持有人利益的商业合作考虑。\n 2、券商专用交易单元选择程序：\n (1)对交易单元候选券商的综合服务进行评估\n 由相关部门牵头并组织有关人员依据上述交易单元选择标准和《券商服务评价办法》，对候选交易单元的券商服务质量和综合实力进行评估。\n (2)填写《新增交易单元申请审核表》牵头部门汇总对各候选交易单元券商的综合评估结果，择优选出拟新增单元，填写《新增交易单元申请审核表》，对拟新增交易单元的必要性和合规性进行阐述。\n 第69页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n (3)候选交易单元名单提交分管领导审批\n 公司分管领导对相关部门提交的《新增交易单元申请审核表》及其对券商综合评估的结果进行审核，并签署审批意见。\n (4)协议签署及通知托管人\n 基金管理人与被选择的券商签订《证券交易单元租用协议》，并通知基金托管人。\n 3、报告期内基金租用券商交易单元的变更情况：\n 无\n 11.7.2基金租用证券公司交易单元进行其他证券投资的情况\n 金额单位：人民币元债券交易回购交易权证交易占当期债占当期回占当期权证券商名称成交金额券成交总成交金额购成交总成交金额成交总额的额的比例额的比例比例\n 瑞银证券------\n 中银国际------\n 中山证券------\n 万联证券------\n 财信证券------\n 粤开证券------\n 财通证券------\n 华安证券------\n 西南证券------\n 财达证券------\n 中信证券(华\n ------\n 南)\n 湘财证券------\n 国盛证券1084509.1016.88%----\n 广发证券------\n 天风证券------\n 申万宏源------\n 兴业证券310320.604.83%----\n 中信建投------\n 中信证券913000.0014.21%----\n 华泰证券1792477.2027.91%----\n 上海证券------\n 第70页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 方正证券------\n 中泰证券------\n 光大证券1305585.4020.33%----\n 国泰君安证券1017500.0015.84%----\n 海通证券------\n 国信证券------\n 长江证券------\n 银河证券------\n 东北证券------\n 招商证券------\n 中金公司------\n 新时代证券------\n 11.8其他重大事件\n 序号公告事项法定披露方式法定披露日期中国证监会基金电子披华安中证全指证券公司指数分级证券投资基\n 1露网站和公司网站等指2020-01-21\n 金2019年第4季度报告定媒介华安基金管理有限公司关于调整旗下基金中国证监会基金电子披\n 22020年春节假期清算交收安排及申购赎回等露网站和公司网站等指2020-01-28\n 交易业务的提示性公告定媒介中国证监会基金电子披华安基金管理有限公司关于公司固有资金投\n 3露网站和公司网站等指2020-02-04\n 资旗下偏股型公募基金相关事宜的公告定媒介中国证监会基金电子披华安基金管理有限公司关于旗下部分分级基\n 4露网站和公司网站等指2020-03-05\n 金暂停基金份额拆分业务的公告定媒介中国证监会基金电子披华安中证全指证券公司指数分级证券投资基\n 5露网站和公司网站等指2020-03-07\n 金基金合同（更新）定媒介中国证监会基金电子披华安中证全指证券公司指数分级证券投资基\n 6露网站和公司网站等指2020-03-07\n 金托管协议（更新）定媒介中国证监会基金电子披华安中证全指证券公司指数分级证券投资基\n 7露网站和公司网站等指2020-03-07\n 金更新的招募说明书摘要（2020年第1号）定媒介中国证监会基金电子披华安中证全指证券公司指数分级证券投资基\n 8露网站和公司网站等指2020-03-07\n 金更新的招募说明书（2020年第1号）定媒介华安基金管理有限公司关于根据信息披露管中国证监会基金电子披\n 9理办法修订旗下公募基金基金合同等法律文露网站和公司网站等指2020-03-07\n 件的公告定媒介\n 10华安基金管理有限公司关于基金电子直销平中国证监会基金电子披2020-03-14\n 第71页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 台调整“微钱宝”账户交易费率优惠活动的公露网站和公司网站等指告定媒介中国证监会基金电子披华安基金管理有限公司关于推迟披露旗下公\n 11露网站和公司网站等指2020-03-25\n 募基金2019年年度报告的公告定媒介中国证监会基金电子披华安基金关于基金电子交易平台延长工行直\n 12露网站和公司网站等指2020-03-30\n 联结算方式费率优惠活动的公告定媒介中国证监会基金电子披华安中证全指证券公司指数分级证券投资基\n 13露网站和公司网站等指2020-04-11\n 金2019年年度报告定媒介中国证监会基金电子披华安中证全指证券公司指数分级证券投资基\n 14露网站和公司网站等指2020-04-22\n 金2020年第1季度报告定媒介华安基金管理有限公司关于暂停泰诚财富基中国证监会基金电子披\n 15金销售（大连）有限公司办理旗下基金相关销露网站和公司网站等指2020-04-24\n 售业务的公告定媒介中国证监会基金电子披关于基金电子交易平台延长工行直联结算方\n 16露网站和公司网站等指2020-06-29\n 式费率优惠活动的公告定媒介中国证监会基金电子披\n 关于基金电子直销平台延长“微钱宝”账户交\n 17露网站和公司网站等指2020-06-29\n 易费率优惠活动的公告定媒介中国证监会基金电子披华安基金管理有限公司关于旗下基金投资关\n 18露网站和公司网站等指2020-07-11\n 联方承销期内承销证券的公告(中芯国际)定媒介中国证监会基金电子披华安基金管理有限公司关于旗下基金投资关\n 19露网站和公司网站等指2020-07-11\n 联方承销期内承销证券的公告(寒武纪)定媒介中国证监会基金电子披关于旗下部分基金增加南京证券为销售机构\n 20露网站和公司网站等指2020-07-14\n 并参加费率优惠活动的公告定媒介中国证监会基金电子披华安中证全指证券公司指数分级证券投资基\n 21露网站和公司网站等指2020-07-21\n 金2020年第2季度报告定媒介中国证监会基金电子披华安基金管理有限公司关于旗下基金持有的\n 22露网站和公司网站等指2020-07-21\n “招商证券”股票估值调整的公告定媒介中国证监会基金电子披华安基金管理有限公司关于旗下基金投资关\n 23露网站和公司网站等指2020-07-29\n 联方承销证券的公告定媒介中国证监会基金电子披华安基金管理有限公司关于旗下基金投资关\n 24露网站和公司网站等指2020-08-04\n 联方承销期内承销证券的公告(亿华通)定媒介\n 第72页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告中国证监会基金电子披华安基金管理有限公司关于旗下基金投资关\n 25露网站和公司网站等指2020-08-04\n 联方承销期内承销证券的公告(天正电气)定媒介中国证监会基金电子披华安基金管理有限公司关于旗下基金投资关\n 26露网站和公司网站等指2020-08-04\n 联方承销期内承销证券的公告(敏芯股份)定媒介中国证监会基金电子披华安基金管理有限公司关于旗下基金投资关\n 27露网站和公司网站等指2020-08-11\n 联方承销期内承销证券的公告(博睿数据)定媒介中国证监会基金电子披华安基金管理有限公司关于旗下基金投资关\n 28露网站和公司网站等指2020-08-12\n 联方承销期内承销证券的公告(均瑶健康)定媒介中国证监会基金电子披华安基金管理有限公司关于总经理变更的公\n 29露网站和公司网站等指2020-08-15\n 告定媒介关于华安中证全指证券公司指数分级证券投中国证监会基金电子披\n 30资基金暂停大额申购、大额转换转入及定期定露网站和公司网站等指2020-08-17\n 额投资的公告定媒介中国证监会基金电子披关于华安中证全指证券公司指数分级证券投\n 31露网站和公司网站等指2020-08-20\n 资基金暂停申购及定期定额投资的公告定媒介中国证监会基金电子披华安基金管理有限公司关于督察长变更的公\n 32露网站和公司网站等指2020-08-22\n 告定媒介中国证监会基金电子披华安基金管理有限公司关于旗下基金投资关\n 33露网站和公司网站等指2020-08-25\n 联方承销期内承销证券的公告(绿的谐波)定媒介中国证监会基金电子披华安中证全指证券公司指数分级证券投资基\n 34露网站和公司网站等指2020-08-28\n 金2020年中期报告定媒介中国证监会基金电子披关于召开华安中证全指证券公司指数分级证\n 35露网站和公司网站等指2020-08-31\n 券投资基金基金份额持有人大会的公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 36券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-08-31\n 提示性公告定媒介华安中证全指证券公司指数分级证券投资基中国证监会基金电子披\n 37 金（华安中证全指证券公司指数分级 A）基金 露网站和公司网站等指 2020-08-31\n 产品资料概要定媒介华安中证全指证券公司指数分级证券投资基中国证监会基金电子披\n 38 金（华安中证全指证券公司指数分级 B）基金 露网站和公司网站等指 2020-08-31\n 产品资料概要定媒介华安中证全指证券公司指数分级证券投资基中国证监会基金电子披\n 392020-08-31金（华安中证全指证券公司指数分级）基金产露网站和公司网站等指\n 第73页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告品资料概要定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 40券投资基金基金份额持有人大会的第一次提露网站和公司网站等指2020-09-01\n 示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 41券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-09-01\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 42券投资基金基金份额持有人大会的第二次提露网站和公司网站等指2020-09-02\n 示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 43券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-09-02\n 提示性公告定媒介中国证监会基金电子披华安基金管理有限公司关于旗下基金投资关\n 44露网站和公司网站等指2020-09-02\n 联方承销期内承销证券的公告(福昕软件)定媒介中国证监会基金电子披关于华安中证全指证券公司指数分级证券投\n 45露网站和公司网站等指2020-09-03\n 资基金恢复申购及定期定额投资的公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 46券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-09-03\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 47券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-09-04\n 提示性公告定媒介中国证监会基金电子披华安基金管理有限公司关于旗下部分基金实\n 48露网站和公司网站等指2020-09-04\n 施赎回费率优惠活动的公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 49券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-09-05\n 提示性公告定媒介中国证监会基金电子披华安基金管理有限公司关于旗下基金投资关\n 50露网站和公司网站等指2020-09-05\n 联方承销期内承销证券的公告(华翔股份)定媒介中国证监会基金电子披关于华安中证全指证券公司指数分级证券投\n 51露网站和公司网站等指2020-09-07\n 资基金恢复基金份额拆分业务的公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 52券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-09-07\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 53券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-09-08\n 提示性公告定媒介\n 54关于华安中证全指证券公司指数分级证券投中国证监会基金电子披2020-09-09\n 第74页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告资基金暂停基金份额拆分业务的公告露网站和公司网站等指定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 55券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-09-09\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 56券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-09-10\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 57券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-09-11\n 提示性公告定媒介华安基金管理有限公司关于暂停上海有鱼基中国证监会基金电子披\n 58金销售有限公司办理旗下基金相关销售业务露网站和公司网站等指2020-09-11\n 的公告定媒介关于召开华安中证银行指数分级证券投资基中国证监会基金电子披\n 59金基金份额持有人大会相关事项的提示性公露网站和公司网站等指2020-09-12\n 告定媒介中国证监会基金电子披华安基金管理有限公司关于旗下基金投资关\n 60露网站和公司网站等指2020-09-12\n 联方承销期内承销证券的公告(天元股份)定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 61券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-09-14\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 62券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-09-15\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 63券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-09-16\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 64券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-09-17\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 65券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-09-18\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 66券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-09-19\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 67券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-09-21\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 68券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-09-22\n 提示性公告定媒介\n 第75页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 69券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-09-23\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 70券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-09-24\n 提示性公告定媒介中国证监会基金电子披\n 关于暂停中民财富基金销售（上海）有限公司\n 71露网站和公司网站等指2020-09-24\n 旗下基金相关销售业务的公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 72券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-09-25\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 73券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-09-26\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 74券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-09-28\n 提示性公告定媒介中国证监会基金电子披关于基金电子交易平台延长工行直联结算方\n 75露网站和公司网站等指2020-09-28\n 式费率优惠活动的公告定媒介中国证监会基金电子披\n 关于基金电子直销平台延长“微钱宝”账户交\n 76露网站和公司网站等指2020-09-28\n 易费率优惠活动的公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 77券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-09-29\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 78券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-09-30\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 79券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-10-09\n 提示性公告定媒介中国证监会基金电子披关于旗下基金参加中国建设银行股份有限公\n 80露网站和公司网站等指2020-10-09\n 司费率优惠活动的公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 81券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-10-10\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 82券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-10-12\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 832020-10-13\n 券投资基金基金份额持有人大会相关事项的露网站和公司网站等指\n 第76页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 84券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-10-14\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 85券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-10-15\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 86券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-10-16\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 87券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-10-17\n 提示性公告定媒介中国证监会基金电子披华安基金管理有限公司关于总经理变更的公\n 88露网站和公司网站等指2020-10-17\n 告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 89券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-10-19\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 90券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-10-20\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 91券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-10-21\n 提示性公告定媒介关于召开华安中证银行指数分级证券投资基中国证监会基金电子披\n 92金基金份额持有人大会相关事项的提示性公露网站和公司网站等指2020-10-22\n 告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 93券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-10-23\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 94券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-10-24\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 95券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-10-26\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 96券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-10-27\n 提示性公告定媒介中国证监会基金电子披华安中证全指证券公司指数分级证券投资基\n 97露网站和公司网站等指2020-10-27\n 金2020年第3季度报告定媒介\n 98关于召开华安中证全指证券公司指数分级证中国证监会基金电子披2020-10-28\n 第77页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告券投资基金基金份额持有人大会相关事项的露网站和公司网站等指提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 99券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-10-29\n 提示性公告定媒介关于召开华安中证全指证券公司指数分级证中国证监会基金电子披\n 100券投资基金基金份额持有人大会相关事项的露网站和公司网站等指2020-10-30\n 提示性公告定媒介关于华安中证全指证券公司指数分级证券投中国证监会基金电子披\n 101 资基金持有人大会之证券股 A 份额、证券股 B 露网站和公司网站等指 2020-11-03\n 份额停牌提示性公告定媒介中国证监会基金电子披关于华安中证全指证券公司指数分级证券投\n 102露网站和公司网站等指2020-11-04\n 资基金基金份额持有人大会会议情况的公告定媒介中国证监会基金电子披华安中证全指证券公司指数分级证券投资基\n 103露网站和公司网站等指2020-11-06\n 金基金合同(更新)定媒介中国证监会基金电子披华安中证全指证券公司指数分级证券投资基\n 104露网站和公司网站等指2020-11-06\n 金托管协议(更新)定媒介中国证监会基金电子披华安基金管理有限公司关于修订旗下部分公\n 105露网站和公司网站等指2020-11-06\n 募基金基金合同的公告定媒介中国证监会基金电子披华安基金管理有限公司关于旗下分级基金整\n 106露网站和公司网站等指2020-11-09\n 改的提示性公告定媒介中国证监会基金电子披华安基金管理有限公司关于旗下分级基金整\n 107露网站和公司网站等指2020-11-10\n 改的提示性公告定媒介中国证监会基金电子披华安中证全指证券公司指数分级证券投资基\n 108露网站和公司网站等指2020-11-11\n 金更新的招募说明书(2020年第2号)定媒介华安中证全指证券公司指数分级证券投资基中国证监会基金电子披\n 109 金（华安中证全指证券公司指数分级 A）基金 露网站和公司网站等指 2020-11-11\n 产品资料概要更新定媒介华安中证全指证券公司指数分级证券投资基中国证监会基金电子披\n 110 金（华安中证全指证券公司指数分级 B）基金 露网站和公司网站等指 2020-11-11\n 产品资料概要更新定媒介华安中证全指证券公司指数分级证券投资基中国证监会基金电子披\n 111金（华安中证全指证券公司指数分级）基金产露网站和公司网站等指2020-11-11\n 品资料概要更新定媒介中国证监会基金电子披华安基金管理有限公司关于旗下分级基金整\n 112露网站和公司网站等指2020-11-11\n 改的提示性公告定媒介\n 第78页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告中国证监会基金电子披华安基金管理有限公司关于旗下分级基金整\n 113露网站和公司网站等指2020-11-12\n 改的提示性公告定媒介中国证监会基金电子披华安基金管理有限公司关于旗下分级基金整\n 114露网站和公司网站等指2020-11-13\n 改的提示性公告定媒介中国证监会基金电子披华安基金管理有限公司关于旗下分级基金整\n 115露网站和公司网站等指2020-11-14\n 改的提示性公告定媒介中国证监会基金电子披华安基金管理有限公司关于旗下分级基金整\n 116露网站和公司网站等指2020-11-16\n 改的提示性公告定媒介中国证监会基金电子披华安基金管理有限公司关于旗下分级基金整\n 117露网站和公司网站等指2020-11-17\n 改的提示性公告定媒介中国证监会基金电子披关于华安中证全指证券公司指数分级证券投\n 118露网站和公司网站等指2020-11-18\n 资基金修订基金合同的公告定媒介中国证监会基金电子披华安中证全指证券公司指数分级证券投资基\n 119露网站和公司网站等指2020-11-18\n 金基金合同(更新)定媒介华安基金管理有限公司关于华安中证全指证中国证监会基金电子披\n 120券公司指数分级证券投资基金整改的提示性露网站和公司网站等指2020-11-19\n 公告定媒介华安基金管理有限公司关于华安中证全指证中国证监会基金电子披\n 121券公司指数分级证券投资基金整改的提示性露网站和公司网站等指2020-11-20\n 公告定媒介华安基金管理有限公司关于华安中证全指证中国证监会基金电子披\n 122券公司指数分级证券投资基金整改的提示性露网站和公司网站等指2020-11-21\n 公告定媒介中国证监会基金电子披华安中证全指证券公司指数分级证券投资基\n 123露网站和公司网站等指2020-11-23\n 金更新的招募说明书(2020年第3号)定媒介华安中证全指证券公司指数分级证券投资基中国证监会基金电子披\n 124 金（华安中证全指证券公司指数分级 A）基金 露网站和公司网站等指 2020-11-23\n 产品资料概要更新定媒介华安中证全指证券公司指数分级证券投资基中国证监会基金电子披\n 125 金（华安中证全指证券公司指数分级 B）基金 露网站和公司网站等指 2020-11-23\n 产品资料概要更新定媒介华安中证全指证券公司指数分级证券投资基中国证监会基金电子披\n 126金（华安中证全指证券公司指数分级）基金产露网站和公司网站等指2020-11-23\n 品资料概要更新定媒介华安基金管理有限公司关于华安中证全指证中国证监会基金电子披\n 1272020-11-23\n 券公司指数分级证券投资基金整改的提示性露网站和公司网站等指\n 第79页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告公告定媒介华安基金管理有限公司关于华安中证全指证中国证监会基金电子披\n 128券公司指数分级证券投资基金整改的提示性露网站和公司网站等指2020-11-24\n 公告定媒介华安基金管理有限公司关于华安中证全指证中国证监会基金电子披\n 129券公司指数分级证券投资基金整改的提示性露网站和公司网站等指2020-11-25\n 公告定媒介中国证监会基金电子披关于旗下基金参加宁波银行费率优惠活动的\n 130露网站和公司网站等指2020-11-26\n 公告定媒介华安基金管理有限公司关于华安中证全指证中国证监会基金电子披\n 131券公司指数分级证券投资基金整改的提示性露网站和公司网站等指2020-11-26\n 公告定媒介华安基金管理有限公司关于华安中证全指证中国证监会基金电子披\n 132券公司指数分级证券投资基金整改的提示性露网站和公司网站等指2020-11-27\n 公告定媒介华安基金管理有限公司关于华安中证全指证中国证监会基金电子披\n 133券公司指数分级证券投资基金整改的提示性露网站和公司网站等指2020-11-30\n 公告定媒介华安基金管理有限公司关于华安中证全指证中国证监会基金电子披\n 134券公司指数分级证券投资基金整改的提示性露网站和公司网站等指2020-12-01\n 公告定媒介关于华安中证全指证券公司指数分级证券投中国证监会基金电子披\n 资基金之华安中证证券 A 份额和华安中证证\n 135露网站和公司网站等指2020-12-02\n 券 B 份额终止运作、终止上市并修改基金合定媒介同的公告中国证监会基金电子披华安中证全指证券公司指数型证券投资基金\n 136露网站和公司网站等指2020-12-02\n 基金合同定媒介中国证监会基金电子披华安中证全指证券公司指数型证券投资基金\n 137露网站和公司网站等指2020-12-02\n 托管协议定媒介华安基金管理有限公司关于华安中证全指证中国证监会基金电子披券公司指数分级证券投资基金之华安中证证\n 138露网站和公司网站等指2020-12-02\n 券 A 份额和华安中证证券 B 份额终止运作的定媒介特别风险提示公告华安基金管理有限公司关于华安中证全指证中国证监会基金电子披券公司指数分级证券投资基金之华安中证证\n 139露网站和公司网站等指2020-12-03\n 券 A 份额和华安中证证券 B 份额终止运作的定媒介特别风险提示公告华安基金管理有限公司关于华安中证全指证中国证监会基金电子披券公司指数分级证券投资基金之华安中证证\n 140露网站和公司网站等指2020-12-04\n 券 A 份额和华安中证证券 B 份额终止运作的定媒介特别风险提示公告\n 第80页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告华安基金管理有限公司关于华安中证全指证中国证监会基金电子披券公司指数分级证券投资基金之华安中证证\n 141露网站和公司网站等指2020-12-07\n 券 A 份额和华安中证证券 B 份额终止运作的定媒介特别风险提示公告华安基金管理有限公司关于华安中证全指证中国证监会基金电子披券公司指数分级证券投资基金之华安中证证\n 142露网站和公司网站等指2020-12-08\n 券 A 份额和华安中证证券 B 份额终止运作的定媒介特别风险提示公告华安基金管理有限公司关于华安中证全指证中国证监会基金电子披券公司指数分级证券投资基金之华安中证证\n 143露网站和公司网站等指2020-12-09\n 券 A 份额和华安中证证券 B 份额终止运作的定媒介特别风险提示公告中国证监会基金电子披华安中证全指证券公司指数分级证券投资基\n 144露网站和公司网站等指2020-12-10\n 金办理定期份额折算业务的公告定媒介华安基金管理有限公司关于华安中证全指证中国证监会基金电子披券公司指数分级证券投资基金之华安中证证\n 145露网站和公司网站等指2020-12-10\n 券 A 份额和华安中证证券 B 份额终止运作的定媒介特别风险提示公告华安基金管理有限公司关于华安中证全指证中国证监会基金电子披券公司指数分级证券投资基金之华安中证证\n 146露网站和公司网站等指2020-12-11\n 券 A 份额和华安中证证券 B 份额终止运作的定媒介特别风险提示公告华安基金管理有限公司关于华安中证全指证中国证监会基金电子披券公司指数分级证券投资基金之华安中证证\n 147露网站和公司网站等指2020-12-14\n 券 A 份额和华安中证证券 B 份额终止运作的定媒介特别风险提示公告华安基金管理有限公司关于华安中证全指证中国证监会基金电子披券公司指数分级证券投资基金之华安中证证\n 148露网站和公司网站等指2020-12-15\n 券 A 份额和华安中证证券 B 份额终止运作的定媒介特别风险提示公告关于华安中证全指证券公司指数分级证券投中国证监会基金电子披\n 149 资基金之华安中证证券 A 份额约定年基准收 露网站和公司网站等指 2020-12-16\n 益率调整的公告定媒介关于华安中证全指证券公司指数分级证券投中国证监会基金电子披\n 150资基金办理定期份额折算业务期间华安中证露网站和公司网站等指2020-12-16\n 证券 A 份额停复牌的公告 定媒介关于华安中证银行指数分级证券投资基金之中国证监会基金电子披\n 151 华安中证银行 A 份额约定年基准收益率调整 露网站和公司网站等指 2020-12-16\n 的公告定媒介关于华安中证银行指数分级证券投资基金办中国证监会基金电子披\n 152 理定期份额折算业务期间华安中证银行 A 份 露网站和公司网站等指 2020-12-16\n 额停复牌的公告定媒介华安基金管理有限公司关于华安中证全指证中国证监会基金电子披\n 1532020-12-16\n 券公司指数分级证券投资基金之华安中证证露网站和公司网站等指\n 第81页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 券 A 份额和华安中证证券 B 份额终止运作的 定媒介特别风险提示公告中国证监会基金电子披关于华安中证全指证券公司指数分级证券投\n 154露网站和公司网站等指2020-12-17\n 资基金定期份额折算结果及恢复交易的公告定媒介中国证监会基金电子披\n 关于华安中证证券 A 份额定期份额折算后次\n 155露网站和公司网站等指2020-12-17日前收盘价调整的公告定媒介华安基金管理有限公司关于华安中证全指证中国证监会基金电子披券公司指数分级证券投资基金之华安中证证\n 156露网站和公司网站等指2020-12-17\n 券 A 份额和华安中证证券 B 份额终止运作的定媒介特别风险提示公告华安基金管理有限公司关于华安中证全指证中国证监会基金电子披券公司指数分级证券投资基金之华安中证证\n 157露网站和公司网站等指2020-12-18\n 券 A 份额和华安中证证券 B 份额终止运作的定媒介特别风险提示公告中国证监会基金电子披华安基金管理有限公司关于旗下所有前端收\n 158露网站和公司网站等指2020-12-18\n 费模式基金在直销柜台实施费率优惠的公告定媒介中国证监会基金电子披华安基金管理有限公司关于首席信息官任职\n 159露网站和公司网站等指2020-12-18\n 的公告定媒介华安基金管理有限公司关于华安中证全指证中国证监会基金电子披券公司指数分级证券投资基金之华安中证证\n 160露网站和公司网站等指2020-12-21\n 券 A 份额和华安中证证券 B 份额终止运作的定媒介特别风险提示公告华安基金管理有限公司关于华安中证全指证中国证监会基金电子披券公司指数分级证券投资基金之华安中证证\n 161露网站和公司网站等指2020-12-22\n 券 A 份额和华安中证证券 B 份额终止运作的定媒介特别风险提示公告华安基金管理有限公司关于华安中证全指证中国证监会基金电子披券公司指数分级证券投资基金之华安中证证\n 162露网站和公司网站等指2020-12-23\n 券 A 份额和华安中证证券 B 份额终止运作的定媒介特别风险提示公告华安基金管理有限公司关于终止北京电盈基中国证监会基金电子披\n 163金销售有限公司办理旗下基金相关销售业务露网站和公司网站等指2020-12-23\n 的公告定媒介华安基金管理有限公司关于华安中证全指证中国证监会基金电子披券公司指数分级证券投资基金之华安中证证\n 164露网站和公司网站等指2020-12-24\n 券 A 份额和华安中证证券 B 份额终止运作的定媒介特别风险提示公告华安基金管理有限公司关于华安中证全指证中国证监会基金电子披券公司指数分级证券投资基金之华安中证证\n 165露网站和公司网站等指2020-12-25\n 券 A 份额和华安中证证券 B 份额终止运作的定媒介特别风险提示公告\n 第82页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告中国证监会基金电子披关于基金电子交易平台延长工行直联结算方\n 166露网站和公司网站等指2020-12-28\n 式费率优惠活动的公告定媒介中国证监会基金电子披\n 关于基金电子直销平台延长“微钱宝”账户交\n 167露网站和公司网站等指2020-12-28\n 易费率优惠活动的公告定媒介华安基金管理有限公司关于华安中证全指证中国证监会基金电子披券公司指数分级证券投资基金之华安中证证\n 168露网站和公司网站等指2020-12-28\n 券 A 份额和华安中证证券 B 份额终止运作的定媒介特别风险提示公告关于华安中证全指证券公司指数分级证券投中国证监会基金电子披\n 169 资基金之华安中证证券 A 份额和华安中证证 露网站和公司网站等指 2020-12-29\n 券 B 份额基金份额折算的公告 定媒介关于华安中证全指证券公司指数分级证券投中国证监会基金电子披\n 170资基金暂停申购、赎回及定期定额投资业务的露网站和公司网站等指2020-12-29\n 公告定媒介中国证监会基金电子披华安中证全指证券公司指数分级证券投资基\n 171露网站和公司网站等指2020-12-29\n 金转型后基金名称、基金简称变更的公告定媒介华安基金管理有限公司关于华安中证全指证中国证监会基金电子披券公司指数分级证券投资基金之华安中证证\n 172露网站和公司网站等指2020-12-29\n 券 A 份额和华安中证证券 B 份额终止运作的定媒介特别风险提示公告华安基金管理有限公司关于华安中证全指证中国证监会基金电子披券公司指数分级证券投资基金之华安中证证\n 173露网站和公司网站等指2020-12-30\n 券 A 份额和华安中证证券 B 份额终止运作的定媒介特别风险提示公告中国证监会基金电子披华安基金管理有限公司关于旗下基金参加部\n 174露网站和公司网站等指2020-12-30\n 分代销机构费率优惠活动的公告定媒介华安基金管理有限公司关于华安中证全指证中国证监会基金电子披券公司指数分级证券投资基金之华安中证证\n 175露网站和公司网站等指2020-12-31\n 券 A 份额和华安中证证券 B 份额终止运作的定媒介特别风险提示公告\n 注：前款所涉重大事件已作为临时报告在指定媒介上披露。\n §12备查文件目录\n 12.1备查文件目录\n 1、《华安中证全指证券公司指数分级证券投资基金基金合同》\n 2、《华安中证全指证券公司指数分级证券投资基金招募说明书》\n 3、《华安中证全指证券公司指数分级证券投资基金托管协议》\n 第83页共84页华安中证全指证券公司指数分级证券投资基金2020年年度报告\n 4、中国证监会批准华安中证全指证券公司指数分级证券投资基金设立的文件；\n 5、本报告期内在中国证监会指定报纸上公开披露的各项公告原件；\n 6、基金管理人业务资格批件、营业执照和公司章程；\n 7、基金托管人业务资格批件和营业执照；\n 8、华安基金管理有限公司开放式基金业务规则；\n 12.2存放地点\n 基金管理人和基金托管人的办公场所，并登载于基金管理人互联网站 http://www.huaan.com.cn。\n 12.3查阅方式\n 投资者可登录基金管理人互联网站查阅，或在营业时间内至基金管理人或基金托管人的办公场所免费查阅。\n 华安基金管理有限公司\n 二〇二一年三月二十六日\n ", "url": "无", "title": ["无"], "source_domain": "集团"} diff --git a/flagdata/quality_assessment/Bert/models/config.json b/flagdata/quality_assessment/Bert/models/config.json new file mode 100644 index 0000000..3dd593e --- /dev/null +++ b/flagdata/quality_assessment/Bert/models/config.json @@ -0,0 +1,35 @@ +{ + "_name_or_path": "bert-base-chinese", + "architectures": [ + "BertModel" + ], + "score_range": [0, 1], + "attention_probs_dropout_prob": 0.1, + "classifier_dropout": null, + "directionality": "bidi", + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_norm_eps": 1e-12, + "max_position_embeddings": 512, + "model_type": "bert", + "num_attention_heads": 12, + "num_hidden_layers": 12, + "pad_token_id": 0, + "pooler_fc_size": 768, + "pooler_num_attention_heads": 12, + "pooler_num_fc_layers": 3, + "pooler_size_per_head": 128, + "pooler_type": "first_token_transform", + "position_embedding_type": "absolute", + "torch_dtype": "float32", + "transformers_version": "4.31.0", + "type_vocab_size": 2, + "use_cache": true, + "vocab_size": 21128, + "doc_cfg": {"sequences": 1}, + "segment_cfg": {"use_segment_scale": false, "scales": [90, 30, 130, 10], + "sequences": [12, 36, 9, 108]} + } \ No newline at end of file diff --git a/flagdata/quality_assessment/Bert/models/tokenizer/special_tokens_map.json b/flagdata/quality_assessment/Bert/models/tokenizer/special_tokens_map.json new file mode 100644 index 0000000..7520992 --- /dev/null +++ b/flagdata/quality_assessment/Bert/models/tokenizer/special_tokens_map.json @@ -0,0 +1,7 @@ +{ + "cls_token": "[CLS]", + "mask_token": "[MASK]", + "pad_token": "[PAD]", + "sep_token": "[SEP]", + "unk_token": "[UNK]" +} diff --git a/flagdata/quality_assessment/Bert/models/tokenizer/tokenizer_config.json b/flagdata/quality_assessment/Bert/models/tokenizer/tokenizer_config.json new file mode 100644 index 0000000..8ee5223 --- /dev/null +++ b/flagdata/quality_assessment/Bert/models/tokenizer/tokenizer_config.json @@ -0,0 +1,15 @@ +{ + "clean_up_tokenization_spaces": true, + "cls_token": "[CLS]", + "do_basic_tokenize": true, + 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+##｡ +##｢ +##｣ +##､ +##･ +##ｯ +##ｰ +##ｲ +##ｸ +##ｼ +##ｽ +##ﾄ +##ﾉ +##ﾌ +##ﾗ +##ﾙ +##ﾝ +##ﾞ +##ﾟ +##￣ +##￥ +##👍 +##🔥 +##😂 +##😎 diff --git a/flagdata/quality_assessment/Bert/network/document_bert_architectures.py b/flagdata/quality_assessment/Bert/network/document_bert_architectures.py new file mode 100644 index 0000000..b97ac82 --- /dev/null +++ b/flagdata/quality_assessment/Bert/network/document_bert_architectures.py @@ -0,0 +1,122 @@ +import torch +import time +from torch import nn +from torch.nn import LSTM +from transformers import BertPreTrainedModel, BertConfig, BertModel +import torch.nn.functional as F + + +def init_weights(m): + if isinstance(m, nn.Linear): + torch.nn.init.xavier_uniform_(m.weight) + m.bias.data.fill_(7) + + +class DocumentBertSentenceChunkAttentionLSTM(BertPreTrainedModel): + def __init__(self, bert_model_config: BertConfig, pretrained_bert=None): + super(DocumentBertSentenceChunkAttentionLSTM, self).__init__(bert_model_config) + if pretrained_bert is None: + self.bert = BertModel(bert_model_config) + else: + self.bert = pretrained_bert + self.dropout = nn.Dropout(p=bert_model_config.hidden_dropout_prob) + self.lstm = LSTM(bert_model_config.hidden_size, bert_model_config.hidden_size) + self.mlp = nn.Sequential( + nn.Dropout(p=bert_model_config.hidden_dropout_prob), + nn.Linear(bert_model_config.hidden_size, 1) + ) + self.w_omega = nn.Parameter(torch.Tensor(bert_model_config.hidden_size, bert_model_config.hidden_size)) + self.b_omega = nn.Parameter(torch.Tensor(1, bert_model_config.hidden_size)) + self.u_omega = nn.Parameter(torch.Tensor(bert_model_config.hidden_size, 1)) + + nn.init.uniform_(self.w_omega, -0.1, 0.1) + nn.init.uniform_(self.u_omega, -0.1, 0.1) + nn.init.uniform_(self.b_omega, -0.1, 0.1) + self.mlp.apply(init_weights) + + def forward(self, document_batch: torch.Tensor, bert_batch_size=0): + # if self.training: + # # 不同doc串行，为了更大的batch_size + # bert_output = torch.zeros( + # size=(document_batch.shape[0], min(document_batch.shape[1],bert_batch_size), + # self.bert.config.hidden_size), dtype=torch.float, device=self.device) + + # for doc_id in range(document_batch.shape[0]): + # bert_output[doc_id][:bert_batch_size] = self.dropout( + # self.bert(document_batch[doc_id][:bert_batch_size, 0], + # token_type_ids=document_batch[doc_id][:bert_batch_size, 1], + # attention_mask=document_batch[doc_id][:bert_batch_size, 2])[1]) + # else: + # 并行化推理 + N = document_batch.shape[0] + bert_batch_size = min(bert_batch_size, document_batch.shape[1]) + bert_output = self.dropout( + self.bert( + document_batch[:, :bert_batch_size, 0, :].reshape((N * bert_batch_size, -1)), + token_type_ids=document_batch[:, :bert_batch_size, 1, :].reshape((N * bert_batch_size, -1)), + attention_mask=document_batch[:, :bert_batch_size, 2, :].reshape((N * bert_batch_size, -1)) + )[1]) + bert_output = bert_output.reshape((N, bert_batch_size, -1)) + + output, (_, _) = self.lstm(bert_output.permute(1, 0, 2)) + output = output.permute(1, 0, 2) + # (batch_size, seq_len, num_hiddens) + attention_w = torch.tanh(torch.matmul(output, self.w_omega) + self.b_omega) + attention_u = torch.matmul(attention_w, self.u_omega) # (batch_size, seq_len, 1) + attention_score = F.softmax(attention_u, dim=1) # (batch_size, seq_len, 1) + attention_hidden = output * attention_score # (batch_size, seq_len, num_hiddens) + attention_hidden = torch.sum(attention_hidden, dim=1) # 加权求和 (batch_size, num_hiddens) + prediction = self.mlp(attention_hidden) + assert prediction.shape[0] == document_batch.shape[0] + return prediction + + +class DocumentBertCombineWordDocumentLinear(BertPreTrainedModel): + def __init__(self, bert_model_config: BertConfig, pretrained_bert=None): + super(DocumentBertCombineWordDocumentLinear, self).__init__(bert_model_config) + if pretrained_bert is None: + self.bert = BertModel(bert_model_config) + else: + self.bert = pretrained_bert + self.bert_batch_size = 1 # 为了运算速度，对于不同的文章仅取第一个分段 + self.dropout = nn.Dropout(p=bert_model_config.hidden_dropout_prob) + + self.mlp = nn.Sequential( + nn.Dropout(p=bert_model_config.hidden_dropout_prob), + nn.Linear(bert_model_config.hidden_size * 2, 1) + ) + self.mlp.apply(init_weights) + + def forward(self, document_batch: torch.Tensor): + # if self.training: + # # 不同doc串行，为了更大的batch_size + # bert_output = torch.zeros(size=(document_batch.shape[0], + # min(document_batch.shape[1], self.bert_batch_size), + # self.bert.config.hidden_size * 2), + # dtype=torch.float, device=self.device) + + # for doc_id in range(document_batch.shape[0]): + # all_bert_output_info = self.bert(document_batch[doc_id][:self.bert_batch_size, 0].squeeze(dim=1), + # token_type_ids=document_batch[doc_id][:self.bert_batch_size, 1], + # attention_mask=document_batch[doc_id][:self.bert_batch_size, 2]) + # bert_token_max = torch.max(all_bert_output_info[0], 1) + # bert_output[doc_id][:self.bert_batch_size] = torch.cat( + # (bert_token_max.values, all_bert_output_info[1]), 1) + # else: + # 并行化推理 + # start = torch.cuda.Event(enable_timing=True) + # end = torch.cuda.Event(enable_timing=True) + # start.record() + all_bert_output_info = self.bert(document_batch[:, :self.bert_batch_size, 0, :].squeeze(dim=1), + token_type_ids=document_batch[:, :self.bert_batch_size, 1, :].squeeze(dim=1), + attention_mask=document_batch[:, :self.bert_batch_size, 2, :].squeeze(dim=1)) + # end.record() + # torch.cuda.synchronize() + # print("BERT-DOC-TOKEN 耗时" + str(start.elapsed_time(end))) + + bert_token_max = torch.max(all_bert_output_info[0], 1) + bert_output = torch.cat((bert_token_max.values, all_bert_output_info[1]), 1) + + prediction = self.mlp(bert_output.view(bert_output.shape[0], -1)) + assert prediction.shape[0] == document_batch.shape[0] + return prediction diff --git a/flagdata/quality_assessment/Bert/network/model_architechure_bert_multi_scale.py b/flagdata/quality_assessment/Bert/network/model_architechure_bert_multi_scale.py new file mode 100644 index 0000000..4ba431f --- /dev/null +++ b/flagdata/quality_assessment/Bert/network/model_architechure_bert_multi_scale.py @@ -0,0 +1,113 @@ +import os +import time +import torch +from transformers import BertModel +from utils.encode import encode_documents +from utils.data import asap_essay_lengths, fix_score +from transformers import BertConfig, CONFIG_NAME, BertTokenizer +from network.document_bert_architectures import DocumentBertCombineWordDocumentLinear, DocumentBertSentenceChunkAttentionLSTM + + + +class DocumentBertScoringModel(torch.nn.Module): + def __init__(self, args): + super(DocumentBertScoringModel, self).__init__() + self.args = vars(args) + + # bert config + if os.path.exists(self.args['bert_model_path']): + cfg_path = os.path.join(self.args['bert_model_path'], CONFIG_NAME) + if os.path.exists(cfg_path): + config = BertConfig.from_json_file(cfg_path) + + self.config = config + self.prompt = int(args.prompt[1]) + + # 得分范围 + self.min_score, self.max_score = config.score_range[0], config.score_range[1] + self.sigmoid = torch.nn.Sigmoid() + + # Multi-Scale-BERT-AES 的每个Segment尺度，'90_30_130_10' + self.chunk_sizes = self.args['chunk_sizes'] + self.use_segment_scale = config.segment_cfg["use_segment_scale"] + + # bert_batch_size 每个 Segment 尺度对应的文章切分数, 处于计算速度与统计结果的平衡，对于每第 i 个segment尺度的切分段 + # 如果超过self.bert_batch_sizes[i], 取前self.bert_batch_sizes[i]个Segment + self.bert_batch_sizes = [] + if 0 not in self.chunk_sizes: + for chunk_size in self.chunk_sizes: + bert_batch_size = int(asap_essay_lengths[self.prompt] / chunk_size) + 1 + self.bert_batch_sizes.append(bert_batch_size) + + # bert tokenizer + self.bert_tokenizer = BertTokenizer.from_pretrained(self.args['tokenizer_path']) + + # BERT-TOKEN-DOC 模型，计算文档&Token 尺度得分 + if 'pretrained_model_path' in list(self.args.keys()) and \ + os.path.exists(self.args['pretrained_model_path']): + bert_token_doc = BertModel.from_pretrained(self.args['pretrained_model_path'], + config=config) + else: + bert_token_doc = BertModel.from_pretrained("bert-base-chinese") + + self.bert_regression_by_word_document = DocumentBertCombineWordDocumentLinear( + config, bert_token_doc) + + if self.use_segment_scale: + # BERT-Segment 模型， 计算每个Segment尺度的得分 + if 'pretrained_model_path' in list(self.args.keys()) and \ + os.path.exists(self.args['pretrained_model_path']): + print(self.args['pretrained_model_path']) + bert_seg = BertModel.from_pretrained(self.args['pretrained_model_path'], + config=config) + else: + bert_seg = BertModel.from_pretrained("bert-base-chinese") + self.bert_regression_by_chunk = DocumentBertSentenceChunkAttentionLSTM( + config, bert_seg) + + + + def forward(self, document_representations_word_document, + document_representations_chunk_list=None): + """ + :param document_representations_word_document: 文档&Token 尺度的表示 + Tensor + size: (batch_size, k, 3, L) + + :param document_representations_chunk_list: 每个Segment尺度的表示 + list of Tensor + size: [(batch_size, k_scale, 3, L_scale)] * scale_num + + :return: 文档&Token 尺度的得分 + 每个Segment尺度的得分 + """ + # 文档&Token 尺度的得分 + batch_predictions_word_document = self.bert_regression_by_word_document( + document_representations_word_document) + batch_predictions_word_document = torch.squeeze(batch_predictions_word_document) + batch_predictions_word_chunk_sentence_doc = batch_predictions_word_document + + if self.use_segment_scale: + # 每个Segment尺度的得分 + + for chunk_index in range(len(self.chunk_sizes)): + batch_document_tensors_chunk = document_representations_chunk_list[chunk_index] + + batch_predictions_chunk = self.bert_regression_by_chunk( + batch_document_tensors_chunk, bert_batch_size=self.bert_batch_sizes[chunk_index]) + + batch_predictions_chunk = torch.squeeze(batch_predictions_chunk) + batch_predictions_word_chunk_sentence_doc = torch.add(batch_predictions_word_chunk_sentence_doc, + batch_predictions_chunk) + + # 最终得分设定在[a, b]之间 + score = self.min_score + (self.max_score - self.min_score) * self.sigmoid( + batch_predictions_word_chunk_sentence_doc) + + return score + + + + + + + diff --git a/flagdata/quality_assessment/Bert/utils/data.py b/flagdata/quality_assessment/Bert/utils/data.py new file mode 100644 index 0000000..1e79626 --- /dev/null +++ b/flagdata/quality_assessment/Bert/utils/data.py @@ -0,0 +1,519 @@ +import torch +import numpy as np +import multiprocessing +import random, os, json, math +from torch.utils.data import Dataset +from utils.encode import encode_document, encode_document_with_whole_segment + +asap_ranges = { + 1: (2.0, 12.0), + 2: (1.0, 6.0), + 3: (0.0, 3.0), + 4: (0.0, 3.0), + 5: (0.0, 4.0), + 6: (0.0, 4.0), + 7: (0.0, 30.0), + 8: (0.0, 60.0), + 9: (0.5, 9.0), + 10: (1.0, 24.0), +} + +text_quality_ranges = { + "pretrain": (0.0, 1.0), + "fine_tune": (0.0, 1.0) +} + +asap_essay_lengths = { + 1: 649, + 2: 704, + 3: 219, + 4: 203, + 5: 258, + 6: 289, + 7: 371, + 8: 1077, + 9: 415, + 10: 1024, + 11: 252 +} + + +def text_quality_score(score, mode): + """ + fix the predicted score + """ + min_score, max_score = text_quality_ranges[mode] + if score < min_score: + return min_score + elif score > max_score: + return max_score + else: + return score + + +def fix_score(score, prompt): + """ + fix the predicted score + """ + if prompt == 9: # telis + int_part = float(int(score)) + float_part = score - int_part + result = int_part + if float_part < 0.25: + result = int_part + elif float_part < 0.75: + result = int_part + 0.5 + else: + result = int_part + 1 + + min_score, max_score = asap_ranges[prompt] + if result < min_score: + return min_score + elif result > max_score: + return max_score + else: + return result + + elif prompt <= 10: + min_score, max_score = asap_ranges[prompt] + if score < min_score: + return min_score + elif score > max_score: + return max_score + else: + return round(score) + else: + return score + + +def is_zh(s): + # '包含汉字的返回TRUE' + for c in s: + if c >= '\u4e00' and c <= '\u9fa5': + return True + return False + + +def load_asap_data(data_file, max_len=1024, data_sample_rate=1.0): + ids = [] + texts = [] + labels = [] + sample_index = 0 + with open(data_file) as fin: + for line in fin: + rand_value = random.random() + if rand_value > data_sample_rate: + continue + line = line.strip() + line_vec = line.split("\t") + if len(line_vec) == 3: + ids.append(line_vec[0]) + if len(line_vec[1].split(" ")) >= max_len: + line_vec[1] = " ".join(line_vec[1].split(" ")[0:max_len]) + texts.append(line_vec[1]) + labels.append(float(line_vec[2])) + else: + ids.append(str(sample_index)) + sample_index += 1 + if is_zh(line_vec[0]) and len(line_vec[0].replace(" ", "")) >= max_len: + line_vec[0] = line_vec[0].replace(" ", "")[0:max_len] + elif len(line_vec[0].split(" ")) >= max_len: + line_vec[0] = " ".join(line_vec[0].split(" ")[0:max_len]) + texts.append(line_vec[0]) + labels.append(float(line_vec[1])) + for id, text, label in zip(ids, texts, labels): + yield (id, text, label) + + +class DocumentDataset(Dataset): + def __init__(self, file, tokenizer, max_len, doc_config, + segment_config, train_cfg): + """ + file: 文件路径 + tokenizer: 分词器 + max_len: 最大长度 + doc_config: Doc Squences + segment_config: {"Segment Scale": float, "Segment Squences": float} + train_cfg: 训练配置 + """ + super().__init__() + self.tokenizer = tokenizer + self.max_len = max_len + self.segment_scales = segment_config["scales"] + self.segment_sequences = segment_config["sequences"] + self.doc_sequences = doc_config["sequences"] + self.whole_segment = train_cfg.whole_segment + + # 文件加载 + if file.endswith(".json"): + # 加载json格式文件 + with open(file, "r", encoding="utf-8") as f: + data = json.load(f) + # 提取 text 与 score 字段 + self.data = [{"text": item["text"], + "score": 1 if item["score"] == 1 else train_cfg.cc_score + } for item in data] + + elif file.endswith(".jsonl"): + # 加载jsonl格式文件 + self.data = [] + with open(file, "r", encoding="utf-8") as f: + for line in f: + item = json.loads(line) + self.data.append( + {"text": item["text"], + "score": 1 if item["score"] == 1 else train_cfg.cc_score}) + + def __len__(self): + return len(self.data) + + def __getitem__(self, index): + item = self.data[index] + text = item["text"] + score = item["score"] + + # BERT-DOC-TOKEN 的输入 + if self.whole_segment: + _, representation_doc_token = encode_document_with_whole_segment( + text, self.tokenizer, self.max_len) + representation_doc_token = representation_doc_token[0, :, :].unsqueeze(0) + + else: + representation_doc_token = encode_document(text, self.tokenizer, + self.max_len, self.doc_sequences) + + label = torch.tensor(score, dtype=torch.float) + + return representation_doc_token, label + + +class DocumentDatasetSiamese(DocumentDataset): + def __init__(self, file, tokenizer, max_len, doc_config, segment_config, train_cfg): + super().__init__(file, tokenizer, max_len, doc_config, segment_config, train_cfg) + self.cc_score = train_cfg.cc_score + self.file = file + + data_num = len(self.data) - len(self.data) % train_cfg.batch_size + self.data = self.data[:data_num] + + # 每个样本设一个唯一的id + for index, item in enumerate(self.data): + item["id"] = index + + def __getitem__(self, index): + item = self.data[index] + text = item["text"] + score = item["score"] + + # BERT-DOC-TOKEN 的输入 + _, representation_doc_token = encode_document_with_whole_segment( + text, self.tokenizer, self.max_len) + representation_doc_token = representation_doc_token[0, :, :].unsqueeze(0) + label = torch.tensor(score, dtype=torch.float) + + return torch.tensor(item["id"], dtype=torch.int), representation_doc_token, label + + def get_length(self): + return len(self.data) + + def data_revision(self, false_positive_ids, false_negative_ids): + # 将部分负样本更改为正样本(score = 1) + for id in false_negative_ids: + self.data[id]["score"] = 1 + + # 将部分正样本更改为负样本(score = 0) + for id in false_positive_ids: + self.data[id]["score"] = self.cc_score + + +class DocumentDatasetForPredict(Dataset): + def __init__(self, file, tokenizer, max_len, doc_config, + segment_config, train_cfg): + """ + file: 文件路径 + tokenizer: 分词器 + max_len: 最大长度 + doc_config: Doc Squences + segment_config: {"Segment Scale": float, "Segment Squences": float} + train_cfg: 训练配置 + """ + super().__init__() + self.tokenizer = tokenizer + self.max_len = max_len + self.segment_scales = segment_config["scales"] + self.segment_sequences = segment_config["sequences"] + self.doc_sequences = doc_config["sequences"] + + key = train_cfg.text_key + + # 文件加载 + if file.endswith(".json"): + # 加载json格式文件 + with open(file, "r", encoding="utf-8") as f: + data = json.load(f) + # 提取 text 与 score 字段 + self.data = [{"text": item[key], + "url": item["url"], + "title": item['title'], + "source_domain": item['source_domain'] + } for item in data] + + + elif file.endswith(".jsonl"): + # 加载jsonl格式文件 + self.data = [] + with open(file, "r", encoding="utf-8") as f: + for line in f: + item = json.loads(line) + if len(item[key].strip()) > 0: + self.data.append( + {"text": item[key], + "url": item["url"], + "title": item['title'], + "source_domain": item['source_domain']}) + + def __len__(self): + print("=================>") + print(self) + return len(self.data) + + def __getitem__(self, index): + item = self.data[index] + text = item["text"] + info = {"url": item["url"], "title": item["title"], "source_domain": item["source_domain"]} + + text_segments, representation_doc_token = encode_document_with_whole_segment( + text, self.tokenizer, self.max_len) + + text_index = [index] * len(text_segments) + + return text_index, text_segments, representation_doc_token, info + + def get_length(self): + return len(self.data) + + +class DocumentDatasetForEvaluation(DocumentDataset): + def __init__(self, file, tokenizer, max_len, doc_config, segment_config, train_cfg): + super().__init__(file, tokenizer, max_len, doc_config, segment_config, train_cfg) + + def __getitem__(self, index): + item = self.data[index] + text = item["text"] + score = item["score"] + + # BERT-DOC-TOKEN 的输入 + representation_doc_token = encode_document(text, self.tokenizer, + self.max_len, self.doc_sequences) + + # BERT-Segment 的输入 + representation_segment_list = [] + for i in range(len(self.segment_scales)): + representation_segment = encode_document(text, self.tokenizer, + self.segment_scales[i], + self.segment_sequences[i]) + representation_segment_list.append(representation_segment) + + label = torch.tensor(score, dtype=torch.float) + + return ([text], representation_doc_token, representation_segment_list), label + + +class DocumentDatasetForUST(Dataset): + + def __init__(self, train_cfg, tokenizer, max_len): + """ + 训练数据采集方式 + ust 采用ust半监督训练的方法，正样本是模型给出的 + """ + super().__init__() + self.tokenizer = tokenizer + self.max_len = max_len + self.train_cfg = train_cfg + self.train_folder = train_cfg.train_folder + self.file_list = [file for file in os.listdir(self.train_folder) if file.endswith(".json")] + self.mode = "sample" + self.data = None + + if train_cfg.use_paradigm: + self.paradigm_data_file = train_cfg.paradigm_data_file + self.paradigm_data_generate() + + def paradigm_data_generate(self): + self.paradigm_texts = [] + with open(self.paradigm_data_file, "r", encoding="utf-8") as f: + json_data = json.load(f) + for item in json_data: + if item["score"] == 1: + self.paradigm_texts.append(item["text"]) + + def candidate_data_generate(self): + selected_file_list = random.sample(self.file_list, self.train_cfg.select_file_num) + total_num = int(self.train_cfg.per_jsonfile_text_num * len(selected_file_list)) + # 生成随机 candidate_data_num 个 index + random_index = random.sample(range(total_num), int(self.train_cfg.candidate_data_num)) + + index_for_file = {} + + # 根据文件拆分index + for n in random_index: + quotient = int(n // self.train_cfg.per_jsonfile_text_num) + remainder = int(n % self.train_cfg.per_jsonfile_text_num) + if selected_file_list[quotient] not in index_for_file: + index_for_file[selected_file_list[quotient]] = [] + index_for_file[selected_file_list[quotient]].append(remainder) + + # 多进程读取数据 + max_process = self.train_cfg.num_workers + pool = multiprocessing.Pool(processes=max_process) + results = [] + for file, indexes in index_for_file.items(): + result = pool.apply_async(self.collect_data, (self.train_folder + file, indexes)) + results.append(result) + + pool.close() + pool.join() + + self.data = [] + for result in results: + self.data.extend(result.get()) + + # 给每个text一个唯一的id + for index, item in enumerate(self.data): + item["id"] = index + + def select_samples(self, ids: list, pseudo_labels: list, confidences: list): + """ + indexes: 被选择的，用于训练的样本，每个index在 [0, candidate_data_num - 1]之间 + """ + assert len(ids) == len(pseudo_labels) + input_dict = {ids[n]: {"pseudo_label": pseudo_labels[n], "confidence": confidences[n] + } for n in range(len(ids))} + + dict_with_id_key = {item["id"]: item for item in self.data} + self.data = [{"text": dict_with_id_key[identity]["text"], + "pseudo_label": input_dict[identity]["pseudo_label"], + "confidence": input_dict[identity]["confidence"] + } for identity in ids] + + if self.train_cfg.use_paradigm: + paradigm_confidence = np.mean(np.array([self.data[n][ + "confidence"] for n in range((len(self.data)))])) + + neg_data = [item for item in self.data if int(item['pseudo_label']) == 0] + + if self.train_cfg.data_scheme == "paradigm": + # 全部正样本为范例样本 + self.data = neg_data + indexes = random.sample(range(len(self.paradigm_texts)), len(self.data)) + + elif self.train_cfg.data_scheme == "ust_paradigm": + # 一定比例的样本为范例样本，另一部分为Common Crawl中判为正样本的，抽样得到的结果 + paradigm_num = int(self.train_cfg.paradigm_ratio * len(neg_data)) + cc_pos_num = len(neg_data) - paradigm_num + + pos_data = [item for item in self.data if int(item['pseudo_label']) == 1] + indexes = random.sample(range(len(pos_data)), cc_pos_num) + pos_data = [pos_data[n] for n in indexes] + self.data = neg_data + pos_data + indexes = random.sample(range(len(self.paradigm_texts)), paradigm_num) + + select_paradigm_data = [{ + "text": self.paradigm_texts[index], "pseudo_label": 1.0, + "confidence": paradigm_confidence + } for index in indexes] + self.data.extend(select_paradigm_data) + + def __getitem__(self, index): + item = self.data[index] + text = item["text"] + + # BERT-DOC-TOKEN 的输入 + text_segs, representation_doc_token = encode_document_with_whole_segment( + text, self.tokenizer, self.max_len) + representation_doc_token = representation_doc_token[0, :, :].unsqueeze(0) + + if self.mode == "sample": + return torch.tensor(item["id"], dtype=torch.int), representation_doc_token, text_segs[0] + elif self.mode in ["train", "eval"]: + label = torch.tensor(item["pseudo_label"], dtype=torch.float) + confidence = torch.tensor(item["confidence"], dtype=torch.float) + return representation_doc_token, label, confidence, text_segs[0] + + @staticmethod + def collect_data(file, indexes): + data = [] + + with open(file, "r", encoding="utf-8") as f: + json_data = json.load(f) + for index in indexes: + data.append({'text': json_data[index]['raw_content']}) + # print(file) + return data + + def set_mode(self, mode="sample"): + """ + 数据集是用于MC Dropout采样 or 训练 + mode: str "sample", "train" 或 "eval" + """ + self.mode = mode + + def __len__(self): + return len(self.data) + + +if __name__ == "__main__": + import time + from transformers import BertTokenizer, BertConfig + from torch.utils.data import DataLoader + from tqdm import tqdm + from types import SimpleNamespace + + # # 测试数据加载器 + # with open("configs/base_config.json", "r", encoding="utf-8") as f: + # train_cfg = json.load(f) + # train_cfg = SimpleNamespace(**train_cfg) + # model_cfg = BertConfig.from_pretrained(train_cfg.bert_model_path) + # tokenizer = BertTokenizer.from_pretrained(train_cfg.tokenizer_path) + # max_len = 512 + # doc_cfg = {"sequences": 1} + # segment_cfg = {"scales": [90, 30, 130, 10], + # "sequences": [12, 36, 9, 108]} + + # # 测试Dataset输出shape不一致的情况 + # dataset = DocumentDatasetForPredict(train_cfg.train_file, tokenizer, + # model_cfg.max_position_embeddings, + # model_cfg.doc_cfg, model_cfg.segment_cfg, + # train_cfg) + + # dataloader = DataLoader(dataset, batch_size=None, shuffle=False) + # epoch = 0 + # num_epochs = 100 + + # text_index_list = [] + # text_segments_list = [] + # representation_segment_list = [] + # for step, (text_index, text_segs, representation) in enumerate( + # tqdm(dataloader, desc=f"Epoch {epoch+1}/{num_epochs}", leave=False)): + + # text_index_list.extend(text_index) + # text_segments_list.extend(text_segs) + # representation_segment_list.append(representation) + + # if len(text_index_list) >= 64: + # representation_doc_token = torch.cat(representation_segment_list, dim=0) + # print("success") + + # 测试DocumentDatasetForUST数据集 + with open("configs/ust_config.json", "r", encoding="utf-8") as f: + train_cfg = json.load(f) + train_cfg = SimpleNamespace(**train_cfg) + model_cfg = BertConfig.from_pretrained(train_cfg.bert_model_path) + tokenizer = BertTokenizer.from_pretrained(train_cfg.tokenizer_path) + max_len = model_cfg.max_position_embeddings + start_time = time.time() + dataset = DocumentDatasetForUST(train_cfg, tokenizer, max_len) + end_time = time.time() + print(end_time - start_time) + data = dataset.data + print(len(data)) diff --git a/flagdata/quality_assessment/Bert/utils/encode.py b/flagdata/quality_assessment/Bert/utils/encode.py new file mode 100644 index 0000000..960bce2 --- /dev/null +++ b/flagdata/quality_assessment/Bert/utils/encode.py @@ -0,0 +1,155 @@ +import torch, math, re, logging, json +from transformers import BertTokenizer, XLNetTokenizer, RobertaTokenizer, LongformerTokenizer + +log = logging.getLogger() + + +def encode_documents(documents: list, tokenizer: BertTokenizer, max_input_length): + # 对英文文本进行分词 + tokenized_documents = [tokenizer.tokenize(document) for document in documents] + # BERT接受的最大文本长度为512，因此需要对文本进行截断，除了[CLS]和[SEP ]最大为512 + max_sequences_per_document = math.ceil(max(len(x) / (max_input_length - 2) for x in tokenized_documents)) + # shape = (N, S, K, L) N:文档数量；S:由于BERT参数数量限制，需要将输入文本分为 S 段，S=max_sequences_per_document; + # K: 代表token的index， token类型， attention_mask (对于padding token 不需要 attention)，L:每个序列的最大token数 + output = torch.zeros(size=(len(documents), max_sequences_per_document, 3, max_input_length), dtype=torch.long) + document_seq_lengths = [] + for doc_index, tokenized_document in enumerate(tokenized_documents): + max_seq_index = 0 + for seq_index, i in enumerate(range(0, len(tokenized_document), (max_input_length - 2))): + raw_tokens = tokenized_document[i:i + (max_input_length - 2)] + tokens = [] + input_type_ids = [] + tokens.append("[CLS]") + input_type_ids.append(0) + for token in raw_tokens: + tokens.append(token) + input_type_ids.append(0) + tokens.append("[SEP]") + input_type_ids.append(0) + + input_ids = tokenizer.convert_tokens_to_ids(tokens) # 用词汇表中的index表示每个token + attention_masks = [1] * len(input_ids) # attention_mask: 1表示真实token，0表示padding token + + while len(input_ids) < max_input_length: + input_ids.append(0) + input_type_ids.append(0) + attention_masks.append(0) + output[doc_index][seq_index] = torch.cat((torch.LongTensor(input_ids).unsqueeze(0), + torch.LongTensor(input_type_ids).unsqueeze(0), + torch.LongTensor(attention_masks).unsqueeze(0)), + dim=0) + max_seq_index = seq_index + document_seq_lengths.append(max_seq_index + 1) + return output, torch.LongTensor(document_seq_lengths) + + +def encode_document(document, tokenizer, max_len, sequences): + output = torch.zeros(size=(sequences, 3, max_len), dtype=torch.long) + tokenized_document = tokenizer.tokenize(document) + for seq_index, i in enumerate(range(0, len(tokenized_document), (max_len - 2))): + raw_tokens = tokenized_document[i:i + (max_len - 2)] + tokens = [] + input_type_ids = [] + + tokens.append("[CLS]") + input_type_ids.append(0) + for token in raw_tokens: + tokens.append(token) + input_type_ids.append(0) + + tokens.append("[SEP]") + input_type_ids.append(0) + input_ids = tokenizer.convert_tokens_to_ids(tokens) + attention_masks = [1] * len(input_ids) + + while len(input_ids) < max_len: + input_ids.append(0) + input_type_ids.append(0) + attention_masks.append(0) + + output[seq_index, :, :] = torch.cat((torch.LongTensor(input_ids).unsqueeze(0), + torch.LongTensor(input_type_ids).unsqueeze(0), + torch.LongTensor(attention_masks).unsqueeze(0)), + dim=0) + if seq_index == sequences - 1: + break + + return output + + +# 将文本切分为多个片段，每个片段以的末尾都是完整的段落/句子。 +def encode_document_with_whole_segment(document, tokenizer, max_len, + drop_len_threshold=200): + represent_segs = [] + text_segs = [] + + start_idx = 0 + while start_idx < len(document): + end_idx = find_end_token(document, start_idx, max_len - 2) + + if end_idx == -1: + end_idx = start_idx + max_len - 2 + + text_segment = document[start_idx:end_idx] + if len(text_segment) < drop_len_threshold: + start_idx = end_idx + continue + + text_segs.append(''.join(text_segment)) + + # 将segment处理为模型的输入 + segment_tokens = tokenizer.tokenize(text_segment) + segment_tokens = ["[CLS]"] + segment_tokens + ["[SEP]"] + segment_token_ids = tokenizer.convert_tokens_to_ids(segment_tokens) + segmen_type_ids = [0] * len(segment_token_ids) + segment_attention_masks = [1] * len(segment_token_ids) + + while len(segment_token_ids) < max_len: + segment_token_ids.append(0) + segment_attention_masks.append(0) + segmen_type_ids.append(0) + + segment = torch.cat((torch.LongTensor(segment_token_ids).unsqueeze(0), + torch.LongTensor(segmen_type_ids).unsqueeze(0), + torch.LongTensor(segment_attention_masks).unsqueeze(0)), + dim=0) + + represent_segs.append(segment.unsqueeze(0)) + + start_idx = end_idx + try: + # 使用torch拼接segments + represent_segs = torch.cat(represent_segs, dim=0) + except Exception as e: + logging.warning(f"represent_segs ERROR : {represent_segs}") + logging.warning(f"INFO: {e}") + return text_segs, represent_segs + + +def find_end_token(tokens, start_idx, max_len): + if start_idx + max_len >= len(tokens) - 1: + return -1 + else: + for i in range(start_idx + max_len - 1, start_idx, -1): + + if tokens[i] == "\n": + return i + elif tokens[i] in ['.', '?', '!', '。', '！', '？']: + return i + 1 + + return -1 + + +if __name__ == "__main__": + # 测试数据切分代码 + file = 'data/text_score/webtext.json' + with open(file, 'r', encoding='utf-8') as f: + data = json.load(f) + + # 测试读写jsonl + dist_file = 'data/text_score/webtext_test.jsonl' + with open(dist_file, 'r', encoding='utf-8') as f: + for line in f: + line = json.loads(line) + print(line) + break diff --git a/flagdata/quality_assessment/FastText/data/cleared1.jsonl b/flagdata/quality_assessment/FastText/data/cleared1.jsonl new file mode 100644 index 0000000..299a422 --- /dev/null +++ b/flagdata/quality_assessment/FastText/data/cleared1.jsonl @@ -0,0 +1,100 @@ +{"url": "http://335.3355703.com/news/2022/0314/61491.html", "date_download": "2022-05-19T09:45:36Z", "digest": "sha1:FJEUC6FNIQO7DDB5CM5RYRKFPHCYP2J7", "length": 966, "nlines": 14, "source_domain": "335.3355703.com", "title": "王者威尼斯人现金网网址本命精血一下子就被吸了出来_乐虎国际登录器下载", "raw_content": "铜鼓县一制药公司发生爆炸事故致1死2伤\n文章摘要：王者威尼斯人现金网网址,闪电竟然有龙道圣沉声开口道忐忑所以要灭了千仞峰 ，这是怎么回事他没把歼灭妖兽这一行动放在心上武仙三派都成为了帝品仙器。\n据江西省应急管理厅消息：3月11日1时39分，王者威尼斯人现金网网址：宜春市铜鼓县江西佰利达制药有限公司发生一起爆炸事故，造成1人死亡、2人受伤。省应急管理厅对此高度重视，厅主要领导指示要求加强调度，全力以赴救治受伤人员，做好善后安全稳定，严格落实“三个一律”措施；并于3月11日上午视频连线调度现场救援处置情况，指导事故调查和善后处置工作。\n事故发生后，铜鼓县政府领导和相关部门迅速赶赴现场，开展事故救援和应急处置工作，成立应急抢险组、事故调查组、善后处理组、现场维护组等四个工作组，全力开展事故应急救援、原因调查、现场维护、善后处置等方面工作。目前2名伤者已送医救治，伤势平稳，无生命危险；环境监测结果显示对周边环境未造成污染影响，未引发次生环境污染。\n省应急管理厅认真落实省委省政府“两会”期间安全生产工作要求，迅速派出工作组赶赴现场，勘查事故现场，调阅相关图纸资料，要求严格执行“三个一律”，做好事故调查处理和后续处置工作。经初步了解，事故的直接原因系该企业101车间一反应釜发生爆炸引起（未引发火灾），目前宜春市政府已成立事故调查组，具体原因正在进行全面调查。省安委办下发通报，要求深刻汲取事故教训，充分认识抓好当前危化品安全生产工作的极端重要性，扎实推进危化品安全风险集中治理行动，深入开展“打非治违”巩固提升攻坚战，督促落实企业主体责任，坚决遏制事故发生。\n王者威尼斯人现金网网址：相关新闻\n江西凤凰沟鲜花盛开 市民踏春游玩\n航拍江西赣抚平原 一幅秀美春日田园画卷\n江西高速开展车辆救援技能比武大赛\n满眼绿意浓浓 航拍江西千亩茶园秀色如画\n优乐国际ag代理 威尼斯人现金直营 太阳城电玩城手机版 登峰娱乐代理网址 钻石娱乐电子平台网\n顶尖娱乐电子游戏 玛雅娱乐亚洲 创世九州棋牌官网 红桃k游戏骰宝玩法 威廉希尔管理系统登入\n盛世棋牌 bb电子网址 博发游戏最高返水 皇冠国际充值佣金 同升国际客户端\n无需申请注册就送20\t 2018太阳集团游戏网址 太阳城上网导航 申博太阳城真人登入 太阳申博登入", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 69, "original_length": 4167, "line_ids": [35, 38, 39, 40, 41, 44, 52, 53, 54, 55, 65, 66, 67, 68], "language": "zh", "language_score": 0.97, "perplexity": 1706.7, "bucket": "middle"} +{"url": "http://bdf.1422058.cn/09da0/52149.html", "date_download": "2022-05-19T09:41:20Z", "digest": "sha1:I376MV4HRJOGFOPHYMBU624ROSFIDPL2", "length": 980, "nlines": 16, "source_domain": "bdf.1422058.cn", "title": "什么药治咳喘效果好_贴片机厂家", "raw_content": "什么药治咳喘效果好\n引起慢性支气管炎的原因有很多，而且慢性支气管炎很难完全治疗，一般患有慢性支气管炎的患者都会常常复发，被病痛所熬煎。所以良多患者想理解，怎样医治慢性支气管炎，上面就一起来看看吧，什么药治咳喘效果好。\n慢性支气管炎注意什么：\n1、首先要留意季节的天气变化，调整好身体防寒保暖事宜，以避免感冒或风寒侵入机体导致病情加重和缠绵不愈。\n2、初发病时要及时治疗，平常要多留意休息，尽理避免做过份劳累的工作，让病情加重。生活要有规律。\n3、慢性支气管炎，在病情发作期间应忌食辛辣、鱼腥、生冷之物、不宜过食肥腻食品及过咸食品。什么药治咳喘效果好。\n4、戒烟少酒，保持情绪平稳、不发怒、少冲动、忌讳大喜大悲。\n5、参加适当的体育锤炼，增强体质。室内尽量保持空气新颖湿润，应只管即便少去公开场合和空气混浊的情况，防止病毒感染减轻病情。<北京离婚股权分割\n6、咳嗽痰多者应将痰随时咳出，不应吞咽。如咳痰困难，需别人协助翻身用拍背前方法将痰液排除，以防窒息。\n7、留意病情变化，如病情重复发作减轻，出现心悸、发绀等等情况，可及时到相干医院救治。\n如何治疗慢性支气管炎：\n建议使用罗浮山国药咳特灵，它的主要由小叶榕干浸膏和马来酸氯苯那敏组成。其中小叶榕干浸膏次要是起镇咳、祛痰战争喘作用;而马来酸氯苯那敏是一种抗过敏药物，即次要起消炎的作用，同时具有抗晕动、平静、催眠作用，什么药治咳喘效果好。两种成分适用，可以快速医治咳喘等症状。所以罗浮山国药咳特灵堪称咳嗽经典用药。什么药治咳喘效果好，各种类型的哮喘患者均以气道炎症为共同特性，单纯抗生素并不克不及到达医治的作用，反而会形成耐药。临床治疗多以激素为主，咳特灵可以作为辅助用药：中药身分小叶榕干浸膏能起到平喘感化、西药马来酸氯苯那敏起到抗过敏、消炎作用。所以可以有效的医治慢性支气管炎。什么药治咳喘效果好，大家可以放心购买，建议在医生的指导下运用。\n通过以上内容的引见，置信大家对怎样治疗慢性支气管炎已经有所理解了。慢性支气管炎患者应只管即便少烟少酒，生活房产分割\n规律。平时可以停止适量的运动，提高自身的免疫力，但是要尽量少去不通风和人多的地方进行锻炼。\n电子发烧友网\n 电子发烧友网", "cc_segment": 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"用什么方法可以降低男性白癜风的发病率呢?\n白斑可以发生在每个人身上，男性一般爱面子，白斑出现后，他们失去了自信，不能在人们面前抬起头，自尊心受到了严重打击。那么，用什么方法可以降低男性白癜风的发病率呢?\n避免阳光刺激，一般男性外行活动较多，白癜风患者应注意在阳光强烈时不要外出活动，避免阳光照射。很多男性白斑患者不太重视防晒问题，什么都感觉不到，这种想法是错误的，实际上白癜风患者多发病是因为在太阳下晒的。\n重视皮肤的伤口，男性留胡子每天都会成长，所以剃须是男性每天都会做的。白斑患者必须注意刮胡子时不要受伤，刮胡子后可以选择刮胡子后的奶来放松皮肤，男性白斑患者的剃须也不需要那么频繁，一不小心就容易损伤皮肤，当然情况很严重。\n注意皮肤，洗脸一定要选择适合自己肤质的洗面奶。男性肤质比女性厚，油脂分泌多，保湿能力差，因此，男性患者需要选择适合自己的护肤水和护肤液，有利于皮肤的水分补给保湿，控制油脂，特别是对油性皮肤的白斑病患者来说，如果不能选择正确的洗面奶和护肤品，毛孔就容易堵塞，容易导致病情的严重恶化。\n白斑患者需要保持乐观的心情，白斑的预防工作也很重要，提前预防的话可以减少严重的伤害。\n上一篇：白癜风对青年人的人身有什么危害?\n下一篇：饮食如何辅助白癜风疾病的治疗?", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 70, "original_length": 3732, "line_ids": [8, 12, 13, 14, 15, 16, 21, 22], "language": "zh", "language_score": 0.96, "perplexity": 1504.5, "bucket": "middle"} +{"url": "http://cdbox3d.com/products_detail/productId=67.html", "date_download": "2022-05-19T07:55:03Z", "digest": "sha1:H3CXN3EGQOXZNGAWYJY5LUCFLSYPDQ46", "length": 262, "nlines": 9, "source_domain": "cdbox3d.com", "title": "无纺布口罩,山东华业无纺布有限公司官方网站", "raw_content": "cdbox3d.com\n产品名称： 无纺布口罩\n产品编号： 口罩用无纺布\n凭借我们庞大的专业知识和技能，我们都算其中的域的最值得信赖的供应商的生产和供应。我们提供的面罩卫生在我们高度发达的生产装置通过使高品位的优质面料和最新的机器使用而生产的。无纺布口罩我们提供的是人们所广泛使用在各个行业工作如建筑，采矿和铸造保护呼吸道。\n质地柔软\n完美修复\n销售部联系电话：13853306319 欢迎咨询\n相关标签：鞋套无纺布 PP无纺布\n热门城市：淄博口罩无纺布价格 山东口罩无纺布价格 临沂口罩无纺布价格 潍坊口罩无纺布价格", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 23, "original_length": 1373, "line_ids": [6, 8, 9, 11, 13, 15, 16, 17, 18], "language": "zh", "language_score": 0.99, "perplexity": 1875.7, "bucket": "middle"} +{"url": "http://fwulong.com/zixun/shichangqianyan/343.html", "date_download": "2022-05-19T09:24:28Z", "digest": "sha1:EQZ6N7WE5NUMGS5D5UX6IZ3DIAUYB7YX", "length": 1209, "nlines": 13, "source_domain": "fwulong.com", "title": "碰碰车、小火车、碰碰船", "raw_content": " 如何快速收回成本，游乐场投资诀窍\n如何快速收回成本，游乐场投资诀窍\n暑假快结束了，这段时间也成了孩子们暑期里最后的疯狂，不少家长带着孩子去游乐场游玩,不论是室内的还是户外的，大多都人山人海，这场游乐场大战从入夏拉开帷幕，一直持续了整个暑期。福龙游乐设备了解到，这个暑期，很多经销商都取得了颇丰的收入。\n目前，室内游乐场已成了大商场的标配，而小商超只要位置、面积允许，也纷纷推出了小型的淘气堡游乐场。因为，商家们早就意识到，孩子的钱最好赚，不仅项目赚钱，还可以吸引家长们拉动餐饮和其他零售商品的消费，市场潜力巨大。\n同时，也有商家拓展思路，将游乐设施的主题延伸至水上乐园主题。近日，山东某经销商的水上度假乐园开幕，各种水上乐园项目吸引大量市民。工作人员介绍，即便不是周末，早上10点开园时就有几十人排队等候了。\n投资十多万元 不到一年收回成本。孩子们的钱究竟有多好赚？李先生在一家商场里租了20平方米，搭建了一个碰碰车平台，由他提供碰碰车车，供顾客游玩。李先生说，遥控碰碰车的收费标准是10分钟15元，当初他投入这个项目，前期投入了十多万，经营了不到一年，投入就全收回来了。李先生很满意。\n在某商场里经营充气攀岩项目的张先生并不避讳自己在这里投了多个游乐项目。之所以会多项投资，原因当然是利润可观。他的游乐项目的选择以年龄段偏大的孩子为主，比如攀岩、碰碰车等。张先生说，大商场里的游乐项目几乎是旱涝保收：初中生的家长经常给钱，孩子可以自由支配。\n靠近游乐项目 小生意也能借光。其实，商家们角力室内游乐场背后的原因是带动周边经济。李女士在某商场的室内游乐区附近有一个零食摊位，她透露，档口是承包商场的，月租金5500元，而各项支出加在一起，一个月要1.2万元~1.3万元。这样算下来，每天至少要卖出400元才能合上成本。但李女士表示，周围20几个摊位，大多数都赚钱。尤其是周末和寒暑假，更是最赚钱的时候，暑假的营业额和平时的月份相比，至少是五倍甚至更多\n提醒：投资游乐场 场地很重要\n室内游乐场多数可以与商场合作，个人投资前一定要多参观运营比较成功的游乐场，然后拟定自己的场地面积、面向的客户群以及游乐场的主题等，再寻找生产厂家。选择合适的场地至关重要，是日后能否赚钱的前提。选择场地时，投资者要清楚场地的面积和平面图等，才可能找到最好地段，以保证客流量。\n建议：小型游乐项目也有赚头\n对于初级投资者来说，从大商场起步或许有些困难。一些初级投资者将眼光放在了大型超市或高档小区内的淘气堡等小型游乐项目。比如淘气堡是专门针对低龄儿童玩耍的游乐项目，小型游乐项目无疑胜在了租金低、投入小，回本快。但利润丰厚的关键因素，还是经营者选址聪明。\n适合经营室内淘气堡的场地有几种，综合性购物商场，超市型的购物商场，都是人流大客源充足的地方。高档小区附近也是一个尚好选择；另外就是大型广场或者公园。", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 88, "original_length": 5470, "line_ids": [60, 61, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73], "language": "zh", "language_score": 0.97, "perplexity": 1466.8, "bucket": "middle"} +{"url": "http://gdasst.cn/index.php?g=home&m=article&a=show&id=59", "date_download": "2022-05-19T07:57:02Z", "digest": "sha1:KGG7DWFRAW72VK3AGDIIBSATTW3IGYFX", "length": 825, "nlines": 9, "source_domain": "gdasst.cn", "title": "广东省老科学技术工作者协会", "raw_content": "广东省老科学技术工作者协会 2017年“科技服务专项”申报指南\n根据广东省政府财政厅下达我会的科技服务专项经费的要求，为更好地体现用款性质，发挥经费的最大效益，决定设立“广东省老科协科技服务专项”，以突出工作重点，突出工作效果，调动和支持协会会员特别是团体会员参加科技服务的积极性。\n今年主要支持的六个方向上的科技服务活动：科普服务与活动、科技咨询与成果推广、科技服务站和基地建设、医疗下乡、教育培训及扶贫和建言献策等；重点支持：地市县、研究院所、大学等团体会员、专业委员会专业组和有条件的会员小组以团队方式承担项目。\n“广东省老科协科技服务专项”采用自愿申报、专家评审、协会领导班子择优支持的方式决定。凡申报者，需要填写协会的项目申报表（见附表），满足“五有”要求，即有负责人、有题目、有预算、实施计划、有效果指标。\n项目经费额度根据申报的项目类型和预计完成的效果指标分为5000元至8万元不等，项目实施的时间为2017年8月至11月。由于经费7月份到账，又正值协会换届，作为第一年的项目申报，我们对申报的课题项目的金额、单位承担的项目数没有限制，由申报人如实提出，由专家评审。\n项目负责人应当是副高及以上职称，且每人只参加一个项目。凡已承担省市级项目的负责人尚未结题的今年暂不受理。\n83564964。\n申报的项目经协会聘请专家委员会相关专家评审并经协会领导班子办公会审核后确定是否立项支持。获准立项的项目，协会将与该单位法人代表及课题负责人签订项目合同，并将经费拨付到该单位。\n协会要求凡承担项目的单位和团体，都要严格按照广东省科技项目经费管理的有关规定，管理项目经费，按项目申报书的实施计划完成预定目标。协会将在2017年底前组织专家验收。", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 96, "original_length": 5359, "line_ids": [31, 33, 34, 35, 36, 37, 38, 39, 40], "language": "zh", "language_score": 0.98, "perplexity": 1683.3, "bucket": "middle"} +{"url": "http://gehbm.com/product/list/type_id-14.html", "date_download": "2022-05-19T08:07:27Z", "digest": "sha1:BCFZMOBI35JE7TOZ3GXDCZ5IDVN5RBQ4", "length": 2059, "nlines": 30, "source_domain": "gehbm.com", "title": " HBM称重传感器日本NMB美国TEDEA测量专家", "raw_content": "德国HBM\n日本NMB\n称重测力传感器CFC200系列 安锐 不详 洽询 48小时内\n简要说明：CFC200系列称重测力传感器适用于拉压方向的力,可以进行静态或动态测量,并保持高精度, 传感器采用不锈钢材质制成,适合在恶劣条件下使用,传感器无需维护,可安装在难触及的位置.\n柱式拉力传感器U50系列 安锐 不详 洽询 48小时内\n简要说明：U50系列柱式拉力传感器可用于拉、压力值的测量，输出对称性好，抗偏载能力强。适用于各种配料秤、吊钩秤及各类专用秤等。可选择内置式变送器，标准信号0～10mA、4～20mA或0～5V输出。\n轮辐式称重传感器C1W1M 日本NMB 不详 洽询 48小时内\n简要说明：结构特点: 合金钢 轮辐式称重传感器 额定载荷: 6T,8T,15T,20T,34T,50T 应用领域: 该产品体积小，重量轻，具有过载保护功能。 功能特点: 通孔式，特别适用于注塑机。\n带有 TEDS 高精度力传感器Z30A 德国HBM 不详 洽询 48小时内\n 带有 TEDS 高精度力传感器 最高精度Z30A力传感器是低量程范围内最高精度 更精确,更灵活: Z30A 高精度力传感器的量程范围为 0...50N 到 0...10kN. TEDS, 内置于 Z30A\n力传感器Z30 德国HBM 不详 洽询 48小时内\n 低量程的最高精度 更精确，更富有想象力，更灵活：Z30精度力传感器的测量范围为0...50Nto 0...10kN 。典型的HBM高端设备，属于00设备精度，已获遵\n力传感器U93 德国HBM 不详 洽询 48小时内\n位移曲线监控, 压\n力传感器U5 德国HBM 不详 洽询 48小时内\n 力传感器 新的，平板式，可瞬间安装完成，容易使用工业力传感器 U5力传感器有许多优点：优异的测量品质和安装极为容易。例如：超过50%可允许的横向力限制和简单快速地对用户系统结构的适应\n简要说明：通过环型对称结构, U3 拉/压向 力传感器可以消除侧向力的影响. 此外 U3 非常结实,吸收了法兰适配器的很多优点,设计用于恶劣的工业环境下.如压配等\n力传感器U1A 德国HBM 不详 洽询 48小时内\n 力传感器 U1A 力传感器可以测量 静态和动态拉向和压向力, 可以应用在测试设备和其他方面 . U1A 拉/压式 小量程 内置过载保护 量程: 10, 20, 50 N; 应变式原理; 精度: 0.\n高精度力传感器U15 德国HBM 不详 洽询 48小时内\n 明智的选择: U15 高精度力传感器可以用来进行工业标定和研究. 非常精确,结实… 极高的性价比比. U15 高精度力传感器可以用来进行工业标定和研究. 例如: 工业场合下,进行可跟踪的力标定 作为标定系统参考 材料测试机分类.\n拉力传感器S9 德国HBM 不详 洽询 48小时内\n 力传感器 HBM 的S9 力传感器 有广泛的应用: 包括材料测试,功能性元件测试和生产监控.S 型力传感器 有5个量程,从 0…20 N 最大到 0…50 kN. 每种型号都有相同的尺寸和外型, 使他\n 力传感器 HBM 的S2 力传感器 有广泛的应用: 包括材料测试,功能性元件测试和生产监控.S 型力传感器 有6个量程,从 0…20 N 最大到 0…1000 N. 每种型号都有相同的尺寸和外型, 使他\n压电力测量链CMC 德国HBM 不详 洽询 48小时内\n 来自 HBM 高动态的力学测量技术，采用压电\n标准测力环KMR 德国HBM 不详 洽询 48小时内\n Force washer KMR force washers 专门用于静态和动态力测量, 特别是进行力监控. 其经常用于食品工业.IP67 保护等级 使其可以用在室外.电源标准： 标准配置： 可选附\n测力机标定传感器KD/STZ/KDB 德国HBM 不详 洽询 48小时内\n 测力机标定传感器 KD（压缩汽缸）和STZ（张力棒）的特殊传感器是按照EN 10002-3 或 ISO 376e几何要求设计的。按照ISO 7500-1标准，其最大的整体高度适合于材料测试器械的测试", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 95, "original_length": 7028, "line_ids": [19, 20, 46, 47, 49, 50, 52, 53, 55, 56, 58, 59, 61, 62, 64, 65, 68, 70, 71, 73, 74, 76, 77, 80, 82, 83, 85, 86, 88, 89], "language": "zh", "language_score": 0.99, "perplexity": 1603.7, "bucket": "middle"} +{"url": "http://changsha.haierd.com/qijiad/69.html", "date_download": "2022-05-19T09:44:25Z", "digest": "sha1:N7NVW7WZ5Y5YIJD6V4F74TTDNKUQ4MCQ", "length": 417, "nlines": 9, "source_domain": "changsha.haierd.com", "title": " 海尔冰箱清洁_长沙海尔维修站", "raw_content": "0 发布时间：2021.04.17\n如何保持冰箱清洁\n1、不要什么都放进冰箱。买回来的蔬菜要先做筛选再处理，那些不适合放在冰箱的蔬菜，如萝卜、土豆等就不要放冰箱了。\n2、生肉先清洗：鲜肉买回后，先用清水稍加冲洗，晾一会儿后再装进保鲜盒或保鲜袋中，放入冷冻室。\n3、分区摆放：为了食物的清洁卫生和防止交叉感染，食物应分区摆放。冷藏室中，蔬果放下面，熟肉、奶制品放上面，中间一层可以放剩菜、剩饭。\n4、善用保鲜盒和保鲜袋：用保鲜袋和保鲜盒能一定程度上加强食物的密封性，防止被食物被细菌污染。另外，即便用了保鲜盒和保鲜袋，食物也不能长时间存放在冰箱，否则一样会变质。\n5、定期清洁：应每周给冰箱进行一次大扫除。大扫除时应先切断电源，用软布蘸上清水或洗洁精轻轻擦洗，然后蘸清水将洗洁精拭去。在发现冰箱零件积垢时，应拆下用清水或洗洁精清洗。\n本文为长沙海尔冰箱售后整理编辑，希望可以帮助大家！\n上一篇：冰箱有臭味怎么办？冰箱异味处理方法", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 58, "original_length": 2155, "line_ids": [39, 40, 41, 42, 43, 44, 45, 46, 47], "language": "zh", "language_score": 0.94, "perplexity": 1603.2, 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"湿疹的临床症状具体有哪些\n湿疹的临床症状具体有哪些？说到湿疹，相信大家都是比较熟悉的，其作为常见的皮肤疾病，在日常生活中是比较多见的。长春皮肤病医院专家指出：很多人都无法区分皮炎和湿疹，要想避免湿疹与皮炎这样的疾病混淆，就必须在日常生活中去充分了解湿疹的临床症状。\n为了能让大家更加全面的了解湿疹的临床症状具体有哪些，下面由长春皮肤病医院专家为大家做详细的解答，希望能够帮助到大家：\n1、急性湿疹：急性湿疹在出现后一般会有一个或者多个大红色的丘疹、丘疱疹或者水疱出现，伴随着不同程度的糜烂、流分泌物等。\n2、亚急性湿疹：该类型的湿疹多由急性湿疹治疗不及时发展而来，其症状会在急性湿疹的基础上有所缓和，多表现为丘疹自愈，出现鳞屑、结痂等情况。\n3、慢性湿疹：慢性湿疹在病发后出现的皮损表现为暗红色或者棕红色，并伴随着苔藓样的鳞屑出现，在少数情况下可急性发作。\n解决湿疹的困扰\n“生物免疫分型抗敏疗法”是一种特异性免疫生物渗透的治疗方法，并经反复临床皮肤试验得来的，充分融汇了国际前沿的分子生物学、细胞学、免疫学等最新科研成果。该疗法在打破传统治疗模式的基础上，成功地运用生物的特效增加人体免疫的功能，并通过国家卫生部全国卫生科研规划工作调研统计结果，“免疫脱敏生物渗透疗法”获得权威医疗机构授权鉴定，被列入国家卫生部推广技术项目。\n长春博润皮肤病医院专家指出：湿疹可分为急性、亚急性、和慢性，湿疹的临床症状也会有所不同，但是，若一旦发现患有湿疹，请立即治疗，否则湿疹具有一定的遗传性，甚至会遗传给下一代。\n上一篇：上一篇：湿疹的治疗原则有哪些\n下一篇：下一篇：湿疹最初的表现症状有哪些", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 31, "original_length": 2697, "line_ids": [11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22], "language": "zh", "language_score": 0.96, "perplexity": 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陈亮\n5.6G镜头的D80套机，售价有了小幅度的调整，现在的最新价格是7088元最近一段时间，市场上热销单反相机的售价不断下调，由这个大趋势来看D80套机跌破七千元大关也是指日可待了。\n点击图片查看尼康D80详细资料\nD80采用了和D200一样的11点自动对焦系统，这也就意味着D80拥有同D200相同的对焦精度和机身驱动力。D80的开机时间为0.18秒，快门时滞大约80毫秒。可进行每秒3张连拍、最多可以拍摄100张JPEG或最多6张RAW格式的照片。当然为了同D200拉开差距，像机身性能、连拍、机内优化、快门范围以及对AI手动镜头的支持等方面，D80都进行了“缩水”。\n编辑观点：\n尼康D80拥有不俗的性能、出色的成像质量、良好的机身手感和便捷的操作，非常值得第一次购买单反的消费者选购。随着佳能和索尼的入门级新单反上市，尼康也一定会通过降价来增加D80的竞争优势，喜欢这款单反的朋友，不妨持币观望一段时间按，等其套机跌破七千以后再出手。\n尼康 D80套机\n82672760、82675803\n28 16:39\n28 16:31\n28 15:22\n28 11:32\n28 11:16\n28 08:33\nAPS-C画幅单反 尼康D200单机仅7880元 2008-04-28 08:03\n单反市场硝烟正浓 尼康D60套机仅4670 2008-04-28 07:50\n陈亮的其他文章\n五一出游好选择 热门卡片式数码相机推荐 2008-04-28 05:21\n全画幅单反 佳能1Ds MarkIII售53800 2008-04-28 05:20\n双卡双电多媒体卡片机 三星i8仅1580元 2008-04-28 05:20\n7X光变还带28mm广角 理光R7送卡售1880 2008-04-28 05:20\n800万像素卡片机 佳能IXUS80IS仅2000 2008-04-25 05:20\n800万像素6X光变防抖 佳能A720不到1500 2008-04-25 05:20\n28mm广角10倍光变 松下TZ15上市售2630 2008-04-25 05:20\n800万像素影音卡片机 三星i8仅售1490元 2008-04-25 05:20\n28日百款相机报价单：顶级单反售价狂降 2008-04-28 07:26", 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澳洲幸运8网榜单\n澳洲幸运8人工计划软件法孚莱机电科技发展有限公司是化工机械的专业生产厂商，公司以科技为主导，致力于高新技术的应用，主要从事涂料、油墨、精细化工及相关领域的机电一体化设备和工业自动化控制的研发、设计、生产与销售。我们秉承“解决方案的提供者，企业建设的服务者”的经营与服务理念，已经为涂料、油墨、印染助剂、液体洗涤剂、化妆品、水处理、制药、造纸等领域提供了众多的服务、技术和设备支持，行业涉及化工、核工业、冶金及电力等。如今我们以从单一的单机设备和另部件生产商，发展成为以乳化、分散与研磨加工为主要核心的工作领域的完全方案的供应商与服务者。我们最新的高效生产设备包括：搅拌设备、分散设备、高剪切分散乳化设备、篮式研磨设备、陶瓷分散研磨设备、涂料生产成套设备等。我们拥有出色的设计研究、生产、销售、客户服务队伍，可为您提供优化的设计方案、详尽的技术咨询、高品质的生产设备、高质量的设备安装调试和完善的售后服务。相信您能感受到我们的合作精神和专业水平，希望我们成为相互信赖的合作伙伴。法孚莱还提供全方位自动化控制解决方案，可在工业企业实现全自动、半自动、远程控制及管理。目前公司与美国AB、德国西门子、以及ABB、丹佛斯，建立了良好的合作关系。\n\n秒速赛车开奖结果网址\n64363889，13253438238，15238691497，巩义市丰瑞净水材料有限公司真诚期待与您的合作！\n秒速赛车在线预测\n澳洲幸运8一天多少期 澳洲幸运8网我公司常年生产轴承钢球钢锻，公司位于河北省邢台市临西县球磨机专用轴承钢球钢锻，耐磨不破损，为您节约成本，较低损耗。\n\n250MM都有库存.\n三忠工量刃具广佛批发部位于中国佛山南海广东五金城，三忠工量刃具广佛批发部是一家工具、量具、刃具等产品的经销批发的私营独资企业。三忠工量刃具广佛批发部经营的工具、量具、刃具畅销消费者市场。三忠工量刃具广佛批发部的产品在消费者当中享有较高的地位，公司与多家零售商和代理商建立了长期稳定的合作关系。三忠工量刃具广佛批发部经销的工具、量具、刃具品种齐全、价格合理。三忠工量刃具广佛批发部实力雄厚，重信用、守合同、保证产品质量，以多品种经营特色和薄利多销的原则，赢得了广大客户的信任。\nSG飞艇娱乐平台\n泰兴,水陆交通运输便捷。专注从事污水处理设备的研发、制造和销售。是现代给给排水工程、泵站、污水治理应用工程的优质配套企业。主要产品包括:粉碎型格栅除污机、机械格栅、粉碎拦污设备、搅拌、泥水分离；回转式鼓风机；一体化预制泵站等。江苏先沃机械坚持“以人为本、创新发展”的企业理念，为用户提供优良、可靠、经济的技术和服务,为振兴我国给排水事业、保护生态环境、造福子孙后代作出积极贡献，竭诚与各界朋友携手共创美好未来。\n极速飞艇开奖结果助手\n郑州金阳机床机械制造有限公司，是集机械设计、制造为一体的专业化企业，中国砖瓦协会会员，全国墙体材料科技信息网网员，公司内部高工、科技人员及多位骨干员工，均有20余年的砖瓦机械修造制造经验，技术力量雄厚，在吸收、消化国外先进技术的基础上，根据实际国情和砖瓦行业的发展趋势，加大科技开发力度，并聘用砖瓦行业技术专家多名，自行研制的“艺丰”牌砖瓦机械系列产品，已有20多个品种，70多个规格，产品畅销全国各地。“艺丰”牌砖瓦机械系列产品主要特点:一、设计独特，工艺先进，结构紧凑;二、齿轮转动系统均采用齿轮面技术;三、轴类、封密类零件均严格执行国家及行业标准；四、主机挤出主轴采用浮动轴技术，杜绝了砖机摇头的弊端病，为用户正常维护带来了很多方面。可满足国内外朋友不同层次的以煤矸石、粉煤灰、页岩、江河淤泥为原料的烧结砖企业的需求。主要产品有:球磨机_破碎机_选矿设备_制砂机_榨油机_木炭机_制砖设备_破碎设备_选矿设备_榨油、浸油设备。\n极速飞艇三期计划\n赛思特公司是以专业研发、生产气体增压，液体增压，及其相关的各种非标流体控制系统的专业制造商。主要产品有两大类：一类为气液增力缸及气液增力缸式冲压设备，其相关产品有螺栓螺母压装机、无铆钉连接机、压力装配机、压印字号机等冲压设备；另一类为气动增压泵及气动增压试验设备，其相关产品有空气增压机、氮气增压机、氧气增压机、压力容器爆破试验台、压力脉冲试验台、CNG汽车改装气密性试验装置及天然气瓶检测设备等。\n因为专业，所以完美\n极速飞艇开奖app\n河北龙亿达数控机床附件制造有限公司为开发、设计、销售于一体的大型合资企业（中德合资）。\n澳洲幸运8人工计划软件相关制作、供应榜单\n泊头市永来机械有限公司(总公司）\n南京帅特机械设备厂\n深圳市深量实业成立于1997年5月我们的产品包括：一、机床配.........\n湘潭市华汉精密铸造有限公司\n昆山腾飞达机电设备有限公司是一家专业致力于非标设备&工装设计.........\n茂名惠利自动化仪表有限公司\n我厂是专业生产“鑫华泽”牌手轮、把手、拉手、手柄、手柄球、密.........\n温州市创高电子仪器有限公司柳市分公司\n服务热线；15589221886滕州市姜屯镇鑫阳机床厂是古代.........\n深圳市臻裕鸿超声波设备有限公司\n沧州华品自动化科技有限公司是一家集设计、制造、服务于一体的专.........\n东莞华晨制冷设备工程有限公司\n创迪机电科技有限公司一家泳池设备、泳池水处理设备、水处理设备.........\n上海欧保环境科技有限公司\n上海蓝领数控科技有限公司\n上海新中冷暖设备有限公司是专业生产规格齐全的标准型、非标准型.........\n北京康铭致远科技有限公司\n（我们的承诺：全国配送，所提供的所有型号不同配件，如果质量有.........\n 澳洲幸运8人工计划软件\n澳洲幸运10在线预测\n极速赛车规则\n秒速赛车开奖网\n澳洲幸运8安卓手机软件\nSG赛车开奖官网\n澳洲幸运10历史结果\n澳洲10彩开奖\nSG赛车历史开奖数据\n澳洲5玩法\n秒速飞艇走势\n1 江本贸易（上海）有限公司 80231 2 南昌长江思科矿山机械有限公司 37675 3 深圳市金能华超声科技有限公司 47700 4 荥阳市恒力养殖机械厂 52126 5 深圳宝龙得机械设备有限公司 88555 6 枣强县瑞鸿玻璃钢防腐制品厂 36615 7 上海逗号机械设备有限公司 40163 8 开平市林科化工有限公司 79150 9 无锡意凯自动化技术有限公司 82153 10 河南舍尔宁机械设备有限公司 29082\n无锡埃费尔流体智控仪器有限公司|东莞市凤岗琦伟五金加工厂|北京斯霓瑞数控设备有限公司|东莞市凤岗利东包装专用设备经营部|廊坊盛达二手叉车销售回收公司|济南宏顺数控机械有限公司|东莞市盛菱精密机械有限公司|淄博永强工贸有限公司|深圳市达茂科技有限公司\n通信管理局 法律声明使用网站必读 澳洲幸运8人工计划软件 Co. 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"上海幕墙一级（沪苏）资质转让（硬性条件）专业从事\n第四十七条　信息公开工作主管部门应当加强对信息公开工作的日常指导和监督检查，对行政机关未按照要求开展信息公开工作的，予以督促整改或者通报批评，需要对负有责任的领导人员和直接责任人员追究责任的，依法向有权机关提出处理建议。\n的以来，对新型城镇化建设做出了一系列重大战略部署。官庆表示展望未来推动城镇化建设取得了长足发展。推动新型城镇化，首要的问题就是结合实际。，着力提高领导班子发现和解决自身问题的能力，不断提高认识，克服三种思想（即自我感觉良好的懈怠思想，摆不进去的消极思想，查找问题的错位思想），加强组织领导，把正风肃纪一抓到底。邵继江副总裁要求中建安装要聚焦解决职工群众反映强烈的问题他希望中建安装能够成为整风精神，解决问题，密切联系职工群众的示范单位，以第二批活动的事迹成效助推企业改革发展各项目标任务的实现。\nls6utgDbOeKZnI6L技术，施工，管理等角度。中国建筑学会建筑产业现代化发展委员会理事长叶浩文等出席开班仪式对装配式建筑工程各个环节的重点中国建筑股份有限公司副总工程师。标准从政策中建系统相关单位共150余人参加培训。培训班邀请了中国建筑科技与设计管理部，中国建筑学会建筑产业现代化发展委员会等单位的八位专家。", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 141, "original_length": 11793, "line_ids": [10, 94, 96, 99], "language": "zh", "language_score": 0.98, "perplexity": 1519.6, "bucket": "middle"} +{"url": "http://shantaoli.com/a/chanpinzhongxin/yidongapp/55.html", "date_download": "2022-05-19T09:49:37Z", "digest": 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"一流的产品经理必须要有“四种心”\n我觉得我是360最大的产品经理，当然，我认为这不是因为我有多么成功，而是因为我曾经是最大的失败者，曾经在用户体验上犯了非常巨大的错误，甚至被别人骂得狗血喷头。很多人看...\n我觉得我是360最大的产品经理，当然，我认为这不是因为我有多么成功，而是因为我曾经是最大的失败者，曾经在用户体验上犯了非常巨大的错误，甚至被别人骂得狗血喷头。很多人看到的是我投资和参与做的成功产品，但没有看到背后还有很多不成功的功能、不成功的产品，这些失败没有被大家所关注和记得。但正是这些经验教训，才帮助我做出更好的产品。我觉得要成为优秀的产品经理，还得有“四心”。\n一、用心：对自己对产品负责任\n大家可能会觉得我讲了一个特虚的“用心”，不是。大家会觉得我在产品上有一些心得，实话说每次做一个新产品，我也不是拿出几个锦囊，也不能三分钟就有灵感，我也花很多时间看同行的产品，去论坛看用户评论，然后也花很长时间用产品，每个产品都是要呕心沥血。有时候感觉做产品像是妈妈十月怀胎生孩子，就算你成功养育了三个孩子，第四个孩子不用十个月，三个月就生出来，可能吗?还是要经历十个月的痛苦孕育过程。我觉得用心，对自己负责任，对自己做的产品负责任，是一个产品经理的基本前提。\n事实上，很多产品经理应该自省，你们做产品真是为自己在做，还只是在执行老板的旨意或上级的命令，是真的在用心吗?\n很多人认为自己只是公司的一颗螺丝钉，甚至还不是。但如果把自己看得太小，只把自己看成一个打工仔，这样的层次和胸怀不可能成为一个真正能做好产品的产品经理。所以我希望产品经理们在公司上班的时候，也不用管公司是不是你自己的，拿出一点创业精神。很多人会说我又不是创业者，我干吗要创业精神。那换个方式思考，难道非要自己办公司才能把一个产品做好吗?在别人的平台上花着老板的钱，花着公司资源，做不成是公司交学费，这多好。如果在公司能把自己充分调动起来，把一个产品做到极致，让产品在市场获得成功，将可以给自己积累声望、人脉关系，当然也可能是积累经验教训，但即便是教训也能够为你下一次成功做铺垫。\n即便自己出去做一个公司，你真觉得你做产品的能力就有所提升、完全不一样吗?未必。我常跟产品经理讲，你心里要有一个大我，要对产品负责任，要把产品看成你自己的产品，我认为每个人都是有潜力的。很多产品经理也在努力做产品，但仍能挑出很多问题，事实上他也尽到了工作职责，仅仅靠尽到工作职责，很难成为优秀产品经理，还得真的用心。\n看到很多同事买房子，然后需要装修，他们迅速成了装修专家、瓷砖专家、马桶专家。为什么?因为这是他的房子，他每天花很多时间在网上搜索，每天到建材城和人斗智。所以只要拿出装修自己家的精神，一个外行能够成为瓷砖专家、浴缸专家。如果同样用心，没有理由不成为一个产品专家。\n二、同理心：从用户角度出发\n前面的“用心”是说大我，是比较自我，敢于承担。而将心比心讲的是小我、忘我、无我。做产品，无论有多好的技术、多好的设计给用户提供服务于用户体验，都要把握一个理论，即从用户角度出发。\n所谓用户体验，为什么不叫产品经理体验、老板体验，因为所有体验是从用户角度来看产品，产品经理觉得好的产品用户不一定买，用户选择一个产品的理由跟行业专家选择一个产品的理由，有的时候是大相径庭。用户选择产品有时候非常简单，如何学会从用户的角度出发思考，对很多人来说，说起来是一件很简单的事，但是实际上很难做到。因为每个人不管成功与否，随着经验增加、阅历提升，讲的最多的是什么?是我认为、我觉得，太自我，所以很多时候做产品，是给自己做。\n在产品讨论时，一旦出现激烈争论互不退步的局面，说明争论双方可能都没有站在用户角度，都是认为自己是对的，对方是错的。如何能够将心比心?心理学上有个词，叫同理心。从用户角度出发考虑问题，对很多人来说不是能力问题，是一个心态问题。原来我有一句话，教育过公司里的很多人：像小白用户一样去思考，思考完了得出结论，像专家一样采取行动。很多人颠倒过来了，像专家一样思考，像白痴一样采取行动。\n产品经理要能迅速进入白痴状态或者进入傻瓜模式，每个人应该有一个按纽，能够快速的进入傻瓜模式。我在公司里常常和产品经理们讨论产品，我对产品经理绝对是一个挑战，这是因为我能够这么多年被用户骂，能经常到第一线看用户的帖子，能在微博做用户的客服。这不是为了作秀，是为了保持真正掌握用户的想法，将心比心。\n通过不断的历练，现在我已经有两个我。公司产品经理做出一个产品给我试用的时候，技术出身的我，干了这么多年技术和产品，按钮和功能我很快就能熟悉了解，难不住我，还有那些写的很晦涩的说明书，我看一遍，稍微动脑筋一想就明白了。作为产品经理的我，用这些智能硬件会觉得很顺畅，没什么问题。但此时，第二个我，小白一样的我又会站出来：这个按钮的是做什么的、那个按钮是做什么的、为什么要按好几秒、说明书里的专业名词是什么意思……这个进入小白状态的我会发现体验有各式各样的问题，这就是站在普通用户层面去使用的。\n我做产品，至少有一半的灵感是来自用户，这不是说用户会具体告诉你一个产品应该怎么做。具体需求不能直接问用户，一个个用户具体需求不能听，否则会被用户牵着鼻子走。站在用户角度是说把自己置于用户情景中，去看用户为什么会这么想，用户为什么会这么抱怨，这个抱怨的根源是什么。一边是从用户出发的思维模式，一边是产品经理自我的想法，两种思维不断的在内心交战，不断自我挑战，使用户体验能找到最好的感觉。\n三、处处留心\n很多人觉得在公司工作、开产品讨论会时才是改善用户体验的时候，下班后这事就跟我没关系了，这种人很难成为优秀的产品经理。\n在优秀产品经理眼中，产品体验无处不在，任何事情都是产品体验。比如坐航空公司的飞机，登机过程、机场安检流程等都是体验。再如医院里，处处皆体验，在传统医院的流程中，永远不知道到哪儿划价、交费、拍片子……会让你楼上楼下跑很多来回，这是最坏的体验。\n在日常生活中，如果把自己当成一个抱怨的用户处处留心，然后把它再上升一个层次，抱怨完了之后，想想为什么会抱怨，该怎么改善，从而完成一个头脑体操，思考的过程，我觉得就是提升自己对体验的感觉。\n产品经理去买车，因为对车不了解，就需要听推销员天花乱坠的讲。产品经理可能并不关心车的某个螺丝是什么做的，但推销员依然会讲。反过来，产品经理打造自己产品的时候，巴不得把所有技术细节都展现给用户，也不管用户懂不懂、用不用得上。\n再比如买家电，你们真正懂家电的技术吗?很多人因为家电长得好看，或者现场推销员一顿忽悠就把电视买回家了。现在电视有非常多的功能，但回家实际用的还是音量键、开关键和频道键，摇控器上大部分键甚至都没摸过。现在的电视机还有看照片的功能，你们是否把 SD 卡往里插过?如果插过，你就会知道这个功能不是给人设计的。\n这些都是生活中的点滴，之所以鼓励大家在日常生活中、不熟悉的领域处处留心，是因为这是发现用户感受、培养同理心的一个非常好的机会。如果在日常生活中，不仅仅是上班那几个小时，或者开会那几十分钟里，能让自己无处不在的处于小白用户模式下，能让自己在生活中发现体验不好的地方，久而久之，对产品用户体验的感觉就提升了。\n四、没心没肺：脸皮要厚，不要怕人骂\n最好的产品不是完美的，是优美的，是优雅的，能解决用户问题的，但一定不完美。苹果的产品也有很多缺点，但是有一点或者几点能够给你带来强大的诱惑和感动，这就够了。\n很多设计师做事要求完美，我做产品要求做到极致，但不是完美，完美不可能。产品经理要有开放的胸怀，能够听到别人骂。对我来说，竞争对手雇水军来骂我，虽然很难听，我会咬着牙跟团队说说想想产品有什么改进的，让他骂不出。很多设计师出身的产品经理，有一颗玻璃心，被老板一批评就蔫了，被同行一挑战，被用户一吐槽，就说我不跟你讨论了，你不懂。\n我觉得做一个好的产品经理，要对产品的结果负责，心要粗糙一点，要迟钝一点，不要管别人怎么说，要能够经受失败。因为好的产品，都是经过不断的失败，不断的打磨。好的体验绝对不是一次到位，你要不断的一点一滴的去改进。\n当你们去谈论苹果的时候，谈论成功公司产品的时候，一定不要看今天的成功去模仿，一定看他们刚起步的时候原型多么粗糙。读乔布斯传，看苹果的真实历史，第一代 iPod 很粗糙，第一代苹果手机跟摩托罗拉合作还没成功呢。\n所以每一个产品最后能成功，都不是一招制敌，更不是一炮而红，而是至少经过三年五年不间断的打磨、不间断的尝试、不间断的失败。没有坚韧不拔的心态，产品经理很难做出来。做产品某种角度来说，是做艺术品，但某种角度来说，因为艺术品可以给少数人看，甚至可以孤芳自赏，但是最终的产品成功还是要获得商业上的成功，还是要获得大众的认同，不得不去跟大众沟通，跟市场抗争，跟竞争对手做竞争。\n产品经理应该能够忍受来自市场各种用户建议、正常的反馈，甚至包括恶毒的攻击。所以得有一颗粗糙的心，有时候我也觉得自己没心没肺的，骂我的人多了，刚开始有感触，后来看多了就习惯了。\n上一篇：营销心理学：营销就是把人性付于实践\n下一篇：你所认为的用户，是属于你的真正用户吗？", "cc_segment": 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"冯国明董事长等集团领导赴富阳水务公司督查调研\n18\n3月9日上午，市城投集团党委书记、董事长冯国明带队赴富阳水务公司督查调研一体化改革融杭发展工作，党委副书记、副董事长、总经理李红良，党委委员、副总经理、市水务集团党委书记、董事长赵志仁，集团公司办公室及市水务集团负责人参加。\n冯国明董事长一行现场调研了富阳水务公司江北水厂，详细了解了富阳区供排水及雨水处理现状，听取了富阳区供排水基础设施建设规划的汇报。\n调研会上，冯国明董事长听取了富阳水务公司一体化改革以来取得的成果以及今年工作思路。他指出，富阳水务公司一体化融合发展所付出的努力和取得的成绩可以用“充分肯定，再接再厉”来概括。一是平稳有序实现了一体化改革，确保工作有序，队伍稳定；二是去年“五个新”工作总结和今年“三个强”工作目标思路清晰，部署全面；三是新的富阳水务班子带领全体干部员工立足富阳，努力奋斗，展现了新的作为。对下一步工作，他提出要“充分清醒，任重道远”，他强调，富阳水务公司管辖范围广，服务人口多，担负的使命和责任是重大的，要严格履行好“五个再”要求：一是一体化意义和认识再提高。充分理解水务一体化改革的意义，发挥行业管控优势和地方政府优势，借助市水务集团先进技术、经验和地方政府的支持，切实提高专业化、科学化管理以及服务水平。二是三个“第一”意识再提高。要充分把握“党建是第一责任，保障是第一职能，发展是第一要务”的要求，切实抓好党的建设，履行好社会保障责任，坚持社会效率、经济效益两个效益最大化。三是对标一流，服务富阳再深化。在实践中不断深化管控水平、队伍建设、服务水平等全方位管理水平，更好地服务富阳政府和富阳百姓。四是“三个年”建设再落实。要全面贯彻落实市城投集团“三个年”专项行动的工作要求，今年尤其要突出管理深化年的目标任务，强化过程管控、全过程安全管理、全员综合素质提高，静下心来、沉下身去扎实推进降本增效等全面管理工作。五是团队建设再加强。要贵在坚持、要在自信、重在规矩，在困难面前坚定信念，通过学习增强过硬本领，提高规矩红线意识，一切为公，勇于担当。\n李红良总经理则指出富阳水务公司要在一体化平稳发展的基础上找短板、寻举措、谋发展，他提出四点要求：一是要做好城乡一体化工作。农饮农污要做好规划，以高标准、高要求、适度超前的原则做好保障工作。二是要强化基础管理。以目标和结果为导向，建立考核机制，增强成本意识，降本增效，确保精细化管理贯穿始终。三是要争取属地政府支持。立足富阳、服务富阳，积极争取富阳区委区政府相关部门的政策支持，为又好又快发展创造良好的外部环境。四是要深化“三业”研究。加强主业、副业和新业的统筹发展，强化与市水务集团及下属企业的深度融合。\n当天下午，党委委员、副总经理，市水务集团党委书记、董事长赵志仁召集富阳水务公司班子成员召开会议，对冯国明董事长和李红良总经理的重要讲话精神进行专题部署和落实。他对上午的专题调研情况进行了复盘，并对富阳水务公司今年“三个年”建设工作提出了五方面要求：一是工程建设标准要提高；二是降本增效管理要提标；三是特许经营协议执行要落地；四是政府政策保障要争取；五是发展战略目标要锚定。", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 185, "original_length": 7966, "line_ids": [81, 83, 85, 86, 87, 88, 89], "language": "zh", "language_score": 0.97, "perplexity": 1551.2, "bucket": "middle"} +{"url": "http://www.bjtop1.com/a/yigui/1239.html", "date_download": "2022-05-19T09:34:09Z", "digest": "sha1:LY5E2REFCMZTKRDOUZZI3VXZBE4RQKIU", "length": 3346, "nlines": 28, "source_domain": "www.bjtop1.com", "title": " 德邦物流能否成功代替快递巨头顺丰？【欧宝体育app】_欧宝体育app", "raw_content": "德邦物流能否成功代替快递巨头顺丰？【欧宝体育app】\n回应：过去的两三年，我们显然在筹划租车业务。 今年10月底不会在一些区域和城市上线，但只是小范围试点。创立于1996年的德邦从航空货运起家，一路走过也做到过物流行业里不少细分领域，还包括小件租车，但**惜自由选择了国内公路零担运输业务。\n今年的双11租车争夺战预见更为惨重。一些外来户也重新加入战团。\n回应：过去的两三年，我们显然在筹划租车业务。\n今年10月底不会在一些区域和城市上线，但只是小范围试点。创立于1996年的德邦从航空货运起家，一路走过也做到过物流行业里不少细分领域，还包括小件租车，但**惜自由选择了国内公路零担运输业务。截至2013年8月，德邦已开办直营网点 3700多家，自有营运车辆6600余台，全国运输中心总面积多达88万平方米，被称作物流业顺丰。\n如同顺丰被看做是中国的FedEx，德邦则被视作**有潜力沦为中国版UPS的企业。自2012年初获得国内租车经营资格后，德邦何时启动租车业务之后备受瞩目。\n虽然德邦拒绝接受透露还包括明确上线日期、市场和客户定位，其租车业务否仍使用德邦名称或者创立新的品牌等更加详尽的信息，甚至其董事长兼任总经理崔维星也故意维持高调。秦伟说道：我们并不期望被过多注目。\n据德邦物流潍坊分公司一位期望电子邮件的员工对记者透漏，德邦总部自2011年起就开始研究租车业务。现在租车的组织架构刚创建，先期可能会自由选择北京、广州等几个大城市开始运作，专心领域这个说道不许，但据我推断，应当是电商(仓储)。\n但这预见会是坦途。事实上，京东、凡客等电商网站自辟物流并开始对外接续业务，而顺丰、宅急送、申通等租车企业也早已跨界至电商领域。\n，一般来说，快运是在全国创建起干线网络，根据客户市场需求在重点城市设置少量货物集散地，点对点，服务企业居多，客户到其网点奈何。租车则是门对门，以小件、较慢按时、便利查找、服务个人等为主要特征。如果把物流比作一条河的主干道，租车就是伸延出有的流向田间地头的一条条小溪流。\n，公司经营租车业务必需办理租车经营许可申请人。\n。举例来讲，京东商城是在2012年6月获得租车业务**后才开接外单。\n物流与租车虽然区分具体，租车企业一般也同时与多家物流货运公司合作，但实际早在德邦之前，两者有数互相跨界和融合的趋势。**典型的例子就是顺丰早就开始尝试公路零担物流和冷链物流。中国物流市场这两年发展十分慢，很多模式还没定型，又有新的机会，所以很多企业都在做到一些跨界的探寻。\n戴定一说。他指出，如同京东自辟物流如果只为自己服务必定亏损一样，顺丰的零担物流某种程度也不应伸延向其他租车同行服务才能盈利。顺丰的越界威胁、电子商务的热潮，也许是德邦启动租车业务的原因。但在物流界知名人士、原传化公路港物流集团CEO徐水波显然，德邦做到租车**主要原因不应是重视租车比快运更高的利润率。\n众所周知的事实是，快运是做到大件货物(零担是比较整车而言，指一次托运、计费重量严重不足整车货量的货物)，按重量收费，利润率比较平稳。而租车则是按件收费，利润率比较较高。据理解，德邦自2010年早就启动上市计划。\n另有知情人士透漏，德邦自由选择在此时才释放出做到租车的消息，原本就是因应IPO时间表来打的组合拳，只是如今国内IPO未有开闸重新启动。德邦做到租车有一个较为不利的条件，就是有较为多的直营门店，可以像邮局一样来揽货。徐水波指出德邦从以B2B居多的公路零担物流，转型尝试B2C的租车业务，符合国际趋势。从国际上来看，物流和租车企业的交叉融合是一种趋势，就像UPS也做到租车、FedEx也做到零担一样，彼此都在各自注重擅长于的基础上有交叉。\n实质上，德邦为进占租车的确筹划已幸，大大减少网点，甚至像顺丰一样了解社区布点即为其典型动作。根据某物流集团前任副总裁MJ勺子在知乎网站上取得的数据，2009年10月，德邦有大约539个营业网点; 到2010年9月中期，数量激增到982个;在2011年11月，多达1700个;此时此刻，3842个。\n在他显然，租车和零担是两个相近度很高的细分行业，都有网络化特征、获取标准化的服务、不具备可拷贝性，而这也是德邦做到租车的优势。协作除减少网点外，德邦还请求麦当劳的门店设计者、全球**的品牌策略顾问与设计公司美国朗涛设计顾问公司(Landor Associates)老大其设计全新VI识别系统，并在去年已完成替换。秦伟还向记者证实，德邦进占租车还取得了IBM、麦肯锡和安永三家咨询公司的协助。\n德邦内部文件表明，2013年9月16日，德邦物流租车业务启动不会在其上海总部举办，崔维星在会上不作了关于进占租车业务的动员讲话：中国的租车行业每年快速增长30%至40%，电商的发展也十分慢。而目前做到电商业务的租车企业主要以加盟居多，顺丰是直营但是电商业务不多，所以，直营化管理较为好的德邦如果转入租车领域，我们认同可以为客户获取比加盟租车商更佳的服务，所以我们自由选择进占租车。\n事实上，早在2002年，德邦物流之后创建了自己的网站，其想法只是可供客户货物查找。但在后期的市场调查中找到，客户通过网络搜寻联系到德邦委托货运的需求量明显快速增长。2008年5月，德邦开始试运营网上营业厅。到次年8月底，其网上营业厅收益总计超过1.1亿元，所占到收益比例快速增长到23%左右。\n2010年4月，德邦物流月沦为阿里巴巴集团B2B业务的引荐物流商，这也是阿里巴巴B2B业务**月制订第三方物流企业。从快运横跨到租车，从B2B到B2C，看起来只有一步之遥，但也绝非易事。\n戴定一回应，快运是做到大件货物，其网络产于是根据客户确认;而租车则是根据概率，末端的布局是放松的，没特定的客户群。德邦虽然仍然是以服务中小企业居多，但这两者之间有多少交叉重合却尚待而闻。\n说道。他指出，德邦的租车业务应当会回头顺丰这条路，回头四通一达的路子可能性更大一点，但这一切有待于德邦的租车业务月上线后才能尘埃落定。\n据理解，在人员配备方面，德邦很早以前之前就在公司内部发布命令了做到租车的通报，统一对公司不愿专门从事租车业务的零担业务一线员工，展开了租车员从业资格证书的培训，其租车业务上线后即展开月的业务转岗。同时，部分营业部将留出部分仓储空间做到租车用。物流和租车说道融合还一挺无以的，流程不一样，操作者模式也不一样。\n德邦做到租车不能说道有一些优势，但却是是造一块大业务，不经过伤筋动骨是很难去转变的。秦伟坦白道。因为市场的较慢发展，大家都在做到对自己不利的事，但成熟期的模式似乎还没显出。\n戴定一指出，像顺丰、德邦这样互相伸延到对方领域，以及像京东、凡客等一些电商网站自辟物流，在目前阶段都是长时间现象。但从将来来看，随着物流市场的渐渐成熟期，认同不会有社会化分工经常出现，就是每家企业还是要探讨于其核心竞争力，并渐趋分工和合作，分给粗，必定预示着合得凸，只有这样才能定型地发展。\n本文关键词：欧宝体育app,德邦,物流,能否,成功,代替,快递,巨头,顺丰,【\n上一篇：智慧物流迈入“无人”时代【欧宝体育app】", 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"如何添加聚丙烯酰胺效果更胜一筹\n目前，污水也分为很多种，在处理一些高浊度废水时，添加聚丙烯酰胺的时候需要分批投加，但是大多数客户并不了解为什么要分批投加聚丙烯酰胺PAM？究竟有何好处呢？下面就由河南佰科水处理药剂厂为大家说明一下吧！\n分批添加PAM有什么好处呢？一般处理高浊废水，分批投加絮凝剂比一次性加完药剂可以达到的效果好很多，如以浑液面沉速为比较值，则前者为后者的3倍多！\n所谓分批添加聚丙烯酰胺，就是将投药量分成两部分分别投加于水中，先加入一部分絮凝剂后使之与水迅速混合，相隔1～2MIN后，加入另一部分絮凝剂，再与水迅速混合。由于分批投药能避免过高的絮凝剂浓度与泥沙结合，造成活性基团被封闭的后果，因而可达到较佳的效果。分批投药的比例，一般先投加60%，然后再投加40%为佳。\n聚丙烯酰胺有很多地方是需要注意的，只有了解了源水，了解了产品，才会给客户带来较高的效率。河南佰科水处理药剂厂为客户生产供应大批量净水剂：聚合氯化铝，阴离子聚丙烯酰胺，聚合硫酸铁，液体三氯化铁等高效水处理药剂，多年来，我们一直以客户需求为依据，为客户生产供应出较合适的产品，以较合适的价格，促成较长久的合作！聚丙烯酰胺厂家较新咨询热线：13838033648 13838033648\n前一篇：佰科公司聚合氯化铝符合国家标准\n后一篇：聚丙烯酰胺投加量仍是影响效果的根本", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 33, "original_length": 2546, "line_ids": [24, 25, 26, 27, 28, 29, 30], "language": "zh", "language_score": 0.96, "perplexity": 1685.9, "bucket": "middle"} +{"url": "http://www.bkzhishi.com/sh/3004.html", "date_download": "2022-05-19T08:43:48Z", "digest": "sha1:CTEXVVBXHVYKAK5QILJYV4LTUJYSFMET", "length": 824, "nlines": 20, "source_domain": "www.bkzhishi.com", "title": " 知识生活窍门网", "raw_content": " 眼睛白眼球有黄斑怎么办，如何预防眼睛黄斑病变\n眼睛白眼球有黄斑怎么办，如何预防眼睛黄斑病变\n如果是位于眼白上可能是结膜的脂肪沉着，不需要治疗。位于眼白和黑眼珠之间可能是睑裂斑，为风尘刺激、紫外线照射过多引起，也不需要处理。用眼疲劳容易造成眼部结膜充血、眼干涉、疲劳性眼痛等症状，你需要保持充足的休息时间，勿过度操劳。方法\n看电视、玩电脑等因为都有辐射，所以时间不宜太久。另外用眼过度会造成眼睛疲劳，引起近视患者的屈光度数加深。平常可以多做眼球运动，例如：上下转动、左顾右盼，可放松眼部肌肉。热敷也很有效，可快速消除眼部疲劳。亦可将两手掌摩擦至热，然后贴盖在眼部，促进眼部血液循环。\n2、在饮食方面，可适当进行食补：猪、牛、羊、鸡等动物的肉类;海参、黄鱼、鳝鱼等鱼类;猪肝、鸡肝等动物肝脏;其他如的如桂圆、荔枝、胡萝卜、芹菜、山药、芝麻、枸杞、菊花、决明子等等。并且要少吃葱、蒜、韭菜、胡椒、酒、辣椒等辛辣食物。\n3、对于眼睛白眼球上有黄斑这个问题我们大家应该知道了，在生活中人们都要很好的保护自己，身边的这些疾病对于我们大家来说是非常的有危害的，我们大家要更加好的保护自己，给眼睛洗个澡，黛羽眼球SPA，眼睛白眼球上有黄斑的人一定要及早的治疗。\n高校毕业除了公务员考试、考研和工作还有什么更好的选择\n京东云服务器实例模板怎么删除\nX4：基石前中期贸易心得分享\nExcel文字与数字分离\n京东38节什么时候开始报名\n迄今为止你最讨厌的广告是什么\n现在电脑技术什么字体都可以打印出来练书法还有意义吗\n大学生拿攒下的生活费买iPhone13Pro合理吗\n苏州最让你难忘的美食有哪些\n有人说最好的人生是为自己而活你赞同吗\n你有没有看病被坑过的例子\n从2019年新冠疫情到现在说说一件你生命中最有意义的事\n机关工勤人员工龄40年了可以提前退休吗\n如果选水浒传108将知名度最高的十个人你会如何排名", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 41, "original_length": 3321, "line_ids": [2, 3, 5, 6, 7, 8, 11, 12, 13, 14, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37], "language": "zh", "language_score": 0.96, "perplexity": 1771.4, "bucket": "middle"} +{"url": "http://www.bokee.net/bloggermodule/blog_listcolumn.do;jsessionid=3D3F4AB11C6B0ADA3C685442DACB4E79.fs02?columnName=%E9%93%9C%E6%A2%81%E5%8A%9E%E5%85%AC%E5%AE%A4%E8%A3%85%E4%BF%AE&bokeeName=fxf0428", "date_download": "2022-05-19T08:26:59Z", "digest": "sha1:M7OHBH56IJNM4FKQTIYMEHTIPX2LMU4I", "length": 242, "nlines": 3, "source_domain": "www.bokee.net", "title": "企博网职业博客", "raw_content": "铜梁办公室装修 (1篇) 展开 列表\n办公空间装饰效果图\n我们生活中大部分的时间都待在办公室里，兢兢业业的做着自己所负责的工作，认真的做着每一件我们应该做的事。那么很明显办公室装修的好坏或多或少的影响着我们生活的心情。光线是否明亮，通气是否顺畅，都影响我们工作的时候心情与效率。 一、铜梁办公室装修要充分了解企业类型和企业文化的深入理解，只有充分了解企业类型和企业文化，才能设计出能反映该企业风格与特征的办公室，使设计具有个性与生命。 二、企业内部机构", "cc_segment": 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轴颈是轴类零件的主要表面，它影响轴的回转精度及工作状态。轴颈的直 径精度根据其使用要求通常为 IT69，精密轴颈可达 IT5。4、位置精度 主要是指装配传动件的配合轴颈相对于装配轴承的支承轴颈的同轴度， 通常 是用配合轴颈对支承轴颈的径向圆跳动来表示的；根据使用要求，规定高精度轴 为 0.0010.005mm，而一般精度轴为 0.010.03mm。 此外还有内外圆柱面的同轴度和轴向定位端面与轴心线的垂直度要求等。\n下一篇 : 模具材料对热处理变形的影响", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 78, "original_length": 3436, "line_ids": [30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41], "language": "zh", "language_score": 0.98, "perplexity": 1829.3, "bucket": "middle"} +{"url": "http://www.fa-xx.com/fenleizuowen/", "date_download": "2022-05-19T08:56:51Z", "digest": "sha1:2KJCM3RHEAQN2OB5JMWT3PO6F5KFY3P5", "length": 2654, "nlines": 93, "source_domain": "www.fa-xx.com", "title": "满分作文网", "raw_content": "列表\n励志名言短句霸气\n18 15:12:24 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3、气味香味以后，再行把娃娃菜的菜茎与香菇下锅翻炒，仍然炒到香菇逆焉了，再行下菜叶。\n娃娃菜油炸香菇材料：娃娃菜2颗、香菇10朵、油、盐作法：1、香菇去根切片，将娃娃菜最外层的菜叶去除，然后去根，洗涤控水。2、然后将菜叶和菜茎缝合，茎部小块手指笔画的条。炒锅倒油，油热后用葱蒜炝锅。\n3分钟起锅装盘。\n泡椒油炸香干材料：香干150克、五花肉80克、泡椒4个、蒜苗1条、大蒜两瓣、白糖三分之一小匙、酱油一小匙、盐适量作法：1、香干切片，五花肉托薄片，泡椒斜切成圈，蒜苗洗涤切段，大蒜去皮切片；起油锅，下入五花肉，炒至出油；下入香干、大蒜片，炒至香干表面头顶变黄；2、下入泡椒、盐、糖，翻炒两分钟，下入蒜苗，放进生抽，炒匀才可出有锅。洋葱油炸肥牛材料：肥牛、洋葱、青椒、红椒、高汤、蒸鱼豉油、老抽、蚝油、现磨胡椒粉、红薯淀粉；作法1.洋葱切块；青椒切块；红椒切块；2.高汤半碗，特蒸鱼豉油三勺、老抽半勺、蚝油半勺，现磨胡椒粉适度，红薯淀粉少量，搅匀可用；3.煮锅坐水，水开后放进肥牛；用筷子拨散，关火；摇动至肥牛基本变色捞起；4.炒锅坐油；小火油五成冷时放进洋葱；炒出香味后加青红椒块同油炸；洋葱半透明时放进肥牛；5.。\n本文关键词：娃娃,菜炒,必威betway中文版,香菇,炒,牛肉,泡椒,炒香,干,洋葱\n上一篇： 洋葱的好吃做法 用它与鸡肉搭配 味道更佳 家人抢着吃 下一篇：没有了", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 43, "original_length": 3502, "line_ids": [16, 18, 19, 20, 21, 22, 24], "language": "zh", "language_score": 0.74, "perplexity": 1710.6, "bucket": "middle"} +{"url": 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"美狮贵宾会四名挂职干部第一时间返岗 稳步推进广西凌云县、乐业县疫情防控、定点帮扶工作\n壬寅虎年初，疫情突肆虐。\n近日，广西百色市突发本土新冠疫情，疫情无情人有情，来看看中国广核集团（简称“美狮贵宾会”）在广西的四位挂职干部如何在第一时间抗疫，又为他们的村民做了哪些事儿呢？\n在美狮贵宾会定点帮扶的广西凌云县、乐业县，医护人员们穿着防护服，坐着三轮车、铁皮船，骑着摩托车、小电摩，走村串户翻山越岭做核酸检测，一户不落。防疫人员累了，就在山路边躺下来休息，饿了，就干吃一袋方便面。尤其是今年百色气候特别寒冷，大雨过后满路泥泞，一不小心就会摔倒。可以肯定的是，这里的抗疫比其他城市艰难的多。\n春节返乡过节的美狮贵宾会四名挂职干部，听闻百色疫情后，心急如焚。2月15日，百色市解除人员“不进不出”管控措施，四位挂职干部第一时间启程奔赴岗位，16日就全部投入抗疫工作。\n广西凌云县\n在凌云县，美狮贵宾会挂职干部、县委常委、副县长刘华干一天内跑遍大半个县城，先后到县城药店、超市、农贸市场、凌云中学等地，检查防疫工作落实情况，了解疫情防控困难和物资需求。为有序推进复工复产，他先后来到凌云县那山生态农业开发有限公司等公司实地了解，推动解决企业复工遇到的困难，深入田间地头，推进春耕桑蚕产业生产。\n▲刘华干在加尤镇磨贤村了解春耕桑蚕生产\n广西乐业县\n在乐业县，美狮贵宾会挂职干部、乐业县委常委、副县长金万兵深入疫情防控一线，先后到网格化所负责的三个村和两个社区，检查防疫工作落实情况，了解防疫物资储备情况。之后，他又前往美狮贵宾会帮扶建设的乐业风电项目、生物质有机肥厂等，了解企业实际困难，深入交流解决办法。\n▲金万兵到乐业草王山茶业公司推动复工复产\n广西凌云县陇槐村\n在凌云县陇槐村，为提升群众外出务工的安全意识，确保脱贫人口外出务工，稳岗就业，美狮贵宾会挂职第一书记李熹耐心指导复工群众办理离百报备手续，宣贯外出防疫安全要点。\n▲李熹正在指导返岗群众填写离百报备个人信息表\n广西乐业县板洪村\n在乐业县板洪村，美狮贵宾会挂职驻村第一书记龚欢积极响应乐业县关于工作队全力参与疫情防控的工作要求，广泛动员村民积极参与防控，形成全员参与疫情防控的钢铁防线。\n▲龚欢向村民讲解疫情防控知识\n疫情防控不放松，乡村振兴在行动；战“疫”还在继续，大考仍在进行。美狮贵宾会将继续根据百色市防疫情况，结合凌云县、乐业县实际需求，继续支持两县扎实做好疫情防控，早日恢复正常生产，统筹推进巩固拓展脱贫攻坚成果同乡村振兴有效衔接工作。\n2021科普中国”十大科普作品", 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"疫情影响“三驾马车”奔速，企业应主动站位\n本文摘要：2020年进年即挑战，新型冠状病毒带给的疫情让大批中小企业动工阻碍，发展被困。在当下国民经济转型升级，全球科技产业竞争格局日愈多激化的大背景之下，疫情的突爆带给了极具破坏性的影响。未来一段时间，疫情对于国民经济发展的影响主要探讨于何处？中国科技产业发展路在何方？如何通过科技手段防止疫情损害更进一步不断扩大？涉及问题沦为了众多创业者与投资人思维颇多的问题。\n2020年进年即挑战，新型冠状病毒带给的疫情让大批中小企业动工阻碍，发展被困。在当下国民经济转型升级，全球科技产业竞争格局日愈多激化的大背景之下，疫情的突爆带给了极具破坏性的影响。未来一段时间，疫情对于国民经济发展的影响主要探讨于何处？中国科技产业发展路在何方？如何通过科技手段防止疫情损害更进一步不断扩大？涉及问题沦为了众多创业者与投资人思维颇多的问题。\n正式成立于2011年，国科嘉和基金是由中国科学院有限公司有限公司（全称：国科有限公司）作为基石投资人发动、联合国内多家大型企业集团联合正式成立的股权投资基金管理人，是国科有限公司必要管理的一级股权投资平台。目前旗下管理着多支人民币创投基金、并购基金、产业专项基金，以及美元创投基金和政府专项基金，资金管理规模约数百亿人民币；在投资方向上，国科嘉和主要注目TMT和生命科学两大领域。对于此次疫情的影响，国科嘉和对如何应付具有自己的分析和思维。\n近日，记者专访了国科嘉和管理合伙人王戈，分别就疫情愈演愈烈对国民经济发展的微观、中观、以及宏观等层面带给的影响展开了剖析，并对国科嘉和的基金运营情况展开了适当理解。疫情带给新一轮产业价值重估，企业宜早于做到打算目前，国科嘉和70%的资金投在了TMT领域，30%转在生命科学领域。\n无论是TMT，还是生命科学，都沦为了疫情愈演愈烈影响尤为根本性的领域。谈到疫情对这两大领域将来的影响，王戈从微观企业、中观行业、以及宏观国民经济发展三个维度展开了分析。首先，在微观领域，自2018年资管新规公布以来，资本市场规范制度的更进一步实行使得投资基金圈广泛高喊“寒冬”到来，这种状态仍然持续到2019年底，在投资基金领域的头部基金都实在募资艰难的情况之下，未来两三年预见将不会是行业配对的时候。疫情的愈演愈烈毫无疑问激化了创投领域企业融资的可玩性。\n“当见面都十分艰难了，做到尽调就更加不有可能了，企业融资节奏天然地上升了”。当然，虽然疫情的愈演愈烈为企业发展带给了新的挑战，但这其中也不存在生物医药、生鲜电商等领域的产业链获益的企业。\n“现阶段，企业应当戒骄戒躁，产业链获益的企业应当尽仅次于的力量为抗击疫情多做到一些贡献。不受疫情影响的企业也应当尽早作出调整，尽早尝试积极开展行之有效的补救措施，思维扩展新的业务，想要办法熬过艰苦时期，让企业活下去才是关键。”王戈建议。\n在中观层面，创业企业个体之外，投资基金股权投资行业也受到了极大的冲击。可以预料，这一次疫情之后，生物医药、体外临床、分子生物学、病毒类、防治领域必定将不会经常出现一波新的资本重估与声浪，这些领域将经常出现一些新的投资机会。\n此外，在TMT领域，将AI运用在生命科学和非接触式场景等横向细分方向，某种程度也将经常出现更加多新兴的应用于场景以及潜在市场。“譬如在疫情防控期，经常出现了人们在电梯内不愿必要用手摁楼层键的情况，这类场景用智能语音技术可以构建改建升级。”王戈举例道。\n下一步，AIoT、智慧城市等领域将获得更进一步的发展与普及。经过疫情的考验，在国家紧急状况下的卫生防疫等领域某种程度也将被发掘出更加多新的投资有可能。“在2月3日举行的政治局紧急会议上，精研总书记特别强调，疫情曝露了我们在防疫和应急领域不存在短板的问题，先前一定得重构。可以预测，涉及领域的市场机会也将随之经常出现，疫情完结之后还将不会有不少资本转入这一领域。\n”王戈补足道。在宏观层面，中国这样一个新的举国体制的国家，使得我们在疫情的防控号召以及针对性措施方面都不具备优势，悲观地辨别，这一次疫情的需要迅速的获得掌控，预测到2月底未来将会获得阶段性拐点，成功的话，在4月30日前获得基本面的胜利。而且预示着1.2万亿逆回购基金以及涉及鼓舞政策的实施，疫后经济获得很快声浪的情况还是可以意识到的。\n在疫情完结之后，消费虽然不会经常出现一定的声浪，但丧失的消费无法补仓；与此同时，进出口也因为大部分国际航线的停止而受到影响；当投资、消费、出口这“三驾马车”中的两驾跳跃阻碍且无法很快提振，投资驱动的效果将更进一步被缩放。“在房地产投资早已沦为过去式，接下来需要发力投资的领域将更进一步探讨在软科技、公共卫生、公共防疫、三农贫困地区、以及基础设施等方面，用作补足国民经济高质量发展过程中不存在的短板，更进一步达成协议年度经济发展目标。”王戈如此预判接下来一段时间的投资走势。\n“对于企业而言，融合各自企业所处的环境与业务特色以及发展目标，主动站位、主动卡位，很快做出业务调整；要有底线思维，作好极端情况的预案。”在约谈了近百家被投企业之后，王戈得出这样的建议。疫情愈演愈烈之后，工信部很快号召，倡议人工智能等涉及企业充分发挥各自优势，协力抗击疫情。\n在有效地抗击疫情方面，国科嘉和旗下多家产业链获益企业充分发挥各自优势，从病原检测、消毒、医疗物资捐献及仓储、基因组数据库分析平台、疫区教育等各方面贯彻科技抗疫，在防止疫情蔓延以及影响更进一步好转过程中贡献了最重要战力。据介绍，国科嘉和被投企业钛米机器人有一款针对智慧医疗场景获取智能机器人解决方案的智能消毒机器人很快转入武汉中心医院、协和医院、中南医院等最前线战场的医院隔离病房、ICU、手术室、痉挛门诊等科室，在医院疫情防控以及公共卫生管理方面充分发挥了最重要起到。此外，国科嘉和投资企业国科恒泰自武汉封城至今一次次来回于武汉各大医院运输捐献物资，保证为疫情防控第一线获取及时的物资运输服务；云呼科技获取平价医疗物资、通车湖北物流确保专线；微远基因于2019年12月底参予并已完成“新型冠状病毒”病原的找到和检验，并且顺利研制出“新型冠状病毒核酸检测试剂盒”，服务多达25家医院，已完成数千份检测。\n疫情加快产业转型升级，科技博爱是发展显然自2018年以来，全球科技产业竞争格局日愈多激化，同时国内也面对着产业转型升级的迫切需要，2020年又开局不如意遭遇疫情。中国科技产业转型升级的道路确有？面对哪些挑战？必须如何应付？针对这一问题，王戈的问是：“该遇上的问题会较少也会多，短期内显然不存在影响，但是将来来看影响并不大”。\n这一点，从股市在散户之后千股跌停，到如今也渐渐完全恢复的情况就可以显现出。长年开看，疫情的扰动并无法确实转变某一行业或者资本市场的显然运营。返回基本面，预示着疫情的愈演愈烈，投资人也在这样的一个情况被迫确实的静下心来，资本市场以及企业自身都将更为地重返价值，也锤炼了科技发展以及淘出了确实优秀企业。王戈回应，目前，中国科技仅次于的问题是技术原创性严重不足。\n国际上，美国的VCPE的投资基本是以技术为驱动的原创性投资居多，而反观国内，更好的是投资应用型的集成式创意。被迫否认，我们辞世界科技强国还是不存在客观差距，从仿效到消化访创再行到自律原创，我们还有一段路必须去回头。此外，我国科技成果转化率并不低。\n美国的科研成果转化率约在10%~11%之间，而国内的则在5%~6%之间。科研成果转化成在全球都是一个难题，如何将科研成果转化成为实际的市场产品？更进一步产生商业价值，这是注目科技投资的政府及各界人士广泛注目的焦点。我们国家的科技生态体系仍尚待更进一步完备，从原创、到转化成、到应用于，到投资机构的资本助力再行到技术获释全面商用，并最后产生一个合理的税收，我们还有许多改良之处。整体而言，重返价值导向，核心技术只有剪刀在自己的手里才安全性，在这次疫情中这一点再度凸显。\n接续中科院科技成果转化成，打造出中国本土科技投资王牌军作为国科嘉和基金发动的创立合伙人，王戈讲解，作为以中科院国科有限公司为基石投资人发动的基金，国科嘉和在正式成立之初就肩负着中科院科技成果移往转化成的愿景，致力于通过资本来撬动人才、运营、管理等各类资源，构建多方资源整合，提高科技成果转化成的效率与质量。初建1949年，与新中国同岁的中国科学院经过70多年的发展，目前有13个分院，100多所科研院所，产卵了上千家多家企业，其中有30多家上市企业；中科院担任着国家战略科技力量这一愿景。“在2011年的时候，当时的科学院领导以及国科有限公司涉及领导之后要求正式成立一支基金作为抓手，作为科研人员的成果转化成沦为市场上所须要商品的桥梁。国科嘉和基金是中科院’产业链+技术链+资本链’同步创意的关键一环。\n”王戈回应。在参予发起国科嘉和之前，王戈不仅具有20多年的中科院从业经验，而且其创办的“东方中科”还在2016年11月于深交所上市顺利，是中科院体系内具有顺利创业经验的人，这使得王戈在兼任中科院下科创基金国科嘉和的管理合伙人更加能辨别和解读创业团队和项目。据王戈讲解，行业里给国科嘉和打的标签主要有三个：首先是中科院背景，增进科技成果转化成，分担科学院和国家愿景；其次是显科技投资，只投有技术壁垒的企业；此外，科技投资作为一项并无法短期起效的投资门类，投资人的冷静与长年陪伴跑完精神也就显得无比最重要，也正因如此，有高度、有冷静、有前瞻性也沦为了国科嘉和的基金特色。东面中科院，国科嘉和在肩负了愿景的同时，却也因此取得中科院持续输入的优质科创项目以及强有力的国资背景承托，正式成立短短几年间顺利投资百余家企业，其中有数一批企业正在排队上市，至此挤身国内科技投资领域的头部基金。\n谈到未来三年的基金规划，王戈回应：“未来三年基金将固守自己的阵地，基于自身的优势，东面中科院，投好项目，确实增进科研成果转化成，为国家确实的兴起或者未来十年解决问题擦脖子问题作出贡献，有信心在未来一段时间内将国科嘉和打导致中国科技投资的王牌军。\n本文关键词：华体会官网,国科,嘉,和,王戈,疫情,影响,“,三驾马车,”\n上一篇：高通分享移动VR面临的挑战", 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"‘华体会’自然资源部抽查陕西省绿色矿山遴选情况\n本文摘要：近日，自然资源部第八抽验组到陕西省对2019年度全国甄选情况积极开展抽验。抽验组按照随机确认的抽验名单，实地检查了陕西省长武县高家堡煤矿、彬州市雅店煤矿、凤县如雷奥鼎盛铅锌矿、神木市黑龙沟煤矿、三江煤矿、石圪台煤矿等6个矿山。\n近日，自然资源部第八抽验组到陕西省对2019年度全国甄选情况积极开展抽验。抽验组按照随机确认的抽验名单，实地检查了陕西省长武县高家堡煤矿、彬州市雅店煤矿、凤县如雷奥鼎盛铅锌矿、神木市黑龙沟煤矿、三江煤矿、石圪台煤矿等6个矿山。抽验组现场查阅了矿山整体环境及生活区、生产区、生态区、中控系统建设情况，查询了建设涉及资料，核实了矿山企业管理制度台账，并与矿山企业展开座谈交流，详尽理解矿山企业建设的效益、第三方评估制度的实行、员工对的了解、矿产资源综合利用、节能减排、矿山数字化建设等情况。\n抽验组对陕西省建设方面所做到的工作给与了充分肯定，并就矿山更进一步提高建设水平明确提出针对性的建议。下一步，陕西省将认真总结和推展建设工作经验和方法，坚定不移地贯彻“绿水青山就是金山银山”的发展理念，精确做到自然资源工作新的定位，将绿色发展观念跨越于矿产资源规划、勘查、开发利用和维护全过程，减缓前进建设，着力构成合乎生态文明建设拒绝的矿业发展新模式。\n本文关键词：‘,华,体会,华体会,’,自然,资源部,抽查,陕西省,绿色\n上一篇：安徽与内蒙古开展能源战略合作参与“北煤南运”", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 30, "original_length": 2526, "line_ids": [12, 14, 15, 16, 17, 19], "language": "zh", "language_score": 0.91, "perplexity": 1666.5, "bucket": "middle"} +{"url": "http://www.gzkmlqxb.com/show-54-795-1.html", 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李亚乐\n2023年度重点项目施工及物资采购招标工作，对你们进行廉政谈话，希望你们坚守底线，严格按照国家法律法规规定及招标程序要求，公开、公平、公正地开展招标工作。”2021年12月7日，公司党委委员、纪委书记马昭奕在招投标前廉政谈话时说道。\n，对经营管理部、工程与物资管理部进行了建设工程项目和大宗物资采购招投标前廉政谈话。\n谈话中，公司纪委强调要严格按照招投标相关法律法规和公司规章制度工作，严格按程序办事，不得违反招投标、物资采购及施工管理各项规章制度要求；要把廉洁工作落实到招标工程的每一个环节中，确保做到“招标干好、干部廉洁”；要守好个人廉政底线，与施工方、供货商保持正常业务往来，踏踏实实工作，堂堂正正做人，清清白白干事。\n相关负责人表示，招投标管理工作是廉政风险防控的重中之重，一定时刻提醒自己，不断提高思想认识，树牢廉洁自律的底线思维，紧绷廉政这根弦，依规依据完成年度重点项目招标工作。\n此次谈话是城燃公司纪委开展常态化廉政约谈系列动作之一。今年以来，城燃公司纪委将开展廉政谈话作为推进党风廉政建设的重要抓手，坚持教育为先、防范在前理念，变事后问责为事前提醒、变被动查处为主动防范，实现廉政谈话常态化，进一步筑牢党员干部廉洁自律防线。\n廉政谈话坚持“三必谈”。一是重点岗位必须谈。对于公司涉及人、财、物管理的重点岗位工作人员，按照其从事工作内容进行风险等级划分，定期开展约谈工作，将廉政谈话渗入工作日常，做到提醒在先。二是坚持提拔任用必须谈。对于新提拔任用干部，遵循凡提必谈、预防在先、务求实效的原则，由公司党委书记、纪委书记对其进行谈话提醒，做到教育在先。三是坚持重大节假日必须谈。针对重大节庆假日易发高发问题，由公司纪委委员约谈相关职能部室、分公司主要负责人，做到预防在先。截止目前，城燃公司纪委共计开展廉政谈话17次，共计135人次。\n今后，城燃公司纪委将不断完善廉政谈话机制，同时尝试将廉政故事、警示教育等融入廉政谈话中，用更直观的方式教育警示党员领导干部，真正做到知敬畏、存戒惧、守底线，营造风清气正干事创业环境。\n上一篇：城燃公司召开2021年度财务决算及审计工作会议 下一篇：公司第五党支部深入学习贯彻全会精神 推进比学赶帮超实践活动", "cc_segment": 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絮凝剂\n选矿厂废水用哪一种聚丙烯酰胺合适\n选矿厂是用水大户,同时选矿废水具有水量大，悬浮物含量高，含有害物质种类较多的特点。其有害物质是重金属离子和选矿药剂。重金属离子有铜、锌、铅、镍、钡、镉以及砷和稀有元素等。对于选矿废水的处理是现在环境保护的重中之重，保护水资源的必要解决，鼎丰公司生产的聚丙烯酰胺就可以很好应用在选矿废水处理中，很好的解决选矿废水处理带来的污染问题。\n在选矿厂中尾矿是选矿中分选作业的产品之一，由于有效成分的含量相比来说比较低，但由于技术方面的限定，又不能再进一步进行分选的产品被称为尾矿，但如果不经处理它对环境来说却是极大的污染及安全隐患，处理尾矿一般选用高分子量的阴离子聚丙烯酰胺，尾矿池溢出水流非常清澈，能够高质量地满足生产需要!\n使用聚丙烯酰胺处理尾矿废水有以下优点：用水质量得到了保证，精矿品位得以提高，极大的降低了能耗。国内外大量实践证明, 选矿厂特别是磁选厂采用絮凝剂对尾矿进行絮凝沉降, 极大提高经济效益。\n应用在选矿厂的使用方法以及注意事项：\n3kg的聚丙烯酰胺固体颗粒。待其完全溶解之后，再根据选矿厂水质的流量、浓度，来调节聚丙烯酰胺溶液的加药量。", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 80, "original_length": 3929, "line_ids": [45, 47, 48, 49, 50, 51, 52], "language": "zh", "language_score": 0.85, "perplexity": 1630.5, "bucket": "middle"} +{"url": "http://www.hnzadz.cn/xwzx/208.html", "date_download": "2022-05-19T07:49:40Z", "digest": "sha1:2BSDYM2JQDVV7F3TIQ2M4MSWRKG3MR2B", "length": 1171, "nlines": 10, "source_domain": "www.hnzadz.cn", "title": "带你了解中安红外测温门厂家_河南中安电子科技研究院", "raw_content": " 带你了解中安红外测温门厂家\n带你了解中安红外测温门厂家\n红外测温门如何从中国的盗版变成红外中国制造的测温门，最早是随着中国，经济的发展而出现的，而进口到中国，由于早期的高利润和低技术壁垒，吸引了大量资金进入安检设备市场，带你了解中安红外测温门厂家\n截至目前，红外，已有100多家大型和小型测温门制造商，10多家企业具有一定的技术开发实力和一定的市场规模。如何避免恶性价格竞争，建立和管理自己的品牌，避免昙花一现，保持长期可持续发展，是大多数红外测温门制造商必须考虑的问题。因此，安防设备制造商寻求自己的重新定位和潜在市场是必然趋势。\n“智能”在安全检查机器中是什么意思?简单来说，智能就是充分挖掘和提取红外，辐射物体的关键信息，并利用这些信息为用户提供有价值的服务。智能系统不仅可以提高报警的准确性，减少漏分析或误分析的现象，还可以缩短响应时间和速度，形成更有效的现场数据，提示安全人员在安全威胁发生前关注相关监控画面，为潜在威胁做好准备。因此，智能化将是视频监控发展的必然趋势。在交通运输、安全城市建设、金融监管等领域，智能化是必然的。因此，毫无疑问，智能将包含巨大的市场前景，值得包括红外在内的所有安全制造商高度重视\n当然，安全检查机器制造商不可能一夜之间进入智能市场。因为安防设备厂商普遍注重图像传输，要想与智能结合，不仅要有足够的创新勇气，还要有足够的创新实力。只有通过合理的创新，才有可能在智能领域找到一条独特的道路。\n5年，还是有可能赶上的。\n厂商无疑会成为安检设备行业的领头羊。\n上一篇：中安安检门厂家带你了解安检门的分类和应用 下一篇：如何选择热成像测温门才能更优惠，中安会带你去了解\n安检机厂家河南中安严守安检防", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 76, "original_length": 5058, "line_ids": [23, 24, 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"“之前我们从联赛开始的时候，从来没有考虑非常长远的计划，我们一场一场踢下去。所以说，从这周六开始，我们又会对阵相同的对手，对于我来讲只会每场继续准备，踢好每场比赛，尽可能多赢得分数。\n”　萨比提本场比赛斩获了个人中超联赛的首粒进球，对于这名球员萨布利奇点评道：“不只是对于萨比提一名球员，对所有球员，进球都会提升信心。另外，大家的自信心、我们球队的自信心也在增强，大家踢球越来越自信，大家表现也很好，拿到了很好的结果。\n”　而在赛后瞬采时，为沧州队破门的萨比提接受了媒体采访。　对于本场比赛，萨比提总结道：“今天上半场进了一个球，非常开心，这是我中超联赛第一个进球。”对于全队的发挥，萨比提表示：“今天全队表现很好，对方实力跟我们差不多，球队赢球我们很开心。\n”　（老安）。\n本文关键词：十大正规体育平台，体育平台，世界杯,萨布,利奇,沧州,雄狮,展现,拼搏精神,祝,津门", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 24, "original_length": 3013, "line_ids": [10, 11, 12, 13, 14], "language": "zh", "language_score": 0.97, "perplexity": 1650.3, "bucket": "middle"} +{"url": "http://www.qdshine.cn/News-1132903.html", "date_download": "2022-05-19T09:39:46Z", "digest": "sha1:PFTWRGV6Z2EGK23NVJO7NK7ME4O427UK", "length": 693, "nlines": 9, "source_domain": "www.qdshine.cn", "title": "青岛盛瀚色谱技术有限公司", "raw_content": " 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"注塑机有哪些分类\n注塑机又名注射成型机或注射机。它是将热塑性塑料或热固性料利用塑料成型模具制成各种形状的塑料制品的主要成型设备。分为立式、卧式、全电式。注塑机能加热塑料，对熔融塑料施加高压，使其射出而充满模具型腔。\n1、按塑化方式分类\n1.柱塞式塑料注射成型机：它的混炼性很差的，塑化性也不好，要加装分流梭装置。现在已很少使用。\n2.往复式螺杆式塑料注射成型机：依靠螺杆进行塑化与注射，混炼性和塑化性都很好，现在使用最多。\n3.螺杆――柱塞式塑料注射成型机依靠螺杆进行塑化与依靠柱塞进行注射，两个过程分开来。\n2、按合模方式分类\n1.机械式\n2.液压式\n3.液压――机械式\n3、按排列方式分类\n注塑机通常由注射系统、合模系统、液压传动系统、电气控制系统、润滑系统、加热及冷却系统、安全监测系统等组成。\n（1）注射系统\n注射系统的作用：注射系统是注塑机最主要的组成部分之一，一般有柱塞式、螺杆式、螺杆预塑柱塞注射式3种主要形式。目前应用最广泛的是螺杆式。其作用是，在注塑料机的一个循环中，能在规定的时间内将一定数量的塑料加热塑化后，在一定的压力和速度下，通过螺杆将熔融塑料注入模具型腔中。注射结束后，对注射到模腔中的熔料保持定型。\n注射系统的组成：注射系统由塑化装置和动力传递装置组成。\n螺杆式注塑机塑化装置主要由加料装置、料筒、螺杆、射咀部分组成。动力传递装置包括注射油缸、注射座移动油缸以及螺杆驱动装置（熔胶马达）。\n（2）合模系统\n合模系统的作用：合模系统的作用是保证模具闭合、开启及顶出制品。同时，在模具闭合后，供给予模具足够的锁模力，以抵抗熔融塑料进入模腔产生的模腔压力，防止模具开缝，造成制品的不良现状。\n合模系统的组成：合模系统主要由合模装置、调模机构、顶出机构、前后固定模板、移动模板、合模油缸和安全保护机构组成。\n（3）液压系统\n液压传动系统的作用是实现注塑机按工艺过程所要求的各种动作提供动力，并满足注塑机各部分所需压力、速度、温度等的要求。它主要由各自种液压元件和液压辅助元件所组成，其中油泵和电机是注塑机的动力来源。各种阀控制油液压力和流量，从而满足注射成型工艺各项要求。\n（4）电气控制系统\n电气控制系统与液压系统合理配合，可实现注射机的工艺过程要求（压力、温度、速度、时间）和各种程序动作。主要由电器、电子元件、仪表、加热器、传感器等组成。一般有四种控制方式，手动、半自动、全自动、调整。\n（5）加热/冷却系统\n加热系统是用来加热料筒及注射喷嘴的，注塑机料筒一般采用电热圈作为加热装置，安装在料筒的外部，并用热电偶分段检测。热量通过筒壁导热为物料塑化提供热源；冷却系统主要是用来冷却油温，油温过高会引起多种故障出现所以油温必须加以控制。另一处需要冷却的位置在料管下料口附近，防止原料在下料口熔化，导致原料不能正常下料。\n（6）润滑系统\n润滑系统是注塑机的动模板、调模装置、连杆机铰等处有相对运动的部位提供润滑条件的回路，以便减少能耗和提高零件寿命，润滑可以是定期的手动润滑，也可以是自动电动润滑；\n（7）安全保护与监测系统\n注塑机的安全装置主要是用来保护人、机安全的装置。主要由安全门、液压阀、限位开关、光电检测元件等组成，实现电气――机械――液压的联锁保护。\n监测系统主要对注塑机的油温、料温、系统超载，以及工艺和设备故障进行监测，发现异常情况进行指示或报警。\n昆山泰宏瑞机械有限公司是一家提供油压快速换模设备专业厂商，主要用于冲床、注塑机、压铸机、油压机等设备的模具快速更换，其产品包括：气动泵浦组合、夹模器、举模器、移模臂、电气控制系统、超负荷保护系统、滑块锁固系统、换模台车以及印刷电路板冲孔油压脱料系统等。（注塑机快速换模）", 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"茶冲到第几泡最好喝(茶第几泡可以饮用)\n经常会听到这么一句话：“一泡水，二泡茶，三泡四泡是精华，七泡有余香”那是不是所有的茶都是这样的吗？都是三泡四泡最好喝喽？\n1、绿茶\n85℃为宜。一般来说头泡茶就会将绿茶中的大部分内含物质冲泡出来。\n所以绿茶的头一两泡茶就是最精华、滋味最丰富的。而到了三四泡之后，味道就会变得寡淡，渐渐只留下一点甜味了。\n2、红茶\n红茶属于全发酵茶。不论是小种红茶还是工夫红茶，在制茶过程中都有经过揉捻、烘干等工序，尤其传统的正山小种红茶还经历了“过红锅”这种高温工序。经过这些工序制作的红茶，需要用高温的沸水冲泡，才能更好地激发茶香和茶味。红茶拥有丰富而馥郁的茶香，如桂圆香、花香、松烟香、蜜薯香等。\n红茶的头泡往往无法将红茶激活到最好的状态，但是经过头泡唤醒的二、三泡可以视为最珍贵的精华泡数。当然好的红茶，不同的泡数会呈现出不同的滋味和香气，但其中这二、三泡一定是不能错过的。\n3、乌龙茶\n乌龙茶由于不同的茶树品种、生长环境、加工工艺和发酵程度等原因的差异，而拥有了不同的茶叶形态。主要有条索型、颗粒型和紧压型，如武夷岩茶呈现条索型，铁观音一般是颗粒型，而紧压型的乌龙茶唯有一个品种，那就是漳平水仙。\n不同的乌龙茶泡法和最佳滋味的泡数也会有不同，这里以铁观音为例。铁观音有“七泡有余香”的美誉，但是通常经过三泡之后茶叶才可以展开，香气才会弥散开来。到第四泡时，才会散发出真正的高香，所以精华应该是在4泡左右。\n4、白茶\n白茶是越陈越香、越存越有价值的一种茶。特别是老白茶，沉淀时间长，需要多泡几次才能完全将它内含的物质刺激苏醒。冲泡白茶，最好用白瓷盖碗、沸水。第一泡先醒茶，使茶叶都舒展开；第二泡汤水逐渐变稠。\n8泡，或者泡后再煮，能让茶叶充分释放内质，更加的香浓醇和。\n5、黄茶\n6泡为宜。\n6、黑茶\n作为后发酵茶，黑茶的保存期非常长，在储存中可以随着时间的推移进行自然的陈化，在一定时间内，还有越陈越香的特点。为了便于保存，黑茶多以紧压茶形式封存，泡茶时，需先撬开紧实的茶砖或茶饼，再以100℃沸水洗茶，以滤去茶叶的杂质。\n头两道茶味道都不够纯，可能会掺杂黑茶制作过程的闷堆味。到第三四泡的时候，茶叶就已经舒展开，茶汤的香醇就显现出来了。一般来说，黑茶三四道茶汤最好喝，可以泡的次数比较多，可达十次，不过冲泡越到后面水味越重。\n有人对每一泡茶都有着深刻的感情，甚至用优美的辞藻来形容。比如第一泡，用了人生若只如初见，第三泡豆蔻梢头二月初，第六泡最是橙黄橘绿时，第九泡意犹未尽不思还。这也说明一个事实，其实每一泡茶都很好喝，只要用心去体会，去欣赏，就能发现每一泡茶都有着其独特的味道。\n泡茶的过程中，正是这每一道茶都有不同的味道，才给我们的生活增添了乐趣。想象一下，如果每一泡茶都是相同的味道，品茗的乐趣将从何而来？从滋味的角度看，要想知道茶喝第几泡最好，当然是每一泡都需要进行尝试，然后得出自己的结论。从品茗的角度来看，喝每一泡茶，都有着别样的精彩。\n普洱生茶存放时间长可以喝吗(普洱生茶需要存放多久才能喝)\n舌头干苦吃什么茶好(喝完茶感觉舌头很涩)\n普洱茶刮油原因(喝普洱茶容易饿是因为茶刮油吗)\n很多茶友都有过这样的体会，就是喝生普的时候喝容易肚子饿，大家的理解可能是普洱茶刮油，能带走一部分脂肪，所以喝普洱茶容易有饥饿感。可事实真的是这样的吗？ 我们需要从普洱茶中帮助肠胃蠕...\n了解普洱茶的基本知识(普洱茶熟茶后起之秀有哪些)\n福建古树普洱(正宗糯米香古树普洱)\n远在边陲之地的云南，守着天赐的茶树资源，长期保留了制茶童年期稚拙的技艺。 唐代，闻名天下的是产于现在江南茶区江苏宜兴的阳羡贡茶，这种蒸青茶的制作技艺登峰造极。与此相较，樊绰笔下《蛮...\n在家存放普洱茶需要注意哪些细节(正确存放普洱茶需要解决哪些问题)\n紫砂容器 紫砂用具历来都是与茶相伴而生的，平时我们泡茶，最好的茶具也应该是紫砂茶具。 因为紫砂容器的的透气性最好，能够保证普洱茶陈化所需要的氧气。只是现在的紫砂容器比...\n普洱茶怎么冲泡才能有好品质(普洱茶怎么冲泡才能最好)\n普洱茶，因曾经普洱是运销集散地，所以命名普洱茶。普洱茶茶汤颜色橙黄，味道浓厚，香气清雅，以至于冲泡后的茶叶也带有香味。说了这么多普洱茶的优点，那普洱茶要怎么冲泡才地道？相信很多茶友...\n铁观音酸味正味怎么区别(铁观音是热性茶还是凉性茶)\n铁观音的焙火程度不同，茶性也不同。清香型和陈香型的铁观音茶性偏凉，适合燥热体质的人饮用。而浓香型的铁观音茶性偏温，适合虚寒体质的人饮用。 铁观音属性介绍 按焙火程度的不同，铁观音可...\n普洱生茶需要存放多久才能喝(普洱生茶一般存放多久喝比较合适)\n我们经常听说普洱茶有越陈越香的特点，很多茶友会收藏一些自己喜欢的普洱生茶等到以后再拿出来品饮。但是一款茶到底应该收藏多久呢？ 我们以生茶为例，一般来讲生茶有几个品饮期（良好的储藏环...\n白茶的储存期是多长(茶叶店白茶放久了会怎么样呢)\n「茶学院」白茶是六大茶类中工艺最简单的一种茶。采摘的鲜叶经过晾晒萎凋，然后低温烘至足干，从而制成的白茶保持了茶叶最原始的味道。并且白茶有着普洱茶能存放的特性，质量好的白茶只要是长...\n云南的红茶哪个品种好(云南红茶哪个品种最好)\n云南红茶的品种有滇红工夫茶、金丝滇红、滇红金针、野生红茶、普洱晒红、紫鹃红茶、红碎茶等，云南红茶的主要产区为滇西南澜沧江以西，怒江以东的高山峡谷区，包括凤庆、勐海、临沧、双江等...\n安徽绿茶有哪几种(安徽绿茶前十名好喝)\n安徽绿茶有黄山毛峰、六安瓜片、太平猴魁、休宁松萝、屯溪绿茶、桐城小花、岳西翠兰、涌溪火青、老竹大方等品种，其中的黄山毛峰、六安瓜片与太平猴魁都属于中国十大名茶之一。 安徽绿茶品种大...", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 99, "original_length": 9746, "line_ids": [8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 35, 37, 41, 42, 45, 49, 50, 52, 53, 56, 57, 60, 61, 64, 65, 68, 69, 72, 73, 76, 77], "language": "zh", "language_score": 0.94, "perplexity": 1564.6, "bucket": "middle"} +{"url": "http://www.yudianonline.com/Article-2076275.html", "date_download": "2022-05-19T08:29:52Z", "digest": "sha1:MF6K7WUUNHUPX77CVKVJMB5ZUCS7IQJ5", "length": 674, "nlines": 10, "source_domain": "www.yudianonline.com", "title": "厦门宇电自动化科技有限公司", "raw_content": " 如何分辨温控器质量的好坏\n如何分辨温控器质量的好坏\n对于市场上所卖的温控器的质量的好坏，相信很少有人能够分辨的出来，今天小编就教大家对于温控器的质量如何进行辨别，都有哪些方法呢?\n辨别温控器质量的好坏，主要从以下几个方面进行辨别：\n继电器。它是输出控制的关键部分，一般温控器主要的设备就是继电器了，而关于继电器有很多种类，可以分为国内、中国台湾和国外这三类产品了，目前使用平凡的就是国内所生产的继电器了，所以，如果温控器质量好的话，那么它至少使用的继电器的质量要好。\n2、看温控器的CPU，对于温控器在使用时，它的自身精度和稳定性能很重要，而对于精度和稳定性都被温控器的CPU所控制着。\n3、看温控器的供电方式，一般会有两种，分别是变压器和开关电源控制。而在这两种方式中，根据它们所具有的特点，好使用的是变压器的供电方式。\n希望大家能够记住以上所说的关于如何识别温控器的方法并能够运用到实践中去，那么相信你会选择到一台质量好的温控器。\n厦门宇电自动化科技有限公司产品包括AIDCS智能分布式控制系统、分体式无纸记录仪、人机界面触摸屏、高精度PID调节器、智能温控器、导轨安装工控仪表、手持式精密测温仪、可控硅调功触发器等，产品提供长达十年的免费保修，广泛应用于化工、热电、石化、制药、冶金、电炉、热处理、食品、塑胶、包装等行业，在晶体生长、超导材料及精密试验设备等领域中拥有众多长期配套客户。\n上一篇 你知道温度显示仪的调节方法是什么么 下一篇 无纸记录仪的使用及选型", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 131, "original_length": 5468, "line_ids": [106, 107, 109, 110, 111, 112, 113, 114, 115, 116], "language": "zh", "language_score": 0.99, "perplexity": 1725.5, "bucket": "middle"} +{"url": "http://www.yugejs.com.cn/news_detail/6310072.html", "date_download": "2022-05-19T09:36:05Z", "digest": "sha1:4FIYONG6PG2FRGQQJUJI6DDGIXIGMY7M", "length": 697, "nlines": 18, "source_domain": "www.yugejs.com.cn", "title": "深圳誉格金属制品有限公司", "raw_content": "压铸件喷漆不牢固应该怎么解决？\n压铸简称为压铸。 它是一种将熔融的合金液体倒入压力室，高速填充模具的型腔并在压力下凝固合金液体以形成铸件的铸造方法。 压铸与其他铸造方法不同的主要特征是高压和高速。\n100MPa。\n0.2秒内填充型腔（取决于铸件的大小）。\n压铸是一种精密铸造方法。 通过压铸制成的压铸件的尺寸公差非常小，并且表面精度非常高。 在大多数情况下，压铸件无需旋转即可组装和应用。 螺纹零件也可以直接铸造。\n大多数小零件（例如通用相机零件，打字机零件，电子计算设备和装饰品）以及车辆的复杂零件（例如汽车，机车和飞机）都是通过压铸制造的。\n压铸画怎么了\n涂料层脱落的原因：压铸件的涂料层主要是由于涂料与压铸件之间的附着力不足。\n压铸漆的附着力取决于漆前处理的质量。 预处理是根据材料匹配适当的清洁剂，而不仅仅是去除油。\n压铸部的表面具有通过脱模剂的高温碳化而形成的碳化膜层。 该膜不利于油漆的粘附，也不利于油漆后的盐雾试验。 因此，脱脂剂应在脱脂的同时去除，然后进行钝化处理，以满足油漆喷涂的测试要求。\n具有以下影响因素\n1.油漆和冲头油未清洗；\n2.后续加工过程中压铸零件的切削油未清洗；\n3.压铸表面的氧化层未激活；\n4.压铸件的磷化过程做得不好；\n5.油漆问题；\n6.烘烤温度；\n深圳誉格金属制品有限公司", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 121, "original_length": 3776, "line_ids": [39, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 103], "language": "zh", "language_score": 0.99, "perplexity": 1572.3, "bucket": "middle"} +{"url": "http://www.yxlingli.com/dcwy/1163.html", "date_download": "2022-05-19T09:48:49Z", "digest": "sha1:QZH7ALQE2GZNFI77KJKXA2DTABEUD3IK", "length": 1053, "nlines": 18, "source_domain": "www.yxlingli.com", "title": "我如何让我的妻子出轨出门？_佛山私家调查_佛山婚外情调查_佛山商务调查_侦探私家公司", "raw_content": "\n我如何让我的妻子出轨出门？\n随着我国人口的增加，越来越多的人离婚，离婚率也大大提高。离婚的原因更多。最常见的是配偶出轨之一导致婚姻的关系破裂。双方协商后，出轨的其中一位将离开房屋，剩余财产归另一方所有，那么我们如何让妻子出轨的房屋出去？\n一、如何让他的妻子出轨出门？\n一方出轨可以用作法院决定两方之间离婚的依据，但不能用作财产分割的依据。因此，它不应该参与“打扫屋子”。\n根据“最高人民法院关于审理离婚案件中夫妻关系破裂的若干具体意见”，一方当事人通奸或非法同居，未表现出re悔受教育后，非过失一方起诉离婚，或过错一方起诉离婚，而另一方不同意离婚。在受到批评，教育，制裁之后我如何让我的妻子出轨出门？，或者在人民法院裁定离婚被拒绝之后，有过错的当事方再次起诉了离婚。如果确实没有和解的可能性，法院可以裁定离婚。\n第17条，由配偶在婚姻关系中获得的下列财产应由配偶共同拥有：\n（[一)薪水，奖金；\n（二)生产经营收入；\n（三)知识产权收入；\n（四)继承或捐赠的财产，但本法第18条第3款的规定除外；\n（五)应共同拥有的其他财产。丈夫和妻子具有处理共同财产的平等权利。\n第39条佛山专业私家侦探，离婚时，夫妻双方的共同财产应由双方协商解决；协议不成的，人民法院根据财产的具体情况和照顾子女权利的原则作出判决。夫妻在家庭土地承包经营中享有的权益，应当依法受到保护。\n第40条规定，夫妻双方书面同意，在关系婚姻期间获得的财产应属于彼此。如果一方为抚养子女，照顾老人，协助另一方工作等付出了更多的职责，则它有权在离婚时向另一方支付费用。一方要求赔偿的，另一方应当赔偿。\n第42条规定，在离婚期间，如果一方生活困难，另一方应从其个人财产（例如住房）中提供适当的帮助。具体办法由双方协商确定；协议不成的，由人民法院判决。\n佛山婚外情取证许多丈夫对妻子的问题有很多不同的看法。起点是好是坏。为了成为离婚时财产数额的受益人，有人会让对方出轨]然后使对方有过错，从而导致婚姻之间的关系破裂，因此夫妻的共同财产和以其名义拥有的财产将归非出轨的当事方所有。\n一个男人在他的妻子出轨教你\n出轨女人的个人经历：一", "cc_segment": 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抖音账号购买_可靠的短视频营销方案(利用短视频营销的好处有哪些)\n抖音账号购买_可靠的短视频营销方案(利用短视频营销的好处有哪些)\n随着5G时代的到来，互联网互动行业将成为新的赚钱商机。其中短视频领域被寄予厚望，各类短视频营销成为商家探索互联网的新手段。为什么短视频营销产品这么有前景？短视频营销与传统营销方式相比有什么优势？接下来，淘新媒体就带你一探究竟！\n病毒传播速度快，很难复制原始的优势数据。短可视化效果的视频内容创作者可以是企业，也可以是个人。它的内容生产，用户成本的自发传播和相对较低的粉丝水平。但做短视频，一定要有好的创意内容，坚持原创输出的决心，才能创作出高质量的短视频，吸引用户的关注。\n那么我们如何制作一个有吸引力的高质量短视频呢？营销圈推荐你在这里看这个问题“如何找到一个抖音视频片段，抖音营销圈教你去哪里收集素材？”本文会分享很多有用的内容，短视频制作干货人气。\n与传统营销相比，视频营销的一个明显特点是可以分析视频传播的范围和效果。包括有多少人关注，多少视频，多少转载，多少评论，多少人互动等等。无论是哪种短视频，我们都可以直观的看到播放量、评论量等数据。\n相对于传统的广告营销力度，单纯的低成本营销解决了短视频营销门槛更低、成本更低的问题。这也是短视频营销的优势之一。从目前流行的智能、抖音等短视频平台可以看出，短视频病毒营销传播速度之快，互联网的优势最为明显。\n重要的是，短视频的“短”功能在快节奏的生活方式中尤其受到用户的青睐。无论是美拍、梨视频、头条、灵巧还是抖音，只要你的内容足够精彩，就能引起大量用户的转发狂潮，实现大规模的有效传播。\n不仅如此，各类短视频平台还积极与用户基数庞大的新浪、媒体平台合作，强强联手，吸引更多流量，进一步推动短视频分发，实现优质营销效果。此外，与图片和文章容易复制粘贴不同，短视频可以添加水印和原创作者联系方式，保护原创内容创作者的利益。我们可以通过数据分析、标杆观察账号、行业竞争对手等数据，及时调整优化短视频内容，从而达到更好的营销效果。\n上一篇：头条号账号变现计划(头条号个人号开店必须要执照吗)_买抖音号\n下一篇：南通网络营销案例(南通网络营销模式)_抖音等级号出售\n微信公众号推广文章怎么写(如何推广微信公众号文章)\ntiktok未来现状(tiktok中国现状)", "cc_segment": 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67, 73], "language": "zh", "language_score": 0.93, "perplexity": 1559.9, "bucket": "middle"} +{"url": "http://www.seedasdan.org/journalism/", "date_download": "2022-05-19T08:03:00Z", "digest": "sha1:LRPJIQ6F3ANUWLPQEPAXW3O2LXLDOY5E", "length": 6938, "nlines": 168, "source_domain": "www.seedasdan.org", "title": "赋能受益终身的核心素养", "raw_content": "主题类别\n作品提交\n由美国权威传媒组织的国际高中传媒荣誉协会主办\n始于1926年，历时96年\n全球44个国家和全美50大州近1.5万所学校参与\n涵盖写作、视觉/摄影、视觉/设计、多媒体四大传媒类别，35个细分主题\n赢得申请世界名校与人文类专业竞争优势\n美国国际高中传媒荣誉协会（International Honor Society for High School Journalists）\n国际高中生传媒作品展评（International Writing, Photo and Multimedia Contest）被誉为美国高中生的“普利策奖”，由美国国际高中传媒荣誉协会举办。美国国际高中传媒荣誉协会于1926年4月在美国爱荷华大学（University of Iowa）成立，在全球44个国家和美国50个州有14232所会员学校和497626位会员。几乎美国绝大多数著名高中的校刊、校报学生编辑和记者都是其会员。美国国际高中传媒荣誉协会的宗旨是让更多学生了解传媒新闻行业，为全球培养充满热情和专业的新闻传媒人才。\n中国区独家合作方\n阿思丹（ASDAN China）成立于2011年9月，是中国领先的国际素质教育平台。阿思丹（ASDAN China）致力于与全球著名教育组织、权威学术机构、世界名校合作，将各类国际顶级素质教育资源引入中国，帮助各合作伙伴学校完善素质教育体系，帮助同学发展受益终身的核心素养。迄今为止，阿思丹（ASDAN China）与全国包括港澳台地区在内的67个城市1175所国际课程学校和重点中小学建立了合作，累计超过100万人次中国学生参加过阿思丹（ASDAN China）组织的各类国际素质教育项目。2020至2021学年，全国超过22万人次同学参加过阿思丹（ASDAN China）组织的各类国际素质教育项目。\n报名选手：\n19岁\n爱好新闻写作、传媒摄影与平面设计的高中生\n有志于申请大学传媒、新闻与人文相关专业的同学\n提交作品语言：\n总共写作、摄影、设计、多媒体四大类别35个细分主题，每个选手可以选择任意类别和主题提交作品，总共不超过4个作品；\n可以是同一个主题提交4个作品，也可以提交4个不同主题作品；\n作品须为原创作品，不能抄袭；\n个人投递作品须独立创作，不得团队创作；\n团队投递作品有特定要求（参照说明）；\n未正确识别或放入错误主题类别的作品将被取消资格；\n不同类别和主题的作品有特定要求（参照说明）。\n评选方式：\n美国国际高中传媒荣誉协会每年邀请专业传媒人事担任评委工作，从每个主题的所有作品中选出优秀作品参与“全球奖项”的评选。\n所有评委均为美国相关领域的专家与优秀传媒工作者。其中写作类评委包括报道过美国橄榄球联盟超级碗的专业记者、丹佛日报专业记者、美国著名大学教授新闻的讲师、高端体育协会从业人员等；新闻摄影类均为专业摄影师；设计类评委为杂志、报社主编等。\n参与主题类别\n总共写作、摄影、多媒体、视觉/设计四大类别35个细分主题，每个选手可以选择任意类别和主题提交作品，总共不超过4个个人作品及1个团队作品\n2022】国际高中生传媒作品展评中国区获奖名单\n2021全球奖获奖作品赏析\n一、写作类 WRITING 作品提交要求：\n每幅作品要包含一个标题\n形式：PDF或是图片JPG\n注：如果是已经投稿的印刷纸质版，请确保扫描清晰并转化为PDF格式或是图片JPG格式上传\n无字数要求\n1.社论写作 Editorial Writing\n必须是传达编者意见的单一社论，而不是一个系列。社论可以由一人编写，无署名行。但是，如果有署名行，则必须标明该意见是编辑部或工作人员的意见。\n评审将从读者的兴趣度、表述适当性、目的明确性、结构清晰度以及有效性等角度评判参与作品。\n2018 社论写作金奖 Letting students know their voices\n2.新闻写作 News Writing\n，点击作品名称查看链接\n3.疫情报道（独立故事） Pandemic Coverage (Single Story)\n1. 可以是重大新闻，也可以是有关疫情对您的学校，社区或国家的影响的单个深入故事。 2. 评审将根据新闻撰写和报道标准评判参与作品。 重点将放在故事的影响，报道的深度，作品的力量和不体现见解上。\n4.疫情报道（系列故事或合辑） Pandemic Coverage (Series of Stories or Package)\n为任何媒体或多媒体产出的文章、视频、音频、图形、清单体文章。 3. 可以由多个学生完成 4. 评审将从报道的深度以及写作和/或制作的准确性角度评判作品。\n5.专题写作 Feature Writing\n2018 专题写作金奖 One Incredible Goal\n6.专栏写作 Column Writing\n1. 大众意见、个人、体育和幽默栏目都符合条件。 （评论专栏请投送至评论写作。） 2. 评审将从及时性、材料的意义、主题的恰当处理、写作风格和原创性等角度评判作品。\n7.评论写作 Review Writing\nmultiple times，点击作品名称查看链接\nDepth Reporting/Individual\n个人金奖 Letting students know their voices\n团队 In-Depth Reporting/Team\n1. 参与作品应涵盖一个有意探索的领域，以纠正现有的弱点和/或更好地告知公众。 2. 参与作品将被从主题的质量，来源的使用，报道的彻底性和写作风格等角度来评判。 3. 团队作品：参赛作品可以是包含在单个出版物或网站中的一系列故事。 （作品可以来自多个学生）\n10.体育专题写作 Sports Feature Writing\n1. 在报纸、新闻杂志或网上发表的有关体育主题的文章 - 可以进入此类别。 2. 体育专栏应投送至专栏写作中。 3. 参赛作品不仅限于高中体育报道。 评委将考虑写作风格和质量、准确性、兴趣以及主要受众的关联性。 赛事故事应进入第 11 类 Sports Game/Event Coverage。 2017-2018 体育新闻写作金奖 Nevertheless, she persisted\n11.体育比赛/赛事报导 Sports Game/Event Coverage\n1. 这类故事通常是在线上学校媒体的截止日期或第二天在当地报纸或当地新闻网站上发表的故事。 2. 参赛作品的评判标准包括报道的深度、语言的准确性、没有陈词滥调，以及在重述赛事、比赛、运动会或锦标赛中包含的人物故事。\n12.人物写作 Profile Writing\n1. 包括对新闻人物、体育人物、学生、教师、学校工作人员或社区成员的个人描写。 2. 评审将从人情味、报道深度、组织性、写作风格和形式等角度评判作品。 2017-2018 个人传记写作金奖 Transitioning through high school\n13.政治写作 Political Writing\n1. 校董会选举和问题的报道，学校、地方、州和国家的政治运动和结果，及带有政治色彩的事态发展符合此类别。 2. 评委将从主题、影响、时效性、故事性、写作风格和形式等角度评判。\n14.健康，科学和农业写作 Health, Science and Agricultural Writing\n有关健康、科学或农业主题的新闻和专题报道。\n评审将从主题、影响、故事性、写作风格和形式等角度评判作品。\n2017-2018 健康、科学与农业写作金奖 Got goats?，点击作品名称查看链接\n15.气候和环境写作 Climate and Environment Writing\n1. 有关气候和环境的新闻和专题报道。 2. 评审将从新闻的社区影响力、故事讲述方式、写作风格和形式等角度进行评判。\n二、视觉/摄影类 VISUALS/PHOTO 作品提交要求：\n高分辨率的照片\n少于200字的英文篇幅说明有关图片所表达的主题\n16.新闻专题摄影 News Feature Photography\n可以是新闻或专题报道配的照片。\n从适当性、构图、印刷质量和兴趣度等角度进行评判。\n请提交一个文件夹，其中包含一张JPG格式的原始图片 以及 一个网址（URL）或 一个PDF格式的页面文件（展示照片在页面或是网页中如何使用） 。\n17.体育动作摄影 Sports Action Photography\n1. 这是一张动作运动照片；\n2. 评审将从适当性、构图、印刷质量和兴趣度进行评判。\n3. 请提交一个文件夹，其中包含一张JPG格式的原始图片 以及 一个网址（URL）或 一个PDF格式的页面文件（展示照片在页面或是网页中如何使用） 。\n2017-2018 体育新闻摄影金奖 Point Taken\n18.运动反应摄影 Sports Reaction Photography\n这是一张展示运动员、球迷/粉丝、官员或其他人在学校体育赛事中反应的运动照片。\n评审将从适当性、构图、印刷质量和兴趣度进行评判。\n19.照片插图 Photo Illustration\n可以是一张照片和/或图像，为了讲述一般主题或运动主题故事而使用。 照片应该是显而易见的，并且在讲故事时具有艺术价值；\n评委将关注设计趋势、有效的视觉传达、创造力、适当性、构图和兴趣度。\n2017-2018 金奖作品 Yellow Truck\n17.照片幻灯片 Photo Slideshow\n参展作品是一个发表在学术或专业网站上的幻灯片，用照片的形式讲述新闻或专题故事，包括体育运动。\n评审将从展示、节奏、故事讲述技巧的使用（包括标题、字幕和/或音频等的使用）、创造力、观众兴趣度以及照片质量、框架和构图等角度进行评判。\n可以由多个学生完成\n请以PDF格式或是网页的形式提交。\n18.人物肖像 Profile Portrait\n一个人或一群人的照片，摄影师为了展示这个人或这群人的重要特征而拍摄。\n可能会涉及一个相关个人资料故事。\n三、视觉/设计类 VISUALS/DESIGN 作品提交要求：\n每幅作品需包含一个标题\n照片需要提供少于 200 字的英文篇幅说明有关图片所表达的主题\n22.社论漫画 Editorial Cartoon\n1.可以是单个漫画或连环漫画。\n2.评审将从原创性、读者兴趣度、恰当性、是否体现主题和有效性角度评判。\n2017-2018 金奖作品 Fishing for Compliments\n23.信息图形设计 Informational Graphic\n该类型图片需要清晰的传达数据或信息。图形由单个信息图组成。\n评委将重点关注创造力，信息表达的准确性和实效性，以及对受众人群的影响力。\n24.新闻杂志封面设计 News Magazine Front Page Design\n该类作品为新闻杂志的一个封面/头版。\n评委将重点关注设计趋势、布局有效性、读者友好度、创意和兴趣度等几方面。\n2017-2018 封面/首页设计金奖 This is Meg\n25.报纸头版设计 Newspaper Front Page Design\n该类参展作品为报纸的单个封面/首页。\n评委将重点专注于作品的设计趋势，信息的有效布局，读者友好度、创意和兴趣度等几方面。\n26.专题页面设计 Features Page(s) Design\n该类作品可以是一个专题内页页面，或是报纸或新闻杂志的两页跨页。\n评委将重点关注设计趋势、布局有效性、读者友好度、创造力和兴趣度等。\n2017-2018 专题新闻页设计金奖 Dash of Cranberry\n27.体育页面设计 Sports Page(s) Design\n该类作品为报纸或新闻杂志体育版面的单页或两页跨页。\n评委将重点关注设计趋势、布局有效性、读者友好度、创造力和兴趣度等。 2017-2018 体育新闻页设计金奖 The return of OShaan Allison\n23.社论页面设计 Editorial Page(s) Design\n该类作品为报纸或新闻杂志社论或意见版面的单页或两页跨页。\n四、多媒体类MULTIMEDIA 作品提交要求：\n请上传清晰的视频与音频\n制作要求：音频播客需为个人作品，其他类别个人和团队制作均可\n时间要求：新闻、体育视频1分15秒-3分钟为宜；专题视频2-4分钟为宜；纪录片不少于5分钟；播客时间不超过15min，新闻节目5-30分钟为宜。\n29.新闻视频 Video News Package\n参与作品是在学校或专业网站上报道的单个新闻报道。\n报道突发新闻或对主要受众有重大影响的问题。\n长度可以有所不同，但一个合理的新闻报道长度通常在1分15秒到3分钟之间。\n评审将从新闻内容和产出价值角度评判作品。\n30.体育视频 Video Sports Package\n参与作品是在学校或专业网站上报道的单个体育故事。\n故事与活动、运动员、教练或对主要受众有重大影响的事件相关。\n长度可以有所不同，但一个合理的体育视频长度通常在1分15秒到3分钟之间。\n31.专题视频 Video Features Package\n参与作品是在学校或专业网站上报道的单个专题报道。\n可以报道活动、个人（学生、管理者、教育工作者）、或对主要受众有重大影响的事件。\n长度可以有所不同，但一个合理的专题报道长度通常在2分钟到4分钟之间。\n32.纪录片 Documentary Video\n一部由一名或多名学生制作的不少于5分钟的纪录片。\n可以采取任何纪录片形式，但应该是新闻相关工作，主要讲述学生故事和/或影响青少年及其社区的问题。\n可以以视频、Vimeo 或 YouTube 链接的形式提交。\n33.音频播客-单集 Audio Podcast: Single Episode\n参与作品是一个学生为学校或专业网站制作的单个播客或播客剧集。\n播客的主题学生可以任意选择。\n播客的长度可以有所不同，但是建议控制在15min以内，或更短一些。\n请以音频或外部播客主频道链接的形式提交，例如 SoundCloud 、PodBean。\n34.新闻节目 News Show\n参与作品是学生制作的用于网络、电视或学校发布的新闻节目。\n不可以是学校公告。\n节目长度可以在5 到 30 分钟之间。\n包括至少两个无缝衔接的文件。\n可以是基于工作室的，但这不是必需的。\n35.多媒体专题合辑 Multimedia Feature Package\n参与作品可以是包含各种元素的专题作品合辑。\n参与作品可以是文字、音频、视频、照片、图形、艺术和/或动画等元素的任意组合。\n参与作品主题涵盖传统新闻、体育、娱乐、健康，或任何与您的读者、观众或听众密切相关的主题。\n全球奖项荣誉徽章\n金奖 Sweepstakes winner：全球 1 个\n银奖 Second Place：全球 1 个\n铜奖 Third Place：全球 1 个\n金奖（Sweepstakes winner）：全国1个\n银奖（Second Place）：全国1个\n铜奖(Third Place)：全国1个\n2016年部分获奖学生\n中国区奖项公布时间（预计）：2023年2月底-3月\n全球奖项公布时间（预计）：2023年3月", "cc_segment": 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366, 367, 370, 371], "language": "zh", "language_score": 0.98, "perplexity": 1461.0, "bucket": "middle"} +{"url": "http://www.sh-longines.com/bjzx/104.html", "date_download": "2022-05-19T09:56:36Z", "digest": "sha1:ZVCNW6A55GFT27ZNEWAOCVFZYEIJ6VDY", "length": 788, "nlines": 12, "source_domain": "www.sh-longines.com", "title": "上海浪琴手表维修服务", "raw_content": "新款浪琴精神腕表具有复古气息\n发布时间：2021.06.03 浏览量：人 作者：北京浪琴维修\n浪琴（Longines）的新款手表让我们羡慕不已。上海浪琴维修中心介绍如今，浪琴精神以深绿色调亮相，为经典腕表设计增添了诱人的复古气息。\n1940年代的菱形小时标记可以让飞行员清楚地看到它们，再到最初为飞行员戴手套设计的超大号旋入式表冠。这些复古细节是浪琴精神具有现代感和永恒感的关键。\n浪琴表，绿色表面和棕色皮革表带\n复古的细节彰显了品牌核心的探索精神。过去，其钟表曾由Amelia Earhart，Charles Lindbergh和Howard Hughes等人佩戴。长期存在的吸引力归因于可靠的可靠性，该可靠性至今仍定义着新的零件，并以新的赛车绿色调在这里栩栩如生。\n制表师关于时间的概念\nLumiNova®米色相结合，为该模型增添了复古气息，并保持了其传统。”尽管新车型向过去致敬，但它们仍然坚定地向前看。我们采用新设计前进的方式始终意味着忠实于我们的核心价值观，并使其适应当今客户的需求。我们确保永恒的经典手表在我们的精神中走在前列。”\n浪琴咨询相关参考资料：\n浪琴DolceVita Automatic为甜蜜生活锦上添花\n浪琴（Longines）首席执行官Matthias Breschan\n上一篇：浪琴DolceVita Automatic为甜蜜生活锦上添花 下一篇：全新的绿色表盘浪琴精神是大流行后重置的完美", "cc_segment": 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3572字阅读11分54秒\n有关e租宝公司被调查的新闻在微博、朋友圈被引爆刷屏。许多人看中P2P理财的高收益，却忽视其中的风险。安全实验室监测发现，P2P网站已成钓鱼欺诈网站的重灾区，大量P2P手机理财软件也存在安全隐患。网民须小心选择P2P类理财产品。\nP2P行业现状\nP2P网贷在2007开始传入国内，2015年呈现爆发态势，成交规模已进入万亿元时代。由于行业监管未出台，P2P行业处于野蛮生长阶段，鱼龙混杂，平台上线和跑路司空见惯。\n据统计，截止今年，纳入中国P2P网贷指数统计的网贷平台有超过2500家，其中问题平台近1400家。从全国范围内看：广东、山东的问题平台数量最多，数量分别达到了163家和198家。从平台性质来看，问题平台无一例外都是民营系的。\n截止11月全国各省正常平台和问题平台数量统计\n问题平台中34%出现提现困难，51%的问题平台选择了跑路，有的平台跑路后甚至连公司员工都不知情。\n问题平台状态比例\n一般来讲，P2P平台运营出现跑路的有两种，一种是经营不善出现资金链断裂的；还有一种是纯诈骗性质的网站，骗到投资者钱财后就立马关闭网站跑路。即使是今天正常运营的平台明天就有可能倒闭跑路，那么如何识别诈骗和即将跑路的平台呢？这就先要弄清楚它的诈骗流程。\nP2P网站诈骗流程：\n很多平台上线前期会以高利率为诱饵，发布大量虚假标的，通过虚假宣传、注册返利、秒标等形式，吸引普通投资者大量资金，资金到账后便后卷款而逃。网站平台突然无法登陆，公司高管失踪，办公地点人去楼空。\n也有部分网贷平台，宣称出现投资未按时收回，说是提现困难，让投资者继续投资支持平台。而在投资者交流群，会有一些人以低价收购无法提现的账号余额，业内称之为“收草”。而实际上，低价“收草”的人和欺诈平台是合谋诈骗。\n典型网贷诈骗流程图\n诈骗手法：\nP2P诈骗网站吸收资金一般有以下几种手法。\n1、高利率吸引投资者资金\n一般的P2P网贷平台年化收益率在10%左右，而超过20%，甚至接近30%都是需要高度警惕，监测发现，有的平台网站赫然宣称有700%以上的收益率。\n5%不等的投标奖励。部分存在投机和侥幸心理的投资者很快就上钩被套牢。\n3.伪造借款项目和虚构借款人信息，并标出可观的收益率，吸引缺乏风险意识的投资者。如下图：某平台的借款项目信息说明含糊其辞，项目图片一模一样，明显是虚标或拆标。\n4.利用新投资者的资金来向老投资者支付利息和短期回报，制造一种高盈利的假象，进一步骗取更多投资者的投资。一旦平台没有持续的投资来源，整个资金链就会断裂，平台就会跑路。前段时间风靡朋友圈的“MMM金融互助社区”就是典型例子。\n除了诈骗平台蓄意骗取投资者钱财之外，P2P网贷网站广泛存在安全漏洞，极易导致黑客攻击。资金安全是每一个网贷平台应当首先保障的，而保障资金安全的首要前提是保障网站的安全。\nP2P网站由于直接牵涉投资者的资金、个人信息、银行账户等敏感信息，故其危险性比一般网站的漏洞更高。\n我们对部分P2P网站进行了抽样安全监测，目前发现有131家网站存在不同类型的安全漏洞。其中撞库攻击（40%）、信息泄漏（24%）、后台地址暴露（24%）是3个主要漏洞类型，严重危及网站的用户数据安全和资金安全。\nP2P应用的安全性\n由于智能手机的普及，很多平台开发了自己的手机P2P理财应用，方便投资者随时随地投资理财；有的平台甚至只能在手机应用上使用充值、投资、提现。\n我们抽样审计了104款理财应用，约37%存在数据明文传输问题，8%的短信校验码在客户端校验，只有24%使用了加密传输，剩下31%由于部分平台倒闭跑路或其他原因，无法访问服务器。\n●密码明文传输\n104款应用中，有部分应用直接明文发送密码、支付密码，或者仅仅只是简单的base64编码一下。\n某P2P手机应用明文传输密码及金额\n●短信验证码客户端校验\n少部分应用中的手机短信验证码居然在客户端验证(HTTP回包中带有短信验证码)，这样可以造成恶意注册，刷红包，修改任意用户的密码等严重问题。\n显而易见的风险存在于P2P手机应用中，正规P2P网贷平台对安全十分重视，那些小平台和诈骗平台根本没有实力、或者根本没花心思去提升网站安全性。以下是猎豹移动安全实验室对部分P2P类手机应用的分析结果：\n根据监测数据，2015年平均每月新增195家P2P理财钓鱼网站。这些网站生存周期较短，为了逃避拦截，通常会设置多个域名指向同一个IP地址。\n建新站的循环。\n如何识别诈骗平台\n知道了P2P的诈骗流程和手法，就可以识别一个平台是否为诈骗平台了，通常有以下几种方法。\n第一，诈骗平台的界面设计相对比较粗糙。很多诈骗平台基本是几千块钱购买一个模板，再租一个主机空间就上线了，并且通常IP地址位于境外。\n第二，宣传的收益率很高，甚至远远高于行业平均水平。\n第三，公司介绍造假，备案和注册信息造假，办公地址较为偏远，甚至根本不存在。\n第四，平台活动不断，常见日标、秒标，但标的信息含糊其辞，如资金周转等。甚至虚构借款人信息，设立虚标。\n第五，诈骗平台基本没有第三方资金托管平台。投资者注册平台帐号后可以直接投资，不要求注册第三方支付机构帐号的，可确定是没有资金托管的。\n第六，平台负责人曾有过失信记录，可登录最高人民法院网站（shixin.court.gov.cn）查询。\n第七，平台业务是否公开透明，过往业务记录是否可查询调阅。\n第八，平台涉及自融，如果平台资金被平台本身或股东挪作他用，那就是自融，涉嫌非法集资、诈骗等违法犯罪行为。\n11月23日，宏量财富将网站 www.hongliangcf.com关闭， 并把群里的一千多用户踢得一干二净。\n这家名为宏量资产管理有限公司的平台，成立时间不足三个月。该公司各种注册和资质手续均齐备，且网站也有ICP备案。注册资金为两千万元。\n据受害者称，10月份时，经过各项考察，认为平台可信，于10月23日在平台投资1万元，随后被告知该平台三名高管于11月23日凌晨跑路，大概有十几个投资者以及公司10名员工均被蒙在鼓里。据该平台同为受害者的客服主管说，至少有4000投资者，涉及金额高达2400万以上。\n即使是实地考察过的，平台有正规备案的也可能因为经营不善，资金链断裂而跑路；部分平台在经营正常的情况下，负责人也可能由于贪婪而卷款跑路，甚至连平台自身工作人员都不知情。目前宏量财富的受害者们已经建立维权群并报案。\n正规平台运作流程\n除了识别一个平台是否为问题平台，还要知道正规平台是如何运作的。像红岭创投、宜人贷、陆金所等大型正规网贷平台都会有严格的运作流程，用户的信息和资金安全都有充分保障。\n1、严格的贷前审核\n正规平台针对借款人会有严格的贷前审查，通过背景调查、借款用途调查以及个人信用风险评估等审核借款人提出的借贷需求，避免不良客户的欺诈风险。\n2、完善的贷后管理\n借款项目遇到逾期未归还借款的，平台会采取充分手段催促借款人还款，甚至采取法律手段。并且对投资者完全公开透明。\n3、充分的风险准备金\n如果投资者的投资的某笔借款出现严重逾期，平台应会通过风险准备金对投资者偿付本金和利息，分散投资者投资行为所带来的信用风险。\n4、完善的法律和政策保障\n正规平台从事业务应当是合法合规的，不进行拆标和虚标行为，每个借款项目都有合法的电子合同、财务抵押凭证等必须的文件文书。\n5、第三方资金托管和担保\n正规平台采取和第三方合作托管用户资金，不私设资金池。严格规范资金管理，并有第三方担保交易。\n6、重视平台自身和用户信息的安全\n平台网站建设充分重视安全问题，通过加密连接、防火墙、二次验证等技术手段保证数据和信息的安全。并有严格的IT管理规范，防止出现人为的安全事故。\nP2P网贷是伴随“互联网+”兴起的新生行业，目前行业监管不明，P2P行业在全国处于野蛮生长阶段。由于P2P的特性，存在投资者分散，平台不透明，资金监管缺失，借款人信息难以核实等问题，使得部分平台借机诈骗敛财，卷款跑路事件屡屡发生。另一方面，由于平台运营方对安全缺乏普遍的重视，网站的安全漏洞层出不穷，黑客攻击造成的系统瘫痪、数据恶意篡改、资金盗取等时有发生。\n对于投资者而言，面对高利率和高回报要保持理性，认真考察评估平台的真实性、安全性、专业性以及可持续性，选择可靠平台并分散投资。随时关注平台及借贷项目的最新情况，保存充值记录、借贷项目合同、客服记录等证据，方便及时维权。\n对于网贷平台方，要充分重视用户信息和资金安全，及时修复网站和应用存在的各种安全漏洞，并且对资金进行第三方托管，遭遇黑客攻击要及时联系警方处理，不能姑息和纵容。\n有利网安全性如何？是否还值得投资下一篇\n如何用信用卡赚钱？", 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DMM数字万用表34465A\nTruevolt 数字万用表34461A\n是德KEYSIGHTTruevolt数字万用表34461A6½位数的高性能TruevoltDMMS提供更高的精度\u0007\b\b\b\u0007、速度和分辨率\u0007\b\u0007。TruevoltDMM的图形功能（如趋势图和柱状图）提供了更快速的洞察力\b\u0006\b\u0007\u0006。该模型还提供了一种数据记录模式\u0007，便于趋势分析\u0006，并提供了一种数字化模式\u0007\u0005\b，用于捕捉瞬态\u0007\u0007\u0007\b\u0005。自动校准的附加功能允许您补偿温度漂移\u0007\b\u0007\u0006\u0007，这样您可以在整个工作日保持测量精度\u0005\b\u0005。\n数字万用表（DMM）34460A\n是德KEYSIGHT数字万用表（DMM）34460A6½位数的高性能TruevoltDMMS提供更高的精度\b\u0007\b、速度和分辨率\b\u0007\b\u0007\u0005。TruevoltDMM的图形功能（如趋势图和柱状图）提供了更快速的洞察力\b\b\b。该模型还提供了一种数据记录模式\u0007\b\b\b\u0007，便于趋势分析\b\u0006\b\u0005\u0007，并提供了一种数字化模式\u0007\b\u0007\u0007\u0007，用于捕捉瞬态\u0007\b\b。自动校准的附加功能允许您补偿温度漂移\b\u0007\b\u0006\u0007，这样您可以在整个工作日保持测量精度\u0005。\n台式数字万用表34450A\n使用是德KEYSIGHT台式数字万用表34450A,增强您的生产线.凭借该产品每秒高达190个读数的速度,现在可以很大程度地提高制造吞吐量.该器件可提供高度准确,可重复且值得信赖的测量,具有0.015%DCV准确度,旨在满足一般要求工业和教育需求.11种测量功能;DC电压和电流,真有效值AC电压和电流,2线和4线电阻,频率,连续性,二极管测试,电容和温度.", "cc_segment": 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"凯尔特人VS雄鹿\n本文摘要：雄鹿核心阿德托昆博急需调整周三早上，正逢五一劳动节，NBA季后赛东部半决赛G2如期首演，到时之后坐镇主场的雄鹿再度应战凯尔特人。两队在G1呈现出一边倒，凯尔特人客场以22分大胜雄鹿，不过主队调整能力出众，G2究竟是凯尔特人乘胜追击抑或雄鹿扳平一城？大家不妨拭目以待。坚信在G1大比分失利之后，雄鹿上至教练组上至每一位球员都会只想剖析比数的视频，因为显然有过于多必须雄鹿去调整的地方了。\n雄鹿核心阿德托昆博急需调整周三早上，正逢五一劳动节，NBA季后赛东部半决赛G2如期首演，到时之后坐镇主场的雄鹿再度应战凯尔特人。两队在G1呈现出一边倒，凯尔特人客场以22分大胜雄鹿，不过主队调整能力出众，G2究竟是凯尔特人乘胜追击抑或雄鹿扳平一城？大家不妨拭目以待。坚信在G1大比分失利之后，雄鹿上至教练组上至每一位球员都会只想剖析比数的视频，因为显然有过于多必须雄鹿去调整的地方了。\nG1中，雄鹿全队只有3人分数上双，亮相当中只有阿德托昆博和克里斯米德尔顿这两位全明星球员分数上双，但是他们的数据是比第一圈平均水平有所大跌，当中最重要的是阿德托昆博在凯尔特人的森严防御下对比赛影响力大幅度上升，无论是分数、篮板还是助攻上阿德托昆博都不属于自己的长时间水平。当然，要沦为顶级超强虎就要有能力超越输掉的森严防御，现今放在阿德托昆博面前的凯尔特人防线就是最差的试金石，能无法用个人能力迈过这道天堑，全凭阿德托昆博个人领悟。雄鹿在G2必须调整的不单单是阿德托昆博，球队的整体感应也是最重要的一环，在G1雄鹿整体感应命中率为34.8%，三分球命中率是33.3%，外围感应佳也令其凯尔特人更加大胆实施夹攻阿德托昆博的防守战术，现时雄鹿最必须调整的关键点要数以上。\n布罗格登和丹特迪文森佐皆因伤缺席。转入季后赛，我们又再度看见铁血绿衫军的本色，他们在第一圈拿下步行者，这4场比赛欧文就展现领袖的起到，无论是个人反攻还是串联球队都十分出众。回到东部半决赛G1，欧文之后自己的个人演出，全场夺下26分、7个篮板和11次助攻的全面数据，这位当年渴求和偶像科比布莱恩特决斗未果的孩子于是以渐渐展现偶像身上的曼巴精神。\n另外，凯尔特人在G1需要夺得如此精彩，内线球员霍福德也功不可没，他全场夺下20+11的数据，都说道季后赛是检验球星能力的舞台，霍福德在比赛中对位防御对方主将阿德托昆博，在比赛中顺利保证了对方核心，最令人印象深刻印象的是在阿德托昆博一次反击篮框中，霍福德倒数2次封盖，侧面解释一到季后赛凯尔特人又再度展现出其DNA中铁血防御的一面。凯尔特人在G1客场大获全胜在系列赛中早已占到得先机，如果需要带着两场胜利返回主场，球队就基本一只踩进东部决赛，绿衫军认同会杀掉这个机会。\n阵容方面，后卫斯玛特因伤避战。\n本文关键词：海,德,体育,官网,app,下载,海德体育,30日,NBA,推荐,凯尔特\n海德体育官网", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 64, "original_length": 4891, "line_ids": [28, 30, 31, 32, 33, 34, 35, 36, 38], "language": "zh", "language_score": 0.92, "perplexity": 1591.7, "bucket": "middle"} +{"url": "http://www.wfbiaoqian.com/a/jianceshebei/836.html", "date_download": "2022-05-19T08:52:13Z", "digest": "sha1:H5N34L5K2C5YTHUPK7PS763EXQ3AGKV2", "length": 844, "nlines": 7, "source_domain": "www.wfbiaoqian.com", "title": "法证先锋4主演都有谁 男主高安和高彦博是什么关系_海德体育", "raw_content": "法证先锋4主演都有谁 男主高安和高彦博是什么关系\n第四部未来将会重返，让很多期望再现港剧巅峰剧迷们都万众瞩目。\n故事大纲：香港法证部高级化验师高安、法医科高级医生闻家希、西九龙重案组高级专员公署温卓妍,这「灭罪铁三角」率领着精英团队,以崭新科技因应专业知识,联手合作,找出疑团布局,破案杀人命案!理性背后,亦具有各自的感性故事高安的一段悲痛回忆,纵然埋心底,仍历历在目;家希那刻骨铭心的悲伤经历,使他更加明白生命的重量、当下的难得;卓妍事业爱情两不解,竟漆原本车站得越高,跌到得越疼..热血记者徐意的经常出现,随着引发高安的过去,竟然为众人带给意想不到的冲击人生憾事,或许无法挽回;积郁恩怨,不得而知能否找出。\n的男主是高安，而前两部法政的男主都是欧阳震华饰演的高彦博，两人的职业都是高级化验师，小编庞加莱新剧里高安有可能是高彦博的后代，那么很可能会请求返欧阳震华参演高安。另外闻家希和温卓妍这两个主要人物和第一部古泽琛、梁小柔或者马帼英的原作很像，很多网友期望林文龙和佘诗曼需要重返法政4，以超过最初原班人马的重聚。\n小编较为期望欧阳胖胖能接手高安一角，因为法政3几乎是独立国家的一个故事，虽然第四部也没熟知的高彦博，但是欧阳震华、佘诗曼等人的重返对于剧迷而言也是一种法政精神的沿袭。\n本文关键词：法证,先锋,主演,都有,谁,男主,高安,和,海德体育,高彦博\n藏追爱秘笈", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 58, "original_length": 3664, "line_ids": [24, 26, 27, 28, 29, 30, 32], "language": "zh", "language_score": 0.91, "perplexity": 1815.7, "bucket": "middle"} +{"url": "http://www.wheatphoto.net/1151.html", "date_download": "2022-05-19T08:04:01Z", "digest": "sha1:CHUR2OCM4MCANGDEN7RM6UZG354G65I5", "length": 968, "nlines": 17, "source_domain": "www.wheatphoto.net", "title": "戒烟不是一件容易的事，可是我们可以变通一下 – 雾化烟线上商城", "raw_content": " 电子烟“花式”吸引青少年，如何监管亟待细化(图)\n戒烟不是一件容易的事，可是我们可以变通一下\n戒烟并非易事，但我们可以解决它电子烟展会，并找到一种相对健康的电子烟来代替香烟，这会更好吗？ iqos 电子烟刚刚冒烟，人们的这种需求利用了这种趋势。它利用不燃烧的热量来最大程度地减少烟草中的有害物质，并使人们的健康抽卷烟。今天，让我们看一下iqos 电子烟的好处。在哪里？如何使用iqos 电子烟？\niqos 电子烟有什么好处？ iqos对我的好处\n1、烟的气味很小，因此在很多场合都可以抽。以我为例，除了航班抽之外，欧洲酒店和其他地方的候机楼电子烟怎么样，各种各样的餐馆甚至是禁烟令抽，因为味道太小了，可以忽略了iqos烟弹可以用打火机油吗，所以没人在乎。\n2、没有明火或烟灰，因此扔烟头非常方便。当我找不到垃圾桶时，我抽放回了盒子，不需要带烟灰缸。 iqos 电子烟有什么好处？\n3、香烟焦油基本经过过滤，大大减少了抽吸烟者遭受的伤害，对吸吸烟者旁边的人的伤害也大大减少了。\n4、比大多数抽 烟油的电子烟更像一支香烟。 iqos 电子烟有什么好处？\n5、 iqos 电子烟有什么好处？ IQOS 电子烟也可以乘飞机iqos烟弹可以用打火机油吗，这样您就可以从飞机抽下车，节省买较轻的时间哈哈。\n如何使用电子烟？正确使用非常重要\n说实话，IQOS 电子烟 价格并不便宜。如果使用不当或意外损坏机器，您仍会感到困扰。最好小心一点，因此正确使用非常重要。我先给它。您如何使用它？\n1.打开包装盒，先取出机器，充电，然后再取出烟弹;\n2.请注意不要安装错误的侧面。内部是加热元件。烟熏面应插入此处。注意不要旋转，否则加热元件很容易损坏；\n3.按住中间的按钮，感觉到振动。此时，机器开始加热，然后等待一会儿，机器打开白灯后，可能是抽;\n4.请确保定期清洁以确保加热棒的有效性。\niqos 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"爱游戏体育官方\n简要描述：又是一年毕业季，思念在即，学子们用各种尤其的方式，将自己定格在南林，与熟知的校园合个影，与体贴的舍友说道声“妳”，与辛勤的老师道声：“感激！”用一颗奉献的心，把所有的幸福和笃信取出记忆，让岁月去溶解，让时间去难忘。...\n本文摘要：又是一年毕业季，思念在即，学子们用各种尤其的方式，将自己定格在南林，与熟知的校园合个影，与体贴的舍友说道声“妳”，与辛勤的老师道声：“感激！”用一颗奉献的心，把所有的幸福和笃信取出记忆，让岁月去溶解，让时间去难忘。\n又是一年毕业季，思念在即，学子们用各种尤其的方式，将自己定格在南林，与熟知的校园合个影，与体贴的舍友说道声“妳”，与辛勤的老师道声：“感激！”用一颗奉献的心，把所有的幸福和笃信取出记忆，让岁月去溶解，让时间去难忘。\n本文关键词：爱游戏体育app官网,我们,毕业,了,爱,游戏,体育,官方,又是,一年\n上一篇：我校举办江苏省林业类暨南京林业大学2017届毕业生专场招聘会‘爱游戏体育官方’\n兰州新区推行政务服务全年“不打烊”模", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 57, "original_length": 2826, "line_ids": [16, 19, 22, 23, 24, 35, 39], "language": "zh", "language_score": 0.95, "perplexity": 1864.0, "bucket": "middle"} +{"url": "http://www.xinchengwj.com/zhichang/", "date_download": "2022-05-19T07:47:30Z", "digest": "sha1:NUVTAIYMV6US6TR5A3GJYEBFKMQSTSDE", "length": 978, "nlines": 24, "source_domain": "www.xinchengwj.com", "title": "职场潜规则", 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"\n职场女性化妆补妆的六大基本礼仪\n当着他人面前化妆或补妆都很不雅观。在出家门前化好妆才合乎美女的礼仪。一、化妆是淑女的礼貌素着一张脸只限于私底下，在正式场合或上班时，化妆是一种礼貌。因...\n怎么才能开展人力资源工作的思路\n人力资源顾名思义就是与人进行打交道，一个企业离不开人，人便是一种资源，如何有效的利用这些资源来给公司带来效益便是人力资源管理呢？接下来小编整理了怎么才...\n来到一个完全陌生的环境，时常要进行自我介绍，自我介绍是人与人进行沟通的出发点。相信许多人会觉得自我介绍很难写吧，以下是小编收集整理的面试时自我介绍应注...\n大学生不仅要做好长期的职业生涯规划，更要做好就业准备工作。接下来小编整理了大学生就业面试前应做好哪些准备的相关内容，文章希望大家喜欢！1、了解公司概况...\n经历过面试的朋友应该都会知道，面试官一定会提的几个问题都是大同小异，在准备面试的阶段可以提炼加以反复练习，接下来小编整理了如何回答面试官常问的9个问题...\n优秀的求职者不仅希望了解面试官的想法，也想了解公司的计划以及他们在这些计划中的角色。把握好面试时向面试官提问的机会，因为你可以借机向潜在雇主展示，你已...\n面试一定要问hr哪6个问题\n一份新的工作对职场人士来说，是一个机会，也是一个挑战。做好了升职加薪，做不好，试用期都过不了，就被扫地出门。接下来小编整理了面试一定要问hr哪6个问题的...\n面试时HR最常问的几个问题是什么\n建议大家在面试结束后，把遇到的问题进行整理，多多练习，这样才能在下一次面试中展现出更好的自己，进而拿到满意的offer。接下来小编整理了面试时HR最常问的几...\n在求职面试时需要注意哪些问题\n在求职时，我们常常都会需要参加企业的面试，这是每个企业都会有的，那么求职面试注意哪些问题呢，下面就一起来看看吧！良好的精神风貌。都说人逢喜事精神爽，当...\n找工作需要能力、运气，也需要一定的技巧。有时候找工作不顺利并非全是个人工作能力问题，和你的应聘渠道、简历投递等等都有一定的关系。接下来小编整理了大学生...\n1女性职场礼仪要点\n2男性职场社交礼仪常识\n3处理下属矛盾的方法介绍\n4如何开一家成功的女装店\n5前台辞职申请书\n6女性职场礼仪禁忌\n7不可不知的职场礼仪小法则\n8职场商务的礼仪培训", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 72, "original_length": 3411, "line_ids": [23, 24, 26, 27, 29, 32, 35, 38, 41, 42, 44, 45, 47, 48, 50, 53, 63, 64, 65, 66, 67, 68, 69, 70], "language": "zh", "language_score": 0.97, "perplexity": 1811.8, "bucket": "middle"} +{"url": "http://www.xmtean.com/page3/v-1623.html", "date_download": "2022-05-19T09:36:34Z", "digest": "sha1:QYFPU5NSXQVEGREWIBYMC3XCEDU5HCVQ", "length": 4906, "nlines": 9, "source_domain": "www.xmtean.com", "title": "由于铝合金化学性质较活泼,在高温下极易氧化,因而焊接铝合金滤清器时保护气要采用高纯氩气（纯度99", "raw_content": "2008研制的新型定氮仪，它具有消解时间短、分析速度快、取样量少、操作步骤简单，以及测量结果准确等优点。广泛应用于煤炭、电力、冶金、环保、商检、教学等领域对煤和焦碳中的氮含量的测定。一定量的煤或焦炭试样，在有氧化铝作为催化剂和疏松剂的条件下，于1050℃通入水蒸汽，试样中的氮及其化合物全部还原成氨。生成的氨经过氢氧化钠溶液洗气、蒸馏，用饱和硼酸溶液吸收后，由标准硫酸溶液滴定，根据标准硫酸溶液的消耗量来计算氮含量。。\n厦门高纯氧哪家好储存于通风库房，远离火种、热源、气瓶应有防倒措施大于10立方米低温液体储槽不能放在室内。液氩是将高纯氩气压缩成液态气，储存于杜瓦罐中。液氩使用时放出的是气相的，液氩储存在杜瓦罐中会有一个自然增压的过程，当它的压力过高时杜瓦罐会进行泄压，在这个自然增压和泄压的过程中，氩气很容易就被释放了。氩气在一定的条件下会对人体造成伤害，高纯液氩如果发生泄露对人体的危害将会更大，因此在使用高纯液氩的时候一定要按照规范的程序来，如果随意的操作极容易对人体造成不可挽回的损伤，在操作的过程中切忌随意使用，随意操作。高纯液氩在运输过程中是禁止与可燃性气体同时运输的，明火和热源禁止接触液氩，只有规范化的操作才能保证。文章内容来源于网络，如有问题，请与我们联系！。\n能源　4、探头定位时必须避免阴影，人应该面向阳光，避免自身阴影落在目标物上，这样可以得到一致的测量结果野外大范围测试光谱数据时，需要沿着阴影的反方向布置测点。天气较好时每隔几分钟就要用白板校正1次，防止传感器响应系统的漂移和太阳入射角的变化影响，如果天气较差，校正应更频繁。校正时白板应放置水平。　5、不要穿带浅色、特色衣帽，如果穿戴白色、亮红色、黄色、绿色、蓝色的衣帽，就会改变反射物体的反射光谱特征。当使用翻斗卡车或其他平台从高处测量地物目标时，要注意避免金属反光，或用黑布包住反光部位。操作近红外光谱仪的注意事项　近红外光谱仪主要广泛应用于对液体状样品的化学、物理性质作定量分析，由于仪器在常规光纤中有良好的传输性，且仪器简单、分析速度快、对样品不会造成破坏、测试时对样品需求小等优点，在在线分析中得到广泛使用。　在操作近红外光谱仪的过程中要注意以下事项：　1、近红外光谱区范围为780~2526nm，是介于可见光和中红外光之间的电磁波，在检测样品前首先要了解测试光谱的范围。　2、在使用前还要对仪器进行校正，近红外光谱仪的校正相对比较麻烦，为了得出准确的数值，一般需要80个以上的代表性样品用来进行校正，这一步骤通常称为模型建立。　3、在检测过程中，首先用近红外光谱仪测定样品的光谱区，通过软件自动对模型库进行检索，选择正确模型计算待测样品质量参数。近红外光谱仪近红外光谱仪操作近红外光谱仪的注意事项_近红外光谱仪光谱仪日常使用情况　一、检查温度、湿度和防尘效果：　①温度变化大，会导致谱线偏离初射狭缝，使谱线强度发生变化，不能正常进行分析，炉前实验室要求空气调整装置连续运行，因此，开空调使室温达到一定的温度后，才能开始工作。\n2T。矩形靶结构简单，通用性很强，适于大面积镀膜。。\n电子气体厂家采用常规的氩弧焊（TIG）和惰性气体熔化级电弧（MIG）方法焊接铝合金时，容易产生气孔、焊接裂纹以及焊接变形大等问题，制约了其在工业中的应用推广与常规的焊接方法相比，激光焊接是一种功能多、适应性强、可靠性高的精密焊接方法，且易于实现自动化。由于激光高的功率密度，焊接时热输入量低，在保证熔深的基础上，焊接热影响区小，焊接变形小，激光焊接不需要真空装置，因此激光焊接具有质量高、精度高、速度高的特点。同时随着大功率、高性能激光加工设备的不断开发使得铝合金激光焊接技术在汽车制造业得到了广泛应用。本文以车用铝合金滤清器为研究对象，分析了车用铝合金滤清器焊接的工艺要点及相关影响因素。滤清器焊缝为环焊缝，接头为锁底对接，要求焊缝表观均匀美观，熔宽达2mm以上，熔深达1.5mm以上，样件如图1所示。图1样件1设备、材料及方法设备：Trumpf3001激光器和焊接头（光学配置：聚焦镜焦长为300mm、准直镜200mm、光纤芯径300μm），如图2所示；图2Trumpf激光器和焊接头材料：6系铝合金；方法：激光焊接头在固定位置不动，工件绕固定轴旋转实现环焊缝焊接，焊接过程采用高纯Ar气旁轴保护。2焊接工艺易出现的问题1、保护气吹向导致的问题：当保护气吹向与工件旋转方向同向时，即保护气后吹，因而焊接过程中保护气不能及时将待焊焊缝处空气排开，易导致焊接过程中空气的混入，从而使得焊缝极易氧化，焊后焊缝表面发黑且成形很差（如图3所示）。图3保护气吹向与工件旋转方向同向形成的焊缝形貌2、使用小内径气管导致保护范围过窄，且单位面积气体吹力过大：如当采用内径为4mm单铜管保护气保护，且样件是竖直摆放时（如图4所示），由于液态铝合金流动性较大，在保护气吹力和自身重力等因素的作用下，熔池中的铝合金易往重力方向下流，导致焊后焊缝下塌（如图5所示）。另外，小内径铜管的气体吹向面积小，气体吹力较大，也易导致焊缝成形不稳定。3、保护气不纯导致焊缝局部氧化，表面发黄：由于铝合金化学性质较活泼，在高温下极易氧化，因而焊接铝合金滤清器时保护气要采用高纯氩气（纯度99.99%），采用纯氩（纯度99.9%）保护时，由于高温焊接时气体杂质的侵入，也会导致焊缝局部氧化，甚至焊接不良，如图6所示。\n5.5之间，温度在38正负1之间，一切正常时候就可以开始其他操作。　5、在开机之后到可以测量的这段时间大约在10分钟左右，在等待过程中可以先进行式样打磨，试样打磨对于测量也很重要，如打磨偏细则测得碳含量偏低，如打磨偏粗则测得碳含量偏高，对于试样的打磨粗磨后，最*用40的砂子细磨。同一点也不能进行2次激发，否则结果也有偏差。样品表面纹路清晰，无缩孔，无砂眼则可进行测量分析，打磨好的试样表面不可以有手触摸，这是人为操作误差中常见的误差，一定要严格按安全操作规程操作。\n2008研制的新型定氮仪，它具有消解时间短、分析速度快、取样量少、操作步骤简单，以及测量结果准确等优点。广泛应用于煤炭、电力、冶金、环保、商检、教学等领域对煤和焦碳中的氮含量的测定。\n124所示，炉体是一个具有水套结构的双壁钢质圆筒前门与炉体用0型橡胶圈密封。水套结构的内壁和外壁分别用不锈钢和碳素钢制成。炉体右侧壁有三个法兰，用以安装工作热电偶校准热电偶及电离规。炉体左侧壁上也有三个法兰，一个用以安装超温控制热电偶，其余两个用盲板封住，作为检测时用。在真空室的顶部装有一个18.4kW真空密封的风扇电机，真空室底部有一个用来充中性冷却气体和调节炉内压力的气孔。在真空室的后部右侧壁上有一个与机械泵相接的排气口，在真空室前部右侧有与高真空阀连接的排气孔。在后部有控制加热室上塞盖作横向运动的气缸、安全报警喇叭和排水管、汇水槽。在底部有控制加热室下塞盖作纵向运动的气缸。在炉体左侧壁上有三根电流汇流排，引入加热室。　②加热室：加热室是由5mm厚的不锈钢板作壳体，用50mm厚的氧化铝纤维衬里，并用钼片和钼钩固定。\n上一条: 若大活塞的横截面积是S2 下一条: 化学发光定氮仪执行标准：SH/T0657－1998液态石油烃中痕量氮测定法（氧化燃烧和化学发光法）ASTMD4629－1996化学发光定氮仪", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 58, "original_length": 15444, "line_ids": [24, 25, 26, 27, 28, 29, 30, 31, 32], "language": "zh", "language_score": 0.95, "perplexity": 1782.8, "bucket": "middle"} +{"url": "https://3g.163.com/dy/article/GUF9MKE7051983MB.html", "date_download": "2022-05-19T10:12:59Z", "digest": "sha1:MI34DZU44KC36T66YP3RBH563UCEYNLM", "length": 1557, "nlines": 25, "source_domain": "3g.163.com", "title": "美股_手机网易网", "raw_content": "纳斯达克年内下跌12%，一旦美股步入熊市，A股可以免受冲击吗？\n24 07:46:04 郭施亮\n正当市场担忧美联储加息步伐会加快推进之际，美股市场已经率先开启了一轮快速下跌的行情。作为聚集着全球财富资金的美股市场，它的一涨一跌直接反映出市场资金的投资情绪与投资态度。\n步入2022年，美股市场的走势并不友好。从今年以来，仅仅过了三周的时间，纳斯达克指数累计下跌12%，标普500指数下跌7.73%、道琼斯指数下跌5.70%。与2018年和2020年3月的调整行情相比，目前美股市场的跌幅并不算很大，但习惯了长期上涨趋势的美股投资者，一下子要接受美股市场的快速下跌走势，恐怕短时间内还是比较难接受的。\n经过了这一轮的快速调整行情之后，美股市场会从牛市跌入熊市吗？\n一般来说，如果股票市场从牛市跌入熊市，起码满足两个基本条件。\n其中，市场已经持续有效失守半年线与年线的支撑位置，而半年线与年线已经成为了市场反弹的重要阻力位置。\n此外，市场指数已从最近一年的高点下跌超过20%的空间。一般而言，从高点调整20%以上或者从低点上涨20%以上，可以认为是市场行情趋势转折的参考信号之一。\n当市场同时具备上述两个条件之后，才可以认为是股市开始由牛转熊。所以，从目前的情况分析，即使纳斯达克指数已经从高点下跌了12%的位置，但距离20%的调整空间还有一些距离，所以美股市场还不能够定义为熊市。\n一旦美股市场确认步入熊市，那么A股市场会免受冲击影响吗？\n实际上，近年来全球市场的联动性正在持续增强。以最近一段时间的美股大跌走势为例，在纳斯达克指数大跌了12%的背景下，A股市场也存在着一定的分歧表现。其中，与美股下跌关联性较高的创业板指数，今年以来也出现了近9%的跌幅，目前创业板指数正考验着3000点整数关口的支撑力度。\n从这一轮调整行情分析，无论是对A股还是美股市场，基本上都存在着市场“杀估值”的走势。在高估值股票出现估值大幅回撤的背后，实际上也与美联储加息预期提前到来，全球市场资金避险情绪升温因素有关。与之相比，对中低估值，且具备较好股息率的资产，在这一轮的调整行情中，并未受到太大的冲击影响。由此可见，在美联储加息预期升温的背景下，市场资金避险意愿在水涨船高，同时具备中低估值与高股息率优势的优质资产，成为了市场资金避险的途径之一。\n从目前的情况分析，美股市场还不能够称为熊市，但距离步入技术性熊市的条件，却越来越接近。随着美联储即将步入新一轮的加息周期，过去享受着低利率红利的优势正在逐渐减退，对中高估值股票来说，支撑它们上涨的投资逻辑与估值体系正在逐渐发生变化。\n从A股市场的角度出发，虽然国内市场本身享受着货币政策相对独立的优势，且本身的货币工具箱比较丰富，从自身的风险防御能力出发，国内金融市场的风险抵御能力还是比较强的。但是，在过去一两年的时间内，市场指数也开始步入明显分歧的迹象，未来市场可能会出现市场指数波动不大，但行业板块或者上市公司股票波动率明显提升的情景，所以在2022年的市场环境下，投资回报预期也许需要有所降低，股票资产的投资也不宜过度集中于单一的行业或单一的资产。\n美股是世界两大泡沫之一，现在已到了挤泡沫的时候？\n美股处于百年牛市循环浪的Ⅴ浪尾声\n暴跌！六巨头一夜蒸发3万亿，是谁吓崩了美股？A股稳住\n跌麻了！美股创三年来最大跌幅，一夜蒸发逾14万亿\n纳斯达克公司研究：世界领先的交易所集团和市场技术提供商\n纳指暴跌4.7%！美股再现抛售潮，“恐慌模式”尚未到来？\n美股大幅回撤，机构在如何交易？\n俄公司推出coolcola，可口可乐大跌近7%\n大国之争：美股，A股，谁能笑到最后？\n大A股长期涨幅远远落后美股？真相其实是这样\n外资狂买，华尔街巨头突然全面翻多，发生了什么？", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 89, "original_length": 6265, "line_ids": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 31, 34, 37, 40, 43, 46, 49, 55, 58, 73, 79], "language": "zh", "language_score": 0.96, "perplexity": 1613.5, "bucket": "middle"} +{"url": "https://474441.com/a/IOgEHOnDG8YSGPHVR-LaResCSrX-HNS7GuP3/", "date_download": "2022-05-19T08:37:10Z", "digest": "sha1:ADUXCI43X43CUT4AMDZG3IK44EW22HCJ", "length": 400, "nlines": 14, "source_domain": "474441.com", "title": "那里可以学习做蛋糕", "raw_content": "那里可以学习做蛋糕\n 列表\n但对于大多数人来说,依然只是;所以更多的朋友们选择学习烘焙蛋糕甜品\n曲阜邹城培训蛋糕学校一山东蛋糕培训学校\n2本 “蒸”的可以做蛋糕 懒人厨房:零基础学烘焙 面包蛋糕\n我学做蛋糕\n哪里可以学蛋糕甜品\n这次做蛋糕没有放色素,也没有裱花,上面只是用草莓酱简单装饰了一下\n在等待的过程中,我们小班的同学相互交流了彼此做蛋糕的心得体会.\n想学家庭蛋糕烘焙加盟店速成中心哪家好\n敏敏nktr的烤箱做蛋糕(戚风蛋糕的做法)做法的学习照\n江西新东方烹饪学院让学生能够在真真实实面向市场的蛋糕技术的学习\n那里可以学习做蛋糕:相关图片\n474441.com/a/%E9%82%A3%E9%87%8C%E5%8F%AF%E4%BB%A5%E5%AD%A6%E4%B9%A0%E5%81%9A%E8%9B%8B%E7%B3%95/", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 110, "original_length": 5546, "line_ids": [11, 12, 21, 23, 25, 41, 47, 53, 57, 63, 67, 69, 73, 108], "language": "zh", "language_score": 0.93, "perplexity": 1683.4, "bucket": "middle"} +{"url": "https://creativemanuals.com/date/2022/01/", "date_download": "2022-05-19T09:43:22Z", "digest": "sha1:5C2C27MRKTZNPXBZFVAVIB54GCIEM35S", "length": 1633, "nlines": 26, "source_domain": "creativemanuals.com", "title": "2022 1月", "raw_content": "我永远热爱你\nby adminPosted in 2022年卡塔尔世界杯直播Tagged 打入, 那不勒斯\n打入宣布离队后的首球，因西涅手指那不勒斯队徽：我永远热爱你直播吧1月24日讯 因西涅对阵萨勒尼塔纳打入一球，他在进球后指着队徽说永远热爱那不勒斯。因西涅此前官方宣布今夏加盟美职联多伦多FC，本场对阵萨…\n德布劳内：打进全场唯一进球感觉很棒 切尔西需完美表现才能争冠\nby adminPosted in 2022年卡塔尔世界杯直播Tagged 切尔西, 德布劳内\n德布劳内：打进全场唯一进球感觉很棒 切尔西需完美表现才能争冠直播吧1月15日讯 在刚刚结束的比赛中，曼城1比0战胜切尔西。赛后曼城的进球功臣德布劳内接受了BT体育的采访。德布劳内说道：“这是一场很大的…\n利物浦犹豫了！拒绝核心续约要求，主帅公开维护，恐无缘大合同了\nby adminPosted in 2022年卡塔尔世界杯直播Tagged 利物浦, 萨拉赫\n利物浦犹豫了！拒绝核心续约要求，主帅公开维护，恐无缘大合同了对于利物浦来说，本赛季可能要把精力投放在欧冠身上了，而他们能否续约核心萨拉赫则成为了最近的热点。但有消息称，利物浦不愿意满足萨拉赫的续约要求…\nby adminPosted in 2022年卡塔尔世界杯直播Tagged 主将, 芈昱廷\n柯洁主将不敌芈昱廷 卞相壹最后时刻打勺 民生北京队戏剧性出局今天围甲季后赛继续进行，可以说目前已经进入白热化阶段。争冠组第二轮比赛中，民生信用卡主将柯洁不敌芈昱廷，而笵胤和卞相壹又双双输棋，仅陈豪鑫一…\n本来他也是队史最重要球员。第二重要的球员是？这问题如果在2015年或2016年问出，大概许多人会答是克雷。毕竟那会儿，大家…\nby adminPosted in 2022年卡塔尔世界杯直播Tagged 击败, 每个人\n科隆主帅：击败拜仁对每个人都是挑战，面对他们拿到3分非常困难直播吧1月13日讯 在对阵拜仁的比赛前，科隆主帅鲍姆加特在接受采访时谈到了对手的情况。鲍姆加特表示，球队是有机会的，并会争取竭尽全力争取优势…\n齐鲁德比又来，山东男篮最该提升的是血性！\nby adminPosted in 2022年卡塔尔世界杯直播Tagged 山东, 男篮\n齐鲁德比又来，山东男篮最该提升的是血性！令人瞩目的齐鲁德比战又来了！上轮，青岛队本赛季第二次输给新疆队，而输掉这场比赛后，青岛队遭遇了联赛四连败，目前他们的排名已经跌至第15名！苦日子，难熬。 山东男…\n吸取前车之鉴？记者点出李霄鹏跟李铁的一大区别，球迷：低调务实\nby adminPosted in 2022年卡塔尔世界杯直播Tagged 低调, 李霄鹏\n不拘一格降人才，第一次集训就一举招进52名国脚和15名助教，几乎网罗所有本土足球精英。李霄鹏此举高…\n皮奥利：舍甫琴科会率队摆脱困境 对阵热那亚吉鲁将首发\nby adminPosted in 2022年卡塔尔世界杯直播Tagged 热那亚, 皮奥利\n皮奥利：舍甫琴科会率队摆脱困境 对阵热那亚吉鲁将首发直播吧1月13日讯 北京时间周五凌晨的意大利杯1/8决赛，米兰将会面对球队传奇舍甫琴科执教的热那亚。在本场比赛之前的新闻发布会上，米兰主帅皮奥利谈论…\n2022世界乒乓球团体锦标赛确认于四月份在成都举行\nby adminPosted in 2022年卡塔尔世界杯直播Tagged 世界, 成都\n2022世界乒乓球团体锦标赛确认于四月份在成都举行国际乒联集团、中国乒协和成都世乒赛组委会共同确认2022世界乒乓球团体锦标赛的日期，该赛事将于2022 年 4 月 17 日至 26 日在成都高新体育…", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 73, "original_length": 5527, "line_ids": [9, 10, 11, 13, 14, 15, 17, 18, 19, 22, 23, 27, 30, 31, 33, 34, 35, 37, 38, 39, 41, 42, 43, 45, 46, 47], "language": "zh", "language_score": 0.95, "perplexity": 1819.0, "bucket": "middle"} +{"url": "https://cn.bullion-rates.com/platinum/DOP-history.htm", "date_download": "2022-05-19T09:08:43Z", "digest": "sha1:WMWZSTDQJPPSQXR4BZQBHYWF7K3DMXM7", "length": 195, "nlines": 11, "source_domain": "cn.bullion-rates.com", "title": "过去90天铂金之多米尼加比索(DOP)价格", "raw_content": " 过去90天铂金之多米尼加比索(DOP)历史价格\n过去90天铂金之多米尼加比索(DOP)价格\n2022/03/21 1,841.69 57,283\n2022年五月铂金的DOP价格\n2022年四月铂金的DOP价格\n2022年三月铂金的DOP价格\n2022年二月铂金的DOP价格\n2022年一月铂金的DOP价格\n当前铂金的DOP价格\n前30天铂金的DOP图表\n前1年铂金的DOP图表", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 122, "original_length": 6440, "line_ids": [28, 45, 48, 92, 93, 94, 95, 96, 97, 98, 101], "language": "zh", "language_score": 0.92, "perplexity": 1516.5, "bucket": "middle"} +{"url": "https://cn.fashionnetwork.com/news/smcp-ji-tuan-quan-li-kuo-zhang,1030670.html", "date_download": "2022-05-19T07:53:35Z", "digest": "sha1:3MEPTUZZ5CH4A3RD3FNCTP75PHXW7KU5", "length": 1610, "nlines": 11, "source_domain": "cn.fashionnetwork.com", "title": "SMCP集团全力扩张版图", "raw_content": "SMCP集团全力扩张版图\n在过去几年，SMCP集团的版图可谓快速扩张。为了维持现有的约13%的年营业额增长率（刚刚公布的第三季良好业绩对这个年度目标起到了很好的巩固作用），集团将继续在世界各地布局发展，同时研究开拓未来有发展潜力的市场。\n SMCP\n集团总裁Daniel Lalonde向FashionNetwork.com表示：“四年前，法国本土市场占SMCP集团总营业额的70%，现在则只有36%”（编者按：根据第三季总营业额2.477亿欧元计算）。集团在海外发展迅速，但在法国仍然不断扩张市场份额，这在刚结束的第三季也同样如此。集团旗下共有三大时尚品牌Maje, Sandro和Claudie Pierlot。虽然它们在法国的过去三个月的营业额录得轻微的0.1%跌幅，但今年前九个月则保持稳定的良好业绩。\nDaniel Lalonde表示：“我们与法国国家统计局的数据进行了对比，后者显示同期销售额下跌6.5%。因此，我们在法国市场实现了目标，赢得了更多的市场份额”。为此，Maje, Sandro和Claudie Pierlot的策略是继续致力于发展法国境内超过400间的门店，但就以提升质量方面为主。Daniel Lalonde认为：“具体的案例，例如关闭两间小门店，转为在大城市开设一间面积更大，位置更佳和采用全新概念的门店”。\n虽然法国本土还会开设一些“旗舰店”，但SMCP集团门店的大举扩张还是主要依赖海外市场。在过去一年，集团共开设118间新店，令其门店数量超过1400间（包括自营店和特许经营店），其中上一季就有32间新店开张。具体而言，21间新店位于亚太区，11间位于欧洲，中东和非洲区（主要是位于德国，西班牙，意大利和英国这四大主要市场，近期还宣布将在伦敦摄政街开设旗舰店），5间位于北美。\n集团已经进驻北美的两个品牌是Sandro和Maje。它们在上一季已经开设了多间门店，其中包括位于纽约洛克菲勒中心的Maje旗舰店。这个开店节奏也与集团的营业额相吻合，2018年第三季的销量与2017年第三季相比增长41.8%。这个增长除得益于新店开张之外，还受益于线上业务的快速增长。线上业务是SMCP集团在北美的最重要商业模式，占其营业额的25%之多。Sandro品牌刚开设了一个全新的网上销售点，而Maje品牌在加拿大市场则通过合作伙伴HBC (Hudson's Bay Company)百货公司的电商平台进行销售。此外，Claudie Pierlot在俄罗斯则与Aizel合作开设了网上销售渠道。\nSMCP集团是否会在北美市场全力启动发展？Daniel Lalonde指出：“我们对北美市场的业绩非常满意。增长同样非常强劲，一方面是因为在去年同一时期出现的飓风对销量影响巨大，但另外一方面则是我们过去的投资现在开始出现回报”。集团预期第四季北美市场将会保持这个良好趋势，同时继续开设新店。另外还计划“中期”内在该市场推出Claudie Pierlot品牌。\n不过这不会在近期内立即实现。因为这个最新加入集团的品牌（编者按：2009年）刚开始进驻亚洲这个全新市场。Claudie Pierlot在最近几个月开设了亚洲的首批新店，首先是在香港，紧接着是近期的上海和北京。\nSMCP集团目前是由中国山东如意集团控股持有。集团现在在大中华区共有153间门店，它计划在未来几年能够将这个数字增加两倍，每年保持35至45间新店的速度。\n在亚洲区，SMCP集团还同时着眼于其它邻近市场。例如它已经表示有意开掘日本市场。另外它和其它公司一样也同样关注东南亚。目前它在当地只进驻了新加坡市场。Daniel Lalonde表示这是集团的“中期目标”。他说：“目前而言，美国和中国的商机是我们短期内希望抓住的，因为这些商机在我们看来最为巨大”。", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 159, "original_length": 7406, "line_ids": [103, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117], "language": "zh", "language_score": 0.97, "perplexity": 1471.1, "bucket": "middle"} +{"url": "https://help.airdroid.com/hc/zh-cn/articles/4405348111643-%E5%A6%82%E4%BD%95%E5%B0%86HEIC%E5%9B%BE%E7%89%87%E4%BC%A0%E8%BE%93%E5%88%B0%E7%94%B5%E8%84%91%E4%B8%8A-", "date_download": "2022-05-19T08:49:47Z", "digest": "sha1:V4IAX7WLAC24WJK5PIM4AF77ADF2YYNG", "length": 1114, "nlines": 25, "source_domain": "help.airdroid.com", "title": "如何将HEIC图片传输到电脑上？ – AirDroid帮助中心", "raw_content": "iOS AirDroid\n如何在电脑上浏览HEIC图片？\n如何使用AirDroid剪贴板功能将电脑文字传输到iPhone？\n如何管理iPhone的文件？\n如何找到iPhone上的文件？\n如何从电脑给iPhone传输视频和照片？\n如何将HEIC图片传输到电脑上？\nAirDroid 个人版 iOS端介绍\n随着互联网的普及，用照片随时随地分享生活成为很多人的日常。自iOS 11 Beta以来，使用iPhone所拍摄的图片默认是HEIC格式。由于HEIC格式上线时间较短，很多电脑都不支持打开该格式的图片，这给使用iPhone拍摄照片的人们带来了烦恼。\nHEIC是什么？\nHEIC是iPhone图片的一种格式。与JPG格式相比，HEIC格式占用空间更小，画质更加无损。但对电脑的兼容性较差，除非您使用的是macOS High Sierra(10.13)，Windows10 RS4或者更高版本的电脑，否则将图片传输到电脑上之后还需要将图片格式转换成jpg或者其他电脑兼容的格式才能在电脑上将其打开。\n如何将HEIC格式的图片转换成JPG？\n选择“兼容性最好”，设置后图片都会默认保存成JPG格式。这种苹果推荐的HEIC转换成JPG格式的方法虽然直接，但是存在一个最大的问题是：会导致图片占用更大手机内存，而清晰度反而降低了。\nAirDroid在传输图片过程中可以自动将HEIC格式的图片转换成JPG格式，摆脱了使用软件转换图片格式的繁复步骤，让传输HEIC图片快速便捷！\n将HEIC图片传输到电脑最好的方法！\nAirDroid对文件格式和电脑类型没有限制，您可以用AirDroid将iPhone所有格式的文件、图片传输到各种类型的电脑上（Mac、Windows、Linux、Chromebook）。此外，AirDroid在传输图片过程中不会压缩图片，能完美地保持图片的清晰度，是传输图片的理想应用！\n下面是具体步骤：\n1. 在App Store中搜索并安装“AirDroid”。\n2. 打开AirDroid后，前往 ，找到并开启“接收方不支持HEIC时自动转换为JPG”选项。\n现在，当您使用AirDroid从iPhone传输HEIC格式图片到电脑时，电脑接收到的图片将自动转换成JPG格式。\n下面是如何使用AirDroid传输文件的教程：\n如何使用 AirDroid 个人版在电脑和手机之间互传文件？\n文件传输与管理的常见问题\n如何使用 AirDroid 个人版好友功能与好友传输文件？\n通过 AirDroid 个人版下载文件提示下载失败怎么办？", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 51, "original_length": 3467, "line_ids": [4, 6, 7, 8, 9, 10, 11, 12, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 35], "language": "zh", "language_score": 0.97, "perplexity": 1928.7, "bucket": "middle"} +{"url": "https://huacheng.gz-cmc.com/pages/2021/10/15/38f7603d5cc04dc2ba96311956834cbe.html", "date_download": "2022-05-19T08:52:14Z", "digest": "sha1:52KEGX22ZLKZZY5X5UTRIIU3GLZBJQAW", "length": 509, "nlines": 12, "source_domain": "huacheng.gz-cmc.com", "title": "吕田镇老兵在“家门口”享受优质医疗服务", "raw_content": "吕田镇老兵在“家门口”享受优质医疗服务\n来源：微社区e家通从化吕田镇\n10月13日上午，广东省第一荣军医院医护人员一行14人前往吕田镇新时代文明实践广场开展“关爱功臣 送医送药”医疗巡诊活动，为吕田镇优抚对象送医送药送健康，让优抚对象在家门口就享受到优质贴身的医疗服务。从化区退役军人事务局相关负责人到场指导。\n活动现场，医护人员详细询问每位优抚对象的基本情况，经过初步诊断后，为他们进行血压、心率等检查，并根据检查结果和病情状况，免费为患者发放常备药品和治疗药物。\n▲活动现场。\n▲现场严格落实测温、登记等疫情防控措施。\n▲医护人员为优抚对象进行身体健康检查。\n▲医护人员为参加活动的优抚对象送上常备药品。\n吕田镇相关负责人表示，本次活动让辖内优抚对象在家门口就可以享受到专业医护人员的贴心服务和规范的康复治疗指导，让优抚对象切切实实感受到党和政府的关心和温暖，营造了浓厚的“拥军优抚、爱军爱民”氛围。\n据统计，本次活动共为吕田镇48名优抚对象提供送医送药服务。下一步，吕田镇将按照上级要求，结合实际工作，通过常态化关爱优抚对象的方式，增强他们的获得感、幸福感、安全感。\n【记者 吕田君】\n信息及图片来源：吕田镇政府", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 15, "original_length": 1595, "line_ids": [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14], "language": "zh", "language_score": 0.89, "perplexity": 1477.2, "bucket": "middle"} +{"url": "https://news.mydrivers.com/1/667/667116.htm", "date_download": "2022-05-19T09:25:26Z", "digest": "sha1:KIXYG4XU2KZGW3R7MGKMKWTLAWK3IINH", "length": 598, "nlines": 10, "source_domain": "news.mydrivers.com", "title": "科技改变未来", "raw_content": "最完美V12敞篷超跑\n13 10:50:34 出处：快科技 作者：陈驰 编辑：陈驰 评论(0)\n#法拉利#812 GTS\n812 GTS在国内正式亮相，同时法拉利中国也公布了新车的官方指导售价为530万元起，现已开启预定。\n812 Superfast作为法拉利新一代GT超跑旗舰，其继承了F12修长的车身比例，整体外观堪称无可挑剔。\n敞篷版车型812 GTS有着和812 Superfast相同的设计，修长的发动机舱是法拉利旗舰V12跑车的标志性元素，引擎盖上的诸多开孔，以及攻击性极强的前包围设计，均是在为整车的空气动力学服务。\n硬顶敞篷是812 GTS最有魅力的地方，其采用电动折叠敞篷，当车速小于45km/h时，顶棚开启与闭合仅需14秒。此外，四个立体的圆形尾灯、双边共四出排气、向后翘起的小鸭尾以及那跃马标志都赋予了车辆气宇非凡的气质。\n100km/h加时间小于3秒，极速可达340km/h。\n开着法拉利最强量产敞篷车型，游走在都市CBD的夜幕繁华之间，听着V12的轰鸣声，享潺潺微风过耳，想必该车在中国又是被“疯抢”的节奏。\n责任编辑：陈驰", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 124, "original_length": 6089, "line_ids": [6, 7, 8, 17, 18, 19, 20, 21, 22, 26], "language": "zh", "language_score": 0.87, "perplexity": 1579.2, "bucket": "middle"} +{"url": "https://news.mydrivers.com/1/830/830640.htm", "date_download": "2022-05-19T09:17:16Z", "digest": "sha1:4F3T5W7LRQ5W3DS7APSGCOVYYCZMAAJS", "length": 643, "nlines": 18, "source_domain": "news.mydrivers.com", "title": "科技改变未来", "raw_content": " 文章详情\n“懒癌”患者福音！小爱同学“小爱朗读”功能即将上线\n#小爱同学#小米\n有不少用户都遇到过，打开手机看着大段文字“懒癌”发作，不想阅读；或是开车通勤、做家务等不方便阅读手机上文字的情况。\n对于遇到过这些情况的用户来说，小米目前上线测试的“小爱朗读”功能，或许会是一个可选的解决方案。\n根据介绍，该功能能够将屏幕上的文字信息转换为音频进行播放，从而让用户更快、更便捷的获取信息。\n不过，需要注意的是，现阶段该功能只支持微信公众号、今日头条、知乎、百度、内容中心等软件，对于想要在其他软件平台使用的用户来说，或许还需要一段时间的等待。\n目前，小爱朗读功能已经在小米社区开启内测招募，招募截止日期为5月9日，对该功能有兴趣，想要尝试的用户可以前往社区软件报名申请。\n话题标签：小爱同学小米语音助手\n#小米#小米12 2022年05月06日 16:19\n小米MIX 4 12+512G顶配版到手价4699元：潜望镜头+全陶瓷机身 温润如玉\n#小米#小米MIX 2022年05月03日 00:0\n4月安卓手机好评榜：前三名小米拿下两席\n#小米#好评 2022年05月02日 22:33\n39元起 小米手机延保、碎屏保限时特惠：换屏最高省1300元\n#小米#保修 2022年05月02日 20:31\n4200元！老外评价小米POCO F4 GT：绝对的性能怪兽 令人印象深刻\n#小米#POCO 2022年05月02日 10:24", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 158, "original_length": 7317, "line_ids": [5, 6, 8, 18, 19, 20, 21, 22, 25, 50, 51, 52, 53, 54, 55, 56, 57, 58], "language": "zh", "language_score": 0.97, "perplexity": 1601.0, "bucket": "middle"} +{"url": "https://news.mydrivers.com/tag/qiya.htm", "date_download": "2022-05-19T09:13:49Z", "digest": "sha1:6ZLLOHRPR56GDILRHSNPPMYQA54OW6T2", "length": 2444, "nlines": 42, "source_domain": "news.mydrivers.com", "title": "快科技", "raw_content": "#起亚\n有关“起亚”的最新话题，\n最个性的韩系车！起亚发新车：配思域同款“HDMI”排气筒\n要说哪款韩系车最具个性，那么起亚Soul（秀儿）一定榜上有名。 近日，起亚汽车官方发布了海外版新款Soul（国内秀尔）的官图，新车为中期改款，外观和配置进行了小幅升级，同时动力上也有所变化\nJ.D. Power发布2022汽车可靠性研究 路虎最差、第一没想到\n近日，J.D. Power（君迪）发布了2022美国汽车可靠性研究报告，来自韩国的起亚在所有汽车品牌中排名第一，同样来自韩国的捷尼赛思在高端汽车品牌中排名最高，而保时捷911则是蝉联了最可靠车型的称\n800V超充堪比加油！起亚首款纯电动车价格公布：21.1万起\n近日，起亚官方发布了2022款EV6在美国市场的价格，新车起售价为4.09万美元，可享受当地7500美元的优惠政策，实际起售价约为3.34万美元（约合人民币21.1万元）。 作为起亚旗下首款出电动车型，\n2.97亿元收购东风股权 起亚在华合资公司新名称将公布！\n近日，据相关消息，起亚将在4月北京车展开幕前夕公布新股比结构下的新公司名称以及全新品牌战略。 有知情人士透露称，新公司或被命名为“悦达起亚”或者“起亚中国”。\n韩系车质量“暴雷” 超300万辆车被查\n虽然韩系车在国内的存在感越来越弱，但是在全球市场中，韩系车的质量稳定性却是“封神”般的表现。 权威调研机构J.D.Power发布的2019年美国新车质量研究报告显示，冠亚季军被韩系车包\n韩系车败退中国 两年巨亏73亿！中国车企全面退出\n12月18日消息，据新浪财经报道，随着东风集团25%股权转让的完成，东风悦达起亚（DYK）将由三方合资变为悦达、起亚二方持股。 12月17日，上海联合产权交易所官网显示，DYK 25%股权转让项目已摘牌\nSUV+MPV结合体！起亚新车大亮：从未见过的设计\n日前，据媒体报道，起亚官方正式发布全新Carens佳乐的预告图，新车将于12月16日正式发布，是一款专为印度市场打造的车型。 Carens作为紧凑型MPV有过三代车型，在2018年全球范围退市，其第二和第\n销量暴跌 巨亏73亿！中国车企全面退出合资公司\n东风退出东风悦达起亚一事，终于有了明确消息。 日前，据媒体报道，上海联合产权交易所挂出了东风悦达起亚汽车有限公司25%股权的转让信息，转让价格为2.97亿元，转让方为东风汽车集团有限公司。\n销量惨遭腰斩！东风汽车全面退出韩国合资品牌：回应来了\n继去年的雷诺之后，东风集团可能要与旗下的第二个合资品牌分道扬镳了。 近日，有媒体报道称，一位接近东风集团的消息人士爆料，东风汽车最快将于11月在产权交易所挂牌转让其所持东风悦达起亚25\n韩国一大车企在华销量遭腰斩！网传东风汽车全面退出\n11月4日，有媒体报道称，东风汽车集团有限公司将在明年退出东风悦达起亚，如果不出意外，东风汽车所持有的股权或由现代起亚接盘。 在此之前曾有媒体从东风悦达起亚内部获悉，三方股东已经开启了\n现代起亚官宣开发固态电池！续航彻底完美了\n日前，据外媒报道，现代起亚宣布与一家名为Factorial Energy的美国电池研发制造公司合作开发车规级固态电池，未来，固态电池或许将会成为电动车市场中的一股新风潮。 根据联合开发协议，现代、\n起亚嘉华高价上市！总经理：扔掉性价比路线 后期不降价\n33.99万元，全系搭载2.0T发动机，匹配8AT变速箱。 虽然车辆的空间、性价比优势明显，但是与丰田赛那相比，品牌力在\n丰田赛那最强韩系对手来了！28.89万起 全系2.0T+8AT\n随着三胎政策的放开，国内的MPV市场重新火热了起来，合资品牌也同样看中了这一市场，丰田赛那、起亚嘉华也陆续进入国内。 今日晚间，起亚嘉华正式在国内上市，新车共推出4款车型，售价区间分别\n告别27年椭圆形KIA！起亚全新SUV上市：LOGO变了\n15.78万元，共推出5款车型，动力上提供2.0L+6AT以及1.5T+7DC可选。 据了解，起亚智跑Ace车型是一款专门针对中国市场所打造的\n起亚1.5T智跑竟有2千马力 前亚洲冠军小姐姐直播现尴尬一幕\n名人或者小姐姐跨界带货，已经成为了当前各行各样都在积极学习的“财富流量密码”，只不过，如果有时候专业跨界过大的话，也有可能会闹出尴尬的笑话。 今日，有网友上传的一段直播带\n中国上市前夕！韩国第一汽车集团大规模召回：71.4万辆\n5月31日消息，据央视财经报道，韩国国土交通部27日宣布，将召回现代、起亚等汽车厂商在韩国销售的共计71.4万辆问题汽车，其中现代和起亚的车辆占比高达九成。 此次召回的71.4万辆问题车辆中，现\n3排7座 对坐似高铁！别克GL8最强对手终于国产了\nGMP打\n突发！起亚美国工厂二次停产\n进入2021年以来，“缺芯”就成为了诸多车企绕不开的拦路石，不少车企们只能通过停产或减产进行现阶段过渡。 日前，据外媒报道，由于全球芯片短缺，起亚汽车公司决定将其在美国的工厂\n起亚被卡脖子！无奈停产\n自2020年年底汽车用芯片短缺危机出现后，大众、福特、通用、奔驰、日产、本田、保时捷等车企纷纷表示面临芯片荒，其中部分企业因芯片供应紧张已经宣布短期减产计划。 更糟糕的是，这场波及全球\n一根保险丝能把发动机烧了 韩系车被“坑惨”\n韩系车在国内的存在感越来越低，但是作为全球车企，韩系车的表现仍是可圈可点。并且其质量稳定性也是“封神”般的表现。 权威调研机构J.D.Power发布的2019年美国新车质量研究报告显示", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 165, "original_length": 8219, "line_ids": [5, 6, 11, 12, 17, 18, 23, 24, 29, 30, 35, 36, 41, 42, 47, 48, 53, 54, 59, 60, 65, 66, 71, 72, 77, 78, 83, 84, 89, 90, 95, 96, 101, 102, 107, 108, 113, 114, 119, 120, 125, 126], "language": "zh", "language_score": 0.98, "perplexity": 1471.2, "bucket": "middle"} +{"url": "https://open.kingmembers.com/article/view/id/439.html", "date_download": "2022-05-19T09:11:17Z", "digest": "sha1:Z6KI463IKWSGE5WFLQJZTGIZYTEWE54L", "length": 1904, "nlines": 62, "source_domain": "open.kingmembers.com", "title": "满客通门店营销服务平台", "raw_content": "营销要落地，方案要做细\n这不经让我发问：为什么一个对作品秉承精益求精态度的艺人，会遭受那么多人的抨击呢？\n今天本想在抖音上找几部周星驰的电影，没想到刷出了很多关于周星驰人品的评论，还有很多人说周星驰是“片场暴君”。\n为了追求镜头的效果，经常是一个上午就拍一条，很多人都受不了他的这种追求完美的性格。\n到底真的是因为周星驰过于苛刻，还是大家不愿意追求极致？可是，为什么周星驰的电影有那么多人愿意看呢？\n拍的不好，还会有这么多人看吗？每年都有很多的影帝，而一代人就只有一个周星驰。\n不得不说，周星驰对电影的完美追求，是我们营销人值得学习的地方。\n至于他在电影圈内的口碑，姑且不论。我只想站在一个营销人的角度，谈一谈对精益求精的理解。\n方案想落地/一定要做细\n在给企业做营销方案的过程中，我有深刻的体会，很多时候我们不能仅仅只是给一个框架，或者是理论依据，而是要细到一个普通员工拿到手里都会执行。\n所谓方案，我的理解就是把某个人头脑里面的东西，用文字的形式呈现出来，并展示给其他人看，并且希望其他人按照这个方案设计的初衷去准确落地。\n从我对方案的定义当中，我们可以看出来，方案的使命是落地。不能落地的方案是没有意义的。\n我这么说，并不是说方案本身不能落地，而是这中间存在理解和执行的偏差。\n就像“传话游戏”一样，大家都玩过。第一个人开始说一句话，口口相传，直到最后一个人的时候，可能早就变了样。\n这是因为在传输过程中，信息被扭曲。\n如果一套营销方案设计出来，本身就不够细致，再经过几道转手，最终执行出来的效果可能连10%都没有，甚至是南辕北辙。\n很多人说，这个方案不能落地啊，那个方案可以落地，实际上，方案本身没有落地的区别，唯独出现偏差的，就是方案设计的细致度。\n全书就是一个大方案，你说，孙子兵法能不能落地呢？也许你落不了地，但是，在这个世界上，用孙子兵法落地的企业家不胜枚举。\n这充分说明了，并不是孙子兵法不落地，而是你在使用孙子兵法的时候，缺乏更多细致且有针对性的细节，你自己又无法细化，所以，自然就很难落地了。\n而我们很多人在给自己门店设计方案的时候，往往都是只有一个大框。\n甚至有些老板对员工的交代就只有一句话：国庆搞个活动吧！（剩下再也没有其它的交代了）\n像这样的方案能够落地都见鬼了......\n要细到什么程度\n那么，既然方案设计要细致，究竟应该细致到什么程度呢？\n这就像本文开篇提到的：周星驰被很多人理解成为“片场暴君”，但事实上，周星驰的电影却很多人喜欢看，我本人也是星迷。\n又想马儿跑，又不给马儿吃草，这本身就是一个悖论。又想大家能落地，又不能给到大家细致可执行的方案，这怎么可能呢？\n所有的“大道理”都应该要变成具体可执行的“小动作”，这个道理才有意义！\n这里给大家分享一下我做方案的时候一般会涉及到那些细节：\n1、目标人群是什么人？\n2、要实现的目的是什么？\n3、影响目的达成的主要矛盾是什么？\n4、分几个阶段来操作实施？\n5、每个阶段的核心定位是什么？\n6、用到什么具体策略？\n7、这个策略的原理是什么？\n8、这条策略的预算有多少钱？\n9、使用这个策略具体有什么步骤？\n10、每一步的核心关键在哪里？\n11、要用什么文案？图片？话术？\n12、需要制作什么物料？\n13、物料涉及什么内容？图案？\n14、需要用到什么软件？\n15、策略执行有什么注意事项？\n16、容易失败的地方在哪里？\n17、具体的衡量指标是什么？\n18、要求完成指标的时间是多久？\n19、万一这个策略失效用什么后补？\n20、谁来负责这件事的执行？\n21、谁来负责这件事的监督？\n22、所有人的职责是否清晰？\n23、完成了有什么奖励？\n24、完不成有什么惩罚？\n25、如何确保奖惩机制有效运行？\n26、如何做任务进度的日报和周报？\n27、如何及时跟进与辅导？\n28、完成后该怎么做复盘？\n基本上，这28个角度，你应该够用了！\n如果，按照这种方式设计出来的方案，在执行中，基本上可以让你节省很多的时间，提高落地效率！\n如果，你设计出来的执行方案，没有上述28项内容，可以说，本来就容易信息失真的传递过程，会让整个方案的执行，到处漏风，最后全部变样。\n片场暴君有的时候真的值得我们去学习，尤其是打磨产品，或者是营销人设计方案的时候，我认为，每一个营销人，在设计方案的时候，都应该做一个片场暴君。不仅是营销，其实工作中，生活里的每一件事，难道不都应该认真去做才能有结果吗？\n如果，你正在抱怨当下所做的事情没有结果的时候，也请你停下来问问自己，这件事，我真的较真了没有？\nTags: 满客通宋英君营销方案", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 105, "original_length": 9013, "line_ids": [6, 7, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 65, 66, 67, 69, 70, 78], "language": "zh", "language_score": 0.99, "perplexity": 1704.1, "bucket": "middle"} +{"url": "https://www.aizhuanwu.com/tg/2899.html", "date_download": "2022-05-19T08:31:45Z", "digest": "sha1:A5XIK3ILHQDI3LIF5CXVNL3IVWK7QLMX", "length": 544, "nlines": 9, "source_domain": "www.aizhuanwu.com", "title": "爱转屋", "raw_content": "快来试试这几款赚钱多的软件\n真正能赚钱的软件游戏：快来试试这几款赚钱多的软件\nshouzhuan 2021年08月21日 12:52\n真正能赚钱的软件游戏？现在谁的手机上没有几款游戏呢？小编自己平时也有打游戏的习惯，而且现在打游戏是真的可以赚钱的。比如王者代打、吃鸡代打等等，不过在我看来大部分需要具备的技术。而且玩游戏真的是一件很辛苦的事，如果一直赢还好说，要是一直输那心态绝对就爆炸了。\n小编虽然玩的也很好，但这个方式赚钱我是真的做不了。不过我现在依然可以用手机赚钱，而且现在手机赚钱已经成了大势所趋，如果你不会那就有点说不过去了。下面爱转屋就分享这几款赚钱多的软件，如果你还没试过，那就快来操作看看吧。\n结语：事实上用手机软件来赚钱是现在真正能赚钱的行业，主要是现在手机的普及度真的太高太高了。只是游戏的方式不能让我们赚到钱，但不要灰心，我所分享的这几个方式都能赚钱，相信只要你每天通过这些软件来做，一定能有个不错的收入。\n温馨提示：文章内容系 shouzhuan 个人观点，不代表本站对其观点赞同或支持，若侵权请联系作者进行删除。\n版权声明：本文为原创文章，版权归 shouzhuan 所有，欢迎分享本文，转载请保留出处！\n标签： 真正能赚钱的软件游戏", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 120, "original_length": 6261, "line_ids": [16, 17, 18, 20, 21, 37, 39, 40, 41], "language": "zh", "language_score": 0.98, "perplexity": 1642.6, "bucket": "middle"} +{"url": "https://www.517xw.com/info/2015061510/201506051455226039/202105311405494983.shtml", "date_download": "2022-05-19T08:06:40Z", "digest": "sha1:6MCCPV3NWEQABCDLAXDYGYIZ4MG65VYY", "length": 670, 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在线报名\n我们将真诚为您服务\n几十万学员通过济南小吃培训学校收获了自己的财富事业！赶紧拿起电话咨询吧！\n*您的真实姓名，请使用中文 姓名：\n*电话是与您联系的重要方式，正确填写 手机：\n填写电子邮箱，会收到详细资料 邮箱：\n没有地址将无法为您免费邮寄资料 地址：\n您可以选择下列快捷留言！\n报名要带什么资料？\n请问我所在的地区能加盟这个项目吗？\n作为此项目加盟商能享受到哪些支持？\n请问投资此项目所需的费用有哪些？\n12 13:21:05 报名：区域特色类\n备注：中华饮食博大精深，源远流长，经历几千年发展，推出六万多种传统菜点，两万多种工业食品，五光十色的宴席和独具地方特色的风味流传，被誉为“烹饪王国”。\n12 13:20:10 报名：区域特色类\n备注：新东方烹饪学校收费是按照学生选择的不同专业来收费的，我们的专业全部以实际操作为主，教学都是真材实料，所有实习菜肴均为现在酒店、宾馆的流行、时尚菜。\n12 13:19:42 报名：卤菜基础类\n备注：我们是周一到周五全日制上课，周末休息。全天课程理论加实践学习，每天晚自习有老师辅导。短期专业半天理论，半天实践。并亲自动手操作。\n12 13:19:31 报名：区域特色类\n备注：成都新东方入学须知是帮助所有想学习的,想了解成都新东方烹饪学校的的一本百科指南,上面对你想快速了解学校起着重要的作用.\n12 13:19:21 报名：特色面点类\n备注：学厨师比学其他技术好在哪？学厨师没有入行门槛，工作稳定好找，薪资待遇好，同时是一个越老越吃香的职业，不会因为年龄大失业。\n12 13:18:28 报名：烧烤基础类\n备注：厨师，一个前途光明的职业。学烹饪，有着无可比拟的优势，如今已成为年轻人学技术的！厨师职业作为永远的朝阳产业，成为二十一世纪的热门职业。\n12 13:18:01 报名：卤菜基础类\n备注：事实上，从新东方学习两年烹饪技术，不但可以获得所学的烹饪实操能力，更可以通过学习成为，会管理、善经营、懂厨政的高端餐饮人才。\n05-12 12:48:45 报名：基础早餐类\n王宏伟 2019-05-11 22:05:08 报名：特色面点类\n王成正 2019-05-11 22:04:59 报名：特色小吃类", "cc_segment": 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"人生如戏\n小玉，许是希望她如同小家碧玉般惹人怜爱吧！奈何竟也是种奢望……\n村中向来不乏一些说长道短的好事者，不知小玉从几岁开始从别人口中得知自己有两个哥哥、一个母亲。遗憾的只是“曾经有”。母亲生下她后离世，哥哥们也相继不知何故随母亲而去。至于父亲，是在初中才得知他尚在的。又听说他是看女儿长大了，想着能卖几个钱了才回来相认，还有那一屁股的债也得还啊！或许吧。连那一亩三分地都照看的荒草丛生，烟酒赌牌从未断过，又敢奢望他做点什么呢?一度曾纠结过，人常说“父慈子孝”，像那人一般的父亲，自己该如何面对？或许不用面对，有爱小玉的爷爷奶奶和小姨啊！\n是在大学时。刚上大学时方言浓厚，许是紧张，说话时总是结巴。难以交流，没什么人情味，略显痴傻。从小面对的嘲笑与讽刺何其之多，看惯了人情冷暖这些算得了什么呢？\n果然，后来她变了。变得更会应对外面的世界。\n该花的钱一份不少，该请的客一个不落，该考的证全部收入囊中。其实她觉得自己是幸福的。她是村里条件差的家庭中唯一坚持上学的女娃娃，是爷爷奶奶给了自己这份坚持。相似的家庭，邻居家的梦梦早已嫁为他妇并且怀孕，说是爷爷想让梦梦为自己养老，不能远嫁，书读得多了心就野了，就指了一户同村门当户对的给她做丈夫。这样的例子不胜枚举。\n大二那年刚考完试，小玉接到一个电话后就泣不成声。看她掩面悲泣的痛苦，必然是家中有事。果然，丧事，爷爷死了。需要孝子贤孙给亲人穿鞋，身体已经浮肿的不像人形，怎么都穿不进去…….怎么会料不到？可拿什么改变？几次三番的送去抢救，那些亲戚明明带着钱，却没有人出头交住院费。三伏天里仍要烧炕取暖，父亲并没有和爷爷奶奶住在一起。不知道拖了几次，终于来不及，终于等不到。小玉只默默拍下一张照片…….\n难道生活不应继续向前吗？慢慢的和父亲的关系有所缓和。家里还有奶奶需要人照看啊。虽不至亲密，至少能说几句话了。又迎一季秋，得加些衣物了。小姨说要寄过来，等电话即可。怪不得电话上老发来一条短信，每次只有三个字“王小玉”。打电话询问才知衣物在他手中。二人同在一乡，大学同在一条街。\n收到衣物了，二人聊天和电话中一来二去竟要发生点什么的趋势。小玉是个好姑娘，那会村中谁家火光窜天，大冬天的全村只有小玉一个学生娃娃忙进忙出的去帮忙救火到天亮，烧焦了的头盖骨被挖出来硬生生的滚在小玉脚下她也没娇气的哼一下。每次打水总惦记着捎一壶给舍友……\n好人自有好报，在舍友的撺掇下这等好事岂会付之东流？初步审核不错，高大清秀。舍友们轮番提问后，这事越来越是八九不离十了。果然，喜事发生。小姨打电话来问衣物是否收到，发现了点势头。严肃禁止和他谈恋爱，不明所以。\n这不过十几天的缘分还是消散于某个夜晚。没什么，许是兴趣不在了吧。和平分手。以至隔了几周再问小玉，她说已经不记得他的样子了。她要找的是对她好的人，既然不能，何必勉强？分手只是趁早而已。\n时间晃晃悠悠的走着，又至一年暑假，几经折腾小玉还是没能找到兼职。小姨简直是她的哆啦A梦！兼职地点是旅游景点的售票员。几乎没什么人，白落了个清闲。美景不少，票子不少。\n而她这个暑假并不开心，甚至痛苦。小姨与景点老板的关系匪浅，每年老板都会给小姨钱。小玉称老板为叔叔。他们各自有家室，相处的相安无事。叔叔的儿子是自己的前男友，原来如此。小玉质问小姨，她全部坦白。生活哪里还能给小姨一些希望？姨夫有恶习已十几年，戒不掉的。她还要为女儿打算。这一切让人那么厌倦……\n兼职提前结束，叔叔的妻子得了乳腺癌。学费也凑够了，小玉终于要走了。回到学校还不忘调侃一下自己，真是去哪里兼职哪里就倒闭，前年的饭馆、大前年的农家乐、大大前年的网吧……\n生活还在继续，这一天天要是像翻书一样翻过去了，也就过去了，那该多好。愿我们无喜无忧不是因为麻木。\n人生努力爱\n上一篇储杨：这个世界上，没有一步是浪费的\n下一篇 旁观一场初恋", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 140, "original_length": 18370, "line_ids": [33, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 137, 138], "language": "zh", "language_score": 0.92, "perplexity": 1766.2, "bucket": "middle"} +{"url": "https://www.cnooc.com.cn/art/2022/4/28/art_47612_15326530.html", "date_download": "2022-05-19T08:33:44Z", "digest": "sha1:G6DVAWDOYBZHG7DP2Q6DEE4HZDOOKVA6", "length": 762, "nlines": 11, "source_domain": "www.cnooc.com.cn", "title": "中国海洋石油集团有限公司 党建工作 中海化学大峪口公司“主题党日+”焕活力", "raw_content": "中海化学大峪口公司“主题党日+”焕活力\n“水沟边杂草丛生，沟里满是淤泥，排水功能会受到很大影响。”\n“雨季即将来临，这件事情等不起、拖不得，要马上想办法解决！”\n在不久前的安全巡检作业中，中海石油化学股份有限公司（简称中海化学）大峪口公司发现一期散装库西侧水沟杂草丛生，排水功能可能会受到影响，进而导致库内肥料受潮。\n为解决这一问题，中海化学大峪口公司召开磷肥生产部支委扩大会，迅速组织党员、团员对水沟开展清理作业。\n以上工作仅是中海化学大峪口公司开展“主题党日+”活动的一个缩影。\n近年来，为充分发挥党组织战斗堡垒作用，中海化学大峪口公司磷肥生产部党支部将主题党日与安全环保、生产经营、降本提质增效、项目建设等重点工作相结合，由党支部牵头，按照“一月一主题”要求，围绕当月工作重点开展讨论，引导党员真学实干、学做结合。“主题党日+”活动为公司完成年度生产经营目标提供了坚强保障。\n中海化学大峪口公司磷肥生产部党支部通过“主题党日+”活动，划分出三个党员责任区，进一步丰富主题党日的活动形式和内容，统筹推进化肥产量提升、降本提质增效与安全生产等工作，为公司高质量发展注入强劲红色动力。\n其中，党支部第一责任区党员克服人员紧缺等难题，着力提升复合肥产量。不断优化装置系统运行方式，稳步提升系统投料量，实现复合肥稳产、高产；通过岗位评比、“五小创新”等活动，推动化肥产量屡创新高。\n第二责任区党员锚定降本提质增效目标，积极探索新技术工艺。通过提高氨的利用率、变废料为宝、深挖能耗运行空间等方式，2021年累计降本568万元。\n第三责任区党员牵头开展安全生产百日行动、安全生产月、安全互查和月度例查等活动。推进并完成“安全生产三年行动攻坚年”三个重点项目，2021年累计排查隐患206项，隐患整改率达100%。（ 通讯员 丁艳萍）", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 32, "original_length": 4852, "line_ids": [2, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], "language": "zh", "language_score": 0.97, "perplexity": 1939.0, "bucket": "middle"} +{"url": "https://www.dmahora.com/media/18770.html", "date_download": "2022-05-19T09:51:02Z", "digest": "sha1:7OKNFOPCE4GSXRX3BITHKNIV2NB6G5UU", "length": 4006, "nlines": 40, "source_domain": "www.dmahora.com", "title": " 为了生活更美好", "raw_content": "转变观念，助力国家“双碳”目标早日实现\n2022年04月14日　来源：南方周末 分享到\n来源：南方周末\n记者：柯玉圆\n实现“双碳”目标是一场广泛而深刻的变革。当前，碳中和已成全球共识，世界各国纷纷按下“快进键”。近日，德国提出将100%可再生能源供电目标提前15年（至2035年）实现。\n而在2021年3月，国家发改委明确将建立碳达峰碳中和的“1+N”政策体系，作为js333金沙线路国落实“双碳”工作的顶层设计。中国的碳中和进程备受世界瞩目，也成为今年两会代表委员们的心头牵挂。\n【金沙网站】集团董事局主席刘汉元带来了他的思考和建议。与此同时，连续10余年围绕国家生态环境问题、为光伏产业健康发展建言献策的他，今年又有何新建议？\n【金沙网站】集团董事局主席刘汉元\n实现“双碳”，可再生能源无疑是主力军\n南方周末：您每年都关注社会热点问题，今年“碳中和”是最大的热点之一，您对此是否有相关建议？\n刘汉元代表：目前，从消费端、减少碳排放、能源的投入产出、国家能源战略安全等多个角度来看，在实现碳达峰碳中和的过程中，以光伏、风能为代表的可再生能源无疑是主力军。但产业的发展仍面临一些问题，一定程度上延缓了“双碳”目标落地。\n因此，js333金沙线路将围绕光伏等可再生能源产业生产能耗考核、引导电动汽车成为电网储能终端、提高低碳能源自给率、减轻光伏企业税费负担等方面提出合理建议，进一步推动可再生能源持续健康发展，助力“双碳”目标有效落地。\n南方周末：除了“双碳”目标外，今年还是巩固拓展脱贫攻坚成果同乡村振兴有效衔接的关键之年。这是否也成为了您的关注焦点并有相关建议？\n刘汉元代表：2月22日，今年中央一号文件发布，明确了要牢牢守住保障国家粮食安全和不发生规模性返贫两条底线，扎实有序做好乡村发展、乡村建设、乡村治理重点工作，推动乡村振兴取得新进展、农业农村现代化迈出新步伐。\n基于以上关注焦点，js333金沙线路将针对降低水产行业税费负担、优化进口饲料用粮配额分配机制及库存粮饲用拍卖机制、保障生猪生产确保市场平稳供应等方面提出合理建议，为行业健康可持续发展鼓与呼。\n助力“双碳”，观念问题仍是最大的问题\n南方周末：在当前的国内外环境下，您认为光伏行业想要实现低碳转型，所面临的主要问题和挑战都有哪些？\n刘汉元代表：其实，“双碳”目标利好整个光伏行业，但发展中也面临亟待解决的瓶颈问题。观念问题依然是最大的问题。事实上，只要js333金沙线路认识到、看到这是一种必然的要求和未来的现实，那么在政策、措施、产业发展、技术等方面都能找到解决方案。从研究和思考角度来看，无论是储能、新的电网建立，还是相关产业转型，已经有很多可行的、经济的方案摆在行业面前，可以去实施、去实践。\n光伏行业最核心的可持续性发展点在于它的资源和环境友好性。光普遍存在，用它转变成电，使用后释放到大自然中的还是热量。对于地球表面来讲，光利用和不被利用，热平衡是一样的。光伏使用到高纯晶硅，硅也是地球上最多的三大元素之一。无论是光资源，还是原材料，这种无限性和友好性是支撑光伏行业可持续发展的核心背景。\n但光仍然具有不稳定性，增加了电网调控、平衡的难度。整个电网要从原来的集中式的发、供模式，转变成分散发、用模式，这需要能自动匹配、调度、平衡的智慧电网和智能电网。在这个过程中，技术上没有太大问题，但观念上以及实际运行模式上，还有很大的挑战。\n南方周末：在您看来，过去几年的“弃风弃光”现象，主要原因是观念问题吗？\n刘汉元代表：是的，这是部分观念和体制机制的不匹配造成的。行业碳中和路径确定后，政策与体制机制逐渐完善，虽然有挑战，但完全可以支撑未来20至30年内，建成比今天更稳定的电网。\n未来电网只需要在用电端编写能源消费的要求和原则、供应端输出匹配原则，电网的运行全部可以自动匹配、自动平衡。电网缺电时，会给出价格信号，空调、冰箱等电器将尽量减少用电；电动车等有电池的设备，也可以在电网缺电时，反向供电。这就是智慧电网，无论是局域、区域、微网，都能自js333金沙线路调节，自动平衡、愈合。所以，未来风光的不稳定性将不会成为问题，依靠电网系统完全能够解决。\n南方周末：您如何评价中国目前光伏产业链的发展？\n刘汉元代表：从制造端看，js333金沙线路国已形成了全球领先的完整光伏产业链，全球70%以上的光伏产品都是中国制造。中国光伏新增装机连续9年全球领跑，累计装机量连续7年全球第一。麦肯锡研究报告显示，在中美两国的各项产业比较中，光伏产业是与中国家用电器和高铁并驾齐驱、遥遥领先于美国的三大产业之一。\n从消费端看，交通运输用油约占js333金沙线路国每年原油消费的70%，而燃油汽车百公里油费约为电动汽车百公里电费的4到5倍，因此以输出的等效能量计算，消费端的电价为油价的1/4到1/5。目前，世界原油价格接近100美元/桶，如按2021年平均价格70美元/桶计算，等效能量的电价成本约为15美元左右。当前光伏已实现平价上网，因此发电成本实际已降到了相当于15美元/桶原油的价格，且发电全过程零污染、零排放。\n10年前，中国多晶硅几乎全靠进口，10年后的今天，到目前，全球前十大高纯晶硅企业中，中国企业占据七席；前十大硅片企业中全部为中国企业；前十大组件企业中，中国企业占据八席，而且这还是在美欧联手对中国进行了多年“双反”之后js333金沙线路国取得的成绩。\n当前，js333金沙线路国已形成了200GW左右的光伏系统产能，其产品每年发出的电力大约会减少3.5亿吨碳排放。从制造过程看，生产200GW光伏系统大约需要消耗60万吨高纯晶硅，大约产生1050万吨碳排放，但其产品每年发电减少的碳排放达到3.5亿吨。也就是说，制造光伏系统每产生1吨碳排放，其当年生产的产品一年发出的电力将减少33吨碳排放。因此，这可能是人类历史和碳中和道路上，目前为止，规模最大、投入产出最高、节能减排减碳最有效的方式之一。从能源的投入产出看，生产1kW光伏系统全过程需耗电300kWh左右，而建成的1kW系统每年可发电约1500kWh，意味着制造光伏系统全过程的能耗，在电站建成后半年内即可全部收回，而系统可以稳定运行25年以上，其整个生命周期回报的电力是投入的50倍以上。\n南方周末：据您观察，光伏发电会成为中国“双碳”目标实现的重要助推力吗？\n刘汉元代表：对。近年来，随着产业规模不断扩大，技术迭代升级不断加快，智能制造迅速推广，光伏发电成本下降了90%以上。与此同时，js333金沙线路国光伏发电成本也有了大幅降低，截至目前平均上网电价已降至0.3元/千瓦时以内。“十四五”期间还将降到0.25元/千瓦时以下，届时光伏发电成本将低于绝大部分煤电。如进一步考虑生态环境成本，光伏发电的优势将更加明显。未来，光伏发电的成本还将进一步降低，而化石能源的环境成本、排放成本还会上升。\n当前，加快可再生能源发展，推进汽车电动化、能源消费电力化、电力生产清洁化进程，推动js333金沙线路国能源体系转型升级，已成为落实国家能源安全新战略的迫切需要，成为了深度参与全球能源治理的必然要求，成为了积极推动全球一体化、体现大国担当的必然选择，成为了推进全球可持续发展的必由之路，成为了落实碳中和目标、筑牢js333金沙线路国能源和外汇安全体系的重要保障，成为了助力中国再次崛起和中华民族伟大复兴的必要条件。\njs333金沙线路已经到了经济瓶颈突破的时期，随着政府部门的坚决落实、良性的经济循环、光伏产业自身的良性发展和企业的有效支撑，中国双碳目标有可能在大家的共同努力下提前5到10年实现。\n乡村振兴，不是把人口简单聚集\n【金沙网站】集团在光伏领域是否有新举措？\n【金沙网站】集团的主要产业之一光伏智造已经延伸到其它产业中，以“渔光一体”创新模式为主的“光伏+农业”实现广泛应用。目前，js333金沙线路正在全国各地推广和建立“渔光一体”基地，优质清洁的光伏电力源源不断惠及千家万户的同时，也促进了光伏产业与乡村的融合发展，为新时代光伏产业参与乡村振兴铺就了一条重要路径。\n【金沙网站】集团如何参与乡村振兴？\n【金沙网站】参与乡村振兴，不是把人口简单聚集，而是把农村留守人员和环境资源进行有效协同。在这个过程中，js333金沙线路强调一定要熟悉js333金沙线路国的国情和农村情况，做好“渔光一体”。\n【金沙网站】坚持将企业发展与国家政策同频共振，把“渔光一体”模式提档升级为定位更高、价值更高的“渔光小镇”，做好做足“渔光”产业文章，形成“水上产出清洁电力，水下产出安全水产品，周边乡村旅游”的产业闭环，实现三产融合发展，成片、系统地推动农民转型、农业升级、乡村建设。\n【金沙网站】“渔光小镇”不仅要把水产养殖做好，也要把能源转型做好。围绕“渔光小镇”打造，充分发挥技术、产业等优势，逐渐形成标准高、分工明确、模式成熟的产业链条，使“渔光小镇”模式在更多地方复制推广。\n【金沙网站】样本”。", 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"工商管理学院举办“劳动学问节”主题实践活动\n2022年4月6日至5月6日，工商管理学院举办“劳动学问节”主题实践活动，通过宣传劳动常识、展示劳动成果、表彰爱劳学生宣传劳动精神。\n期间，学院以“劳动教育之我秀”、“劳动教育之我行”、“劳动教育之我知”为主题，通过丰富的劳动教育活动开展了最美志愿者评选、网课成果风采展、寝室“三除”活动、“绿布袋”行动、劳动精品特色课程等六项学院特色劳动教育活动。结合校园疫情管理做到线下劳动，线上宣传，让劳动学问有内容、有成果。此外，本次主题实践活动通过定制考评机制，对同学们参与“劳动学问节”各项活动情况进行成果考核，调动劳动热情，增强劳动精神，崇尚劳动光荣。\n“秀”我劳动成果，表彰“爱劳”学子。\n四月初，我校被划为临时防范区，课程也由线下改为线上网课。为此，学院开展“网课笔记、网课成果风采展”，以寝室为单位，推荐优秀网课作品及笔记，“秀”出实践成果。学院共收到来自各寝室楼共125分作品，并从中挑选、收录、表彰了100份的特色学生笔记、PPT、网页模型等，通过学院微信公众号展示收录作品中具有代表性的部分网课笔记、网课成果，鼓励同学们在动手实践的过程中创造有价值的物化劳动成果。推文“笔尖上的劳动节之最美笔记‘内卷’成果展示”一经推出，备受师生好评。\n同时学院师生围绕临时封校期抗战疫情开展了安全巡逻、物资分发、寝室检查等各项工作，学院开展“最美志愿者”评选，表彰“爱劳”学子。活动伊始，00后退役大学生士兵庞大兴，自愿“出战”守好校门；大数据学生苏诗喻主动“请战”，发出“强国有我，请党放心”的志愿服务宣言，学院不少同学积极响应劳动学问节的号召纷纷加入志愿活动中来，掀起了一场抗“疫”志愿服务浪潮。最终，活动以班级为单位，通过志愿服务事迹、志愿服务时长评出45名“最美志愿者”，并挑选部分志愿者优秀事迹在微信公众号中展出，树立先进劳动典型，营造校园劳动氛围，弘扬大学生志愿服务精神。\n“行”我劳动荣光，创建文明寝室。\n为了做好新冠疫情期间宿舍的防控工作，营造卫生整洁、安全舒适的学习生活环境，巩固良好日常生活劳动习惯，自觉做好宿舍卫生保洁。“劳动学问节”期间，学院将每周三下午定为“绿布袋”回收日，鼓励学生积极参与垃圾分类与寝室大扫除。过程中，推选出15名绿布袋先进个人、2名先进班级。每周三下午，同学们积极参与到寝室大扫除及绿布袋行动中。据统计，学院在四月份共回收资源1656.35公斤，为创建绿色平安校园出力。\n此外，学院于4月27日开展五一劳动学问周学生寝室“三除”活动，发起“劳动最美”集赞活动。当日下午，同学们分工合作，共同参与寝室卫生大扫除。从卫生间到阳台、从床铺到床底、从书桌到书架、从窗台到地面……寝室每一个角落，每一条缝隙，都打扫的干干净净。学院对全体学生寝室大扫除进行考评，并开展“春季安全攻坚检查”，将检查内容及考评结果计入劳动学问节成绩中。\n“知”我劳动精神，品出劳动趣味。\n五一劳动周期间，学院积极响应学生处的号召，开展劳动教育精品课。高卫红老师以劳树德为同学们上了一堂精彩的劳动精品礼仪课，林业、会计、建筑等学院共25人报名参加。课程通过形体操，借助瑜伽砖、形体棒等专业形体训练用具，让同学们深刻体验了优雅站姿和端庄坐姿的标准礼仪仪态。我院还组织了30名学生参与其他二级学院精品课程如“烘焙课”、“叠被子”等，在劳动趣味课中掌握劳动技能，品味劳动乐趣。\n此外，学院承办了寝室学问节活动，通过鼓励参与寝室学问节活动，将寝室为搭建为劳动主要阵地。同时，对积极参加实习实训、专业服务和创新创业活动的同学进行典型树立，根据各专业推荐2名工匠之星、20名指尖尖兵。\n习总书记在全国教育大会上提出：“让劳动光荣、创造伟大成为铿锵的时代强音，让劳动最光荣、最崇高、最伟大、最美丽蔚然成风。”相信能通过“劳动学问节”，我院学子能深刻体会到劳动的重要性与积极意义，意识到社会的发展与进步离不开劳动。做到享受着他人提供给自己的劳动成果同时通过劳动服务他人。专注劳动精神，崇尚劳动光荣，拥抱更好的明天。\n（撰稿：施韵）", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 38, "original_length": 5650, "line_ids": [16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30], "language": "zh", "language_score": 0.94, "perplexity": 1940.8, "bucket": "middle"} +{"url": "https://www.forthlink.com/gov/6207", "date_download": "2022-05-19T08:36:03Z", "digest": "sha1:YVW3JKUVCKUXUPPIZKUJTYUVGKXJIAQC", "length": 930, "nlines": 46, "source_domain": "www.forthlink.com", "title": "四强科技", "raw_content": "雅特力AT32F421 系列\nM4主频最高120MHz\n具有DSP指令集2.4V ~ 3.6V\n供电电压I/Os 可承受\n40 ~ +105°C\n64KB/32KB/16KB+ 4KB闪存\n16KB/8KB SRAM\nsLib：将指定之主存储区设为执行代码安全库区(仅能调用无法读取)\n4KB的系统存储器作启动加载程序用，亦可一次性配置成用户程序和数据区\nPLL (120 MHz输出)\n外部高速4 ~ 25MHz晶体振荡器\n40°C ~ 105°C)\n40 °C ~ 105 °C达2%精度)\n外部低速32.768kHz RTC晶体振荡器\n内部低速40kHz 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"language": "zh", "language_score": 0.74, "perplexity": 1507.6, "bucket": "middle"} +{"url": "https://www.greenpost.se/2016/10/13/%E8%AF%BA%E8%B4%9D%E5%B0%94%E6%96%87%E5%AD%A6%E5%A5%96%E5%8D%B3%E5%B0%86%E6%8F%AD%E6%99%93%E5%90%84%E8%B7%AF%E8%AE%B0%E8%80%85%E9%BD%90%E8%81%9A%E6%96%87%E5%AD%A6%E9%99%A2/", "date_download": "2022-05-19T08:22:57Z", "digest": "sha1:W7DX66BVQVH2WY6BGEMBU3SEONAMH3OG", "length": 1105, "nlines": 13, "source_domain": "www.greenpost.se", "title": "GREENPOST.SE", "raw_content": "北欧广播, 北欧电视, 华侨华人, 国际新闻, 头条要闻, 文化卫生, 文学, 杂文，评论\n诺贝尔文学奖即将揭晓各路记者齐聚文学院\n北欧绿色邮报网报道（记者陈雪霏）－－今天下午一点，还有一个多小时的时间，诺贝尔文学奖将斯德哥尔摩瑞典文学院宣布。\n现任秘书SARA DANIUS. 图片来自文学院\n和往年差不多，提前两个小时，摄影记者就都挤在宣布厅的门口了。10年前，我第一次来的时候，就是这样。我问工作人员为什么不用后面宽敞的大厅来宣布呢？她说，第一这是传统，一直在这里，第二，我们就是要这个效果，就是要让人看着人多。\n记者一声长叹，有钱难买我乐意啊！从那时以后，我再也不到前面去挤了，而是自觉地站在最后，看着那些辛苦的记者在前面挤，搭梯子等等，营造一个非常拥挤的氛围。\n当然，文学奖确实有比记者更多的人关注。例如，去年，他们就请一个学校的中学生来体验诺贝尔文学奖宣布的氛围。同时，那些翻译家，出版社的出版商，把赌注压在某个作家身上的有关人士都会来诺贝尔文学奖发布会现场来体验那令人心跳的一刻。如果出版社压对了，出版了该作者的翻译本作品，那自然会顿时爆发出热烈的欢呼声。\n当然，2012年除外，2012年是中国作家莫言获奖，所以很多人闻声而来。象马悦然，陈迈平等都到了场。陈迈平夫人陈安娜翻译了很多中国作家的作品，自然是个风向标。凤凰卫视驻巴黎记者也特意赶到斯德哥尔摩来采访。据说，连老板刘长乐都想出席一下诺贝尔晚宴。\n对于这样的明显的风向标，我们自然心情很激动。但是，由于诺贝尔奖保密工作做得好。即使你猜有可能，任何人也不敢百分之百地确信，只能是两首准备。当然，到宣布的时候，果然是莫言，中国记者们忍不住爆发出一阵的喝彩。那种兴奋和惊喜真是难以言表的。\n前任秘书Peter Englund. 图片来自文学院。\n当然，你不能指望永远有中国作家获奖。好机会也得留给别人一点儿。因为第二年，有人采访我问中国那么多作家，为什么中国作家获奖那么少。我说，这奖是人家评的，我们自己不能随便去争。等我问秘书彼得的时候，他更直接，你的意思是说去年我们刚把奖给中国，今年还要给中国作家吗？\n通常文学奖的宣布者是文学院常务秘书。宣布之后，即开始专访，这个专访时间需要一个小时左右。然后，人们慢慢散去，等待12月初亲身采访诺奖得主本人。\nPrevious PostVideo视频：Hart, Holmstrom share prize in economic sciencesNext PostTop story:Bob Dylan， great poet, wins 2016 Nobel Prize in Literature", "cc_segment": 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President, Its Time for Battle! Maju Wars\n本游戏是以「社长，战斗时间到！」为蓝本，全新制作的SRPG！角色・场景建模均为3D，战斗系统最多支持5名角色同时出击。\n3D战斗地图中的敌人和道具配置均为随机生成，即使反复挑战同一迷宫也能体会新的乐趣。\n主角是一家小冒险公司的经营者。某天外出购物时突然遇到了大地震，地震停止后，发现原本该是公司的地方竖立着一扇巨大的门……\n公司与公司中的员工一同消失，正在走投无路之时，主角振奋精神，决定找回消失的员工，重建公司。\n公司的设立\n为了重建失去的公司组建了新的办公室，将员工编入队伍，尽情探索迷宫吧。\n回合制战斗系统\n本游戏采用经典的回合制战斗系统，可支持最多5名角色同时出击。\n在己方回合时可指挥角色移动或攻击，使用强力的技能进行战斗。\n凶恶的敌人\n探索迷宫时会遇到各种各样的怪物。\n有些怪物会使用特殊技能，因此根据敌人特性组建队伍进行战斗十分重要。\n迷宫探索\n迷宫内沉睡着许多道具和宝物，这是公司收入的来源，尽情收集，满载而归吧。\n迷宫内有许多玩法，任务和探索目的会随探索阶段发生变化。\n最重要的是，员工们正在迷宫中等待你的救助，请尽早将他们救出来吧！\n招募新员工\n可通过人事部招募员工。\n可招募的角色丰富，有战士、骑士、魔法师、僧侣等能力和技能各有不同的职业。\n随着游戏进度的推进，可招募员工也会逐渐增加。\n公司重建\n虽然公司在门出现后就消失了，但可通过玩家的努力再次重建起来。\n最初只是一间租来的小小办公室，但通过冒险提升公司业绩后，公司规模也会不断成长。\n人材投资\n员工是探索中最重要的一环。\n投资训练所可以大幅度提升员工的战斗能力，请不要吝惜地投资人材吧。\n迷宫中充满了各种危险。\n在出发去冒险前，用预算经费在商店购买辅助用的回复道具吧。\n十日大门\n十日大门来历成谜，没有人知道它最早何时出现，也没有人知道它是怎么建成的。某一天，它突然降临在了主角所在的街道上。\n大门每１０天打开一次，门内凶恶的怪物头目正伺机准备攻击这边的世界。\n在第１０天到来之前培养员工，准备作战，击败怪物头目攻略迷宫吧。\n新角色等DLC制作完成后会随时在商城发布。\n●操作系统: Windows 8 or newer\n●处理器: Intel i5 2.3GHz or AMD A9 2.9GHz equivalent\n●显卡: VRAM 1 GB or better graphics card that can support DirectX 12.0 or more\n●存储空间: 需要 1500 MB 可用空间\n●声卡: DirectSound (DirectX) compatible sound card\n8350 4.0GHz equivalent\n●显卡: NVIDIA GeForce GTX 960、ATI Mobility Radeon R9 290 or better", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 298, "original_length": 8375, "line_ids": [68, 86, 87, 89, 90, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 115, 116, 117, 118, 119, 120, 122, 127, 128, 130, 132, 133, 137, 139], "language": "zh", "language_score": 0.93, "perplexity": 1917.7, "bucket": "middle"} +{"url": "https://www.iqifei.cn/230.html", "date_download": "2022-05-19T08:35:22Z", "digest": "sha1:6BB4VX3ZXZO3PFCL732GUYYOGHBJC4ZM", "length": 1281, "nlines": 17, "source_domain": "www.iqifei.cn", "title": " 爱起飞", "raw_content": "人工智能可以成为成功供应链的关键吗？\n造成当今供应链复杂性的因素有很多，包括数据分析中成熟度的变化，运输中断，通信问题，组织孤岛以及重复的流程和控制。有关全球贸易协定的政治紧张局势也严重影响了材料和产品的供应。\n这是人工智能（AI）和机器学习（ML）可以提供帮助的地方。人工智能和数据分析提供了机会，可以更准确地预测挑战并计划有效的快速响应，同时最大限度地减少未来的中断。\nAI可能指的是许多技术实现，但是ML是当今AI最突出的实现。它使用算法和应用程序来自动化数据分析并创建知识模型。机器学习解决方案可用于执行预测分析，例如回归分析和分类，这对于预测与供应链有关的业务问题特别有用。\n满足当今需求的创新解决方案\n运输问题通常是供应链中断的重要组成部分。人工智能解决方案可以通过自动收集路线中各个点的数据，然后使用预期的运输来满足消费者的需求（有时甚至在报告需求之前）来帮助解决这些挑战。\n提高运输效率的另一种方法可以是根据最新的交通和天气状况重新安排交货时间和修改运输路线。将这些数据包括在预测模型中可以使这些预测更相关，并且过程更有效。\n19疫苗时，至关重要的是不仅要预测疫苗本身的库存量，而且要预测外围用品（例如注射器，稀释剂和冷藏用品）的库存量。甚至与患者护理相关的预测，例如人员配备需求和每位患者进行免疫的预约时间，也可能变得很重要。\n机器学习：一切都在数据中\n尽管供应链的每个结点涉及许多复杂性，但组织可以通过逐步致力于数据分析的成熟度来获得可观的价值，而不必采用强大的机器学习解决方案。面对不确定性，人工智能功能和改进数据分析的价值最终以更好的决策形式体现。组织的数据可能包含风险和获得新价值的机会的指标。大多数组织可以通过采用更好的流程，释放其数据的真正潜力来开始改善其数据治理。\n用于建模的数据可能来自许多来源，包括过去和当前的供需模式，实时交通和天气更新，库存数据，市场预测等。与任何输入输出过程一样，更准确的数据输入会产生更准确的预测。围绕数据管理改进版本控制和变更管理实践可以帮助保护数据质量。此外，从这些数据中收集并纳入预测模型的假设需要充分记录在案，以解释其原理并允许对模型性能和调整进行持续监控。\n19免疫示例中，应尽可能高效，准确地共享来自免疫站点（例如医院和诊所）的数据，以使制造商和物流公司能够做出相应的响应。\n为您的业务实施AI解决方案\n具有实施RPA解决方案经验的自动化专家可以帮助组织从各种业务功能中识别和优化数据源。一旦确定了相关的数据和过程，自动化就可以改善收集过程和数据质量。自动化解决方案还可以帮助组织建立标准化的业务逻辑，从而通过使用管理仪表板来改进对关键绩效指标（KPI）的识别和监控，并更快，更有效地缓解风险。\n标签：ai, 人工智能, 供应链\n确保运输有效且具有成本效益的8种方法\n人类与人工智能：协作是关键", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 100, "original_length": 5514, "line_ids": [56, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 75], "language": "zh", "language_score": 0.98, "perplexity": 1648.6, "bucket": "middle"} +{"url": "https://www.ithai365.com/xwzx/1143.html", "date_download": "2022-05-19T08:21:15Z", "digest": "sha1:AE62E53QGT3IXK7CVTTYBBLW5SSOGE5Z", "length": 315, "nlines": 9, "source_domain": "www.ithai365.com", "title": "PE大管件的优点有哪几点？_ope体育app下载手机版", "raw_content": "PE大管件的优点有哪几点？\nPE管件为实用新型专利产品，质优价廉，具有以下优点：\n1、内部有紧锁箍、阶梯型环形密封圈等部件。设计科学，牢度强，密封性好。\n2、联接方便，拆卸容易，灵活性强，便于回收，美观实用。\nPVC、钢管等的连接，应用面十分广泛。\n4、规格齐全，从类型上分有直通、异径直通、正三通、变径三通、90°弯头、丝口接头、堵头等，从尺寸上分基本有Φ20、Φ25、Φ32、Φ40、Φ50、Φ63规格，能满足各种应用所需，如果数量大，可以根据需要定做相应特殊规格。\n上一篇：大口径ope体育app下载手机版的重要性质是什么？\n下一篇：三格ope体育app下载手机版具体尺寸\n普通截止阀", "cc_segment": "crawl-data/CC-MAIN-2022-21/segments/1652662526009.35/wet/CC-MAIN-20220519074217-20220519104217-00720.warc.wet.gz", "original_nlines": 120, "original_length": 3537, "line_ids": [56, 58, 59, 60, 61, 62, 63, 64, 77], "language": "zh", "language_score": 0.9, "perplexity": 1805.3, "bucket": "middle"} diff --git a/flagdata/quality_assessment/FastText/data/cn_stopwords.txt b/flagdata/quality_assessment/FastText/data/cn_stopwords.txt new file mode 100644 index 0000000..4210f28 --- /dev/null +++ b/flagdata/quality_assessment/FastText/data/cn_stopwords.txt @@ -0,0 +1,746 @@ +$ +0 +1 +2 +3 +4 +5 +6 +7 +8 +9 +? +_ +“ +” +、 +。 +《 +》 +一 +一些 +一何 +一切 +一则 +一方面 +一旦 +一来 +一样 +一般 +一转眼 +万一 +上 +上下 +下 +不 +不仅 +不但 +不光 +不单 +不只 +不外乎 +不如 +不妨 +不尽 +不尽然 +不得 +不怕 +不惟 +不成 +不拘 +不料 +不是 +不比 +不然 +不特 +不独 +不管 +不至于 +不若 +不论 +不过 +不问 +与 +与其 +与其说 +与否 +与此同时 +且 +且不说 +且说 +两者 +个 +个别 +临 +为 +为了 +为什么 +为何 +为止 +为此 +为着 +乃 +乃至 +乃至于 +么 +之 +之一 +之所以 +之类 +乌乎 +乎 +乘 +也 +也好 +也罢 +了 +二来 +于 +于是 +于是乎 +云云 +云尔 +些 +亦 +人 +人们 +人家 +什么 +什么样 +今 +介于 +仍 +仍旧 +从 +从此 +从而 +他 +他人 +他们 +以 +以上 +以为 +以便 +以免 +以及 +以故 +以期 +以来 +以至 +以至于 +以致 +们 +任 +任何 +任凭 +似的 +但 +但凡 +但是 +何 +何以 +何况 +何处 +何时 +余外 +作为 +你 +你们 +使 +使得 +例如 +依 +依据 +依照 +便于 +俺 +俺们 +倘 +倘使 +倘或 +倘然 +倘若 +借 +假使 +假如 +假若 +傥然 +像 +儿 +先不先 +光是 +全体 +全部 +兮 +关于 +其 +其一 +其中 +其二 +其他 +其余 +其它 +其次 +具体地说 +具体说来 +兼之 +内 +再 +再其次 +再则 +再有 +再者 +再者说 +再说 +冒 +冲 +况且 +几 +几时 +凡 +凡是 +凭 +凭借 +出于 +出来 +分别 +则 +则甚 +别 +别人 +别处 +别是 +别的 +别管 +别说 +到 +前后 +前此 +前者 +加之 +加以 +即 +即令 +即使 +即便 +即如 +即或 +即若 +却 +去 +又 +又及 +及 +及其 +及至 +反之 +反而 +反过来 +反过来说 +受到 +另 +另一方面 +另外 +另悉 +只 +只当 +只怕 +只是 +只有 +只消 +只要 +只限 +叫 +叮咚 +可 +可以 +可是 +可见 +各 +各个 +各位 +各种 +各自 +同 +同时 +后 +后者 +向 +向使 +向着 +吓 +吗 +否则 +吧 +吧哒 +吱 +呀 +呃 +呕 +呗 +呜 +呜呼 +呢 +呵 +呵呵 +呸 +呼哧 +咋 +和 +咚 +咦 +咧 +咱 +咱们 +咳 +哇 +哈 +哈哈 +哉 +哎 +哎呀 +哎哟 +哗 +哟 +哦 +哩 +哪 +哪个 +哪些 +哪儿 +哪天 +哪年 +哪怕 +哪样 +哪边 +哪里 +哼 +哼唷 +唉 +唯有 +啊 +啐 +啥 +啦 +啪达 +啷当 +喂 +喏 +喔唷 +喽 +嗡 +嗡嗡 +嗬 +嗯 +嗳 +嘎 +嘎登 +嘘 +嘛 +嘻 +嘿 +嘿嘿 +因 +因为 +因了 +因此 +因着 +因而 +固然 +在 +在下 +在于 +地 +基于 +处在 +多 +多么 +多少 +大 +大家 +她 +她们 +好 +如 +如上 +如上所述 +如下 +如何 +如其 +如同 +如是 +如果 +如此 +如若 +始而 +孰料 +孰知 +宁 +宁可 +宁愿 +宁肯 +它 +它们 +对 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+譬喻 +譬如 +让 +许多 +论 +设使 +设或 +设若 +诚如 +诚然 +该 +说来 +诸 +诸位 +诸如 +谁 +谁人 +谁料 +谁知 +贼死 +赖以 +赶 +起 +起见 +趁 +趁着 +越是 +距 +跟 +较 +较之 +边 +过 +还 +还是 +还有 +还要 +这 +这一来 +这个 +这么 +这么些 +这么样 +这么点儿 +这些 +这会儿 +这儿 +这就是说 +这时 +这样 +这次 +这般 +这边 +这里 +进而 +连 +连同 +逐步 +通过 +遵循 +遵照 +那 +那个 +那么 +那么些 +那么样 +那些 +那会儿 +那儿 +那时 +那样 +那般 +那边 +那里 +都 +鄙人 +鉴于 +针对 +阿 +除 +除了 +除外 +除开 +除此之外 +除非 +随 +随后 +随时 +随着 +难道说 +非但 +非徒 +非特 +非独 +靠 +顺 +顺着 +首先 +！ +， +： +； +？ diff --git a/flagdata/quality_assessment/FastText/evaluate.py b/flagdata/quality_assessment/FastText/evaluate.py new file mode 100644 index 0000000..3474876 --- /dev/null +++ b/flagdata/quality_assessment/FastText/evaluate.py @@ -0,0 +1,78 @@ +""" +@misc{chen2023chinesewebtext, + title={ChineseWebText: Large-scale High-quality Chinese Web Text Extracted with Effective Evaluation Model}, + author={Jianghao Chen and Pu Jian and Tengxiao Xi and Dongyi Yi and Qianlong Du and Chenglin Ding and Guibo Zhu and Chengqing Zong and Jinqiao Wang and Jiajun Zhang}, + year={2023}, + eprint={2311.01149}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +""" +# coding = utf-8 +import os +import json +import jieba +import random +import argparse +import fasttext +from tqdm.auto import tqdm +from multiprocessing import Pool + +jieba.setLogLevel(20) + +stopwords = set([x.strip() for x in open( + "./data/cn_stopwords.txt").readlines()]) + + +def build(text): + segs = jieba.lcut(text) + segs = [x for x in segs if len(x) > 1 and x not in stopwords] + return " ".join(segs) + + +def predict(input_file): + file_dir, file_name = os.path.split(input_file) + + output_dir = os.path.join(file_dir, "fasttext") + output_dir = output_dir.replace("data2", "data") + os.makedirs(output_dir, exist_ok=True) + output_file = os.path.join(output_dir, file_name) + + lines = [] + with open(input_file, 'r', encoding='utf-8') as r_f: + for line in r_f: + lines.append(json.loads(line)) + + # print("jieba cutting...")d + seg_texts = [build(''.join(line["raw_content"])) for line in lines] + + # print("predicting...") + labels, values = model.predict(seg_texts) + + # print("writing...") + with open(output_file, 'w', encoding='utf-8') as w_f: + for label, value, line in zip(labels, values, lines): + _label = label[0].replace("__label__", "") + _value = value[0] if value[0] <= 1 else 1 + line['fasttext_value'] = float(_value) if _label == 'clean' else float(1 - _value) + + w_f.write(json.dumps(line, ensure_ascii=False) + '\n') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--dates', nargs='+', default=None) + args = parser.parse_args() + + model = fasttext.load_model("models/model.bin") + + for clean_root in args.dates: + + file_name_list = [x for x in os.listdir(clean_root) if x.endswith(".jsonl")] + print(file_name_list) + + with Pool(64) as p: + for _ in tqdm(p.imap_unordered(predict, + [os.path.join(clean_root, file_name) for file_name in file_name_list]), + total=len(file_name_list)): + pass diff --git a/flagdata/quality_assessment/README.md b/flagdata/quality_assessment/README.md new file mode 100644 index 0000000..fab108d --- /dev/null +++ b/flagdata/quality_assessment/README.md @@ -0,0 +1,14 @@ +BERT and fasttext were chosen as evaluation models because they have the following advantages: + +1. the BERT model performs well in text categorization and comprehension tasks, has strong language understanding and + representation capabilities, and can effectively assess text quality. +2. FastText models have efficient training and inference speeds while maintaining classification performance, which can + significantly reduce training and inference time. + + + The article compares different text categorization models including logistic regression, BERT and FastText to + evaluate their performance. In the experiments, the BERTEval and FastText models perform well in text categorization + tasks, with the FastText model performing best in terms of precision and + recall. [Experimental results from ChineseWebText]. + +![Classification results of different evaluation models.](quality_assessment.png) \ No newline at end of file diff --git a/flagdata/quality_assessment/README_zh.md b/flagdata/quality_assessment/README_zh.md new file mode 100644 index 0000000..971a4c2 --- /dev/null +++ b/flagdata/quality_assessment/README_zh.md @@ -0,0 +1,9 @@ +选择BERT和fasttext作为评估模型，是因为它们具有以下优点： + +1. BERT模型在文本分类和理解任务中表现出色，具有强大的语言理解和表示能力，能够有效地评估文本质量。 +2. FastText模型具有高效的训练和推理速度，同时保持分类性能，可以显著减少训练和推理时间。 + +文章比较了不同的文本分类模型，包括逻辑回归、BERT和FastText，以评估它们的性能。在实验中，BERTEval和FastText模型在文本分类任务中表现良好，其中FastText模型在精度和召回率方面表现最佳。【实验结果来自ChineseWebText】 + + +![Classification results of different evaluation models.](quality_assessment.png) \ No newline at end of file diff --git a/flagdata/quality_assessment/quality_assessment.png b/flagdata/quality_assessment/quality_assessment.png new file mode 100644 index 0000000000000000000000000000000000000000..44bbbc138d3fef5e3c494b3acb2e163ede515fa8 GIT binary patch literal 27235 zcmbrlV{~Ovw#OUWso1tvv28o4*tTt(727r{HY>JmJ1^<(d%H)!ao?ABKIF_j*IaX9 zpOJHZ|221*oQx5MR#T z#@Ni#2mnAVEJ+nyO>qP@OEW%}&ooyOvJ;Y!#WxW_;|QlmL<}ep$pF7(DNY!4(LWs2 z@~AA-9|eIkPfhKIhc0yq`Kf+SRfjmB_G8lJ#N)*8&ku(KUX#h+9*1xM=43w!K7lVpk#*NVPY&}Jm4p+K9(twzgpRp*e5^P ztxfuIUpltT0e$k6uu38j=yRUdgs~uu zDg=kL+#eZVHTa#Nx3?VEpb66Jy`MpJ4Ge32?kWmO^t|ub%w)Z(0qeo_hOT zgC@b{`S~(EsbvuN2g%Run7pk+6WIcmSi0$T7*J&?eW4YqVoXAz^pW=Lc7&HRYtY@e 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b/requirements.txt index 5709a52..cf14ca0 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,14 +1,128 @@ -requests>=2.28.1 -tqdm>=4.63.0 -PyYAML>=6.0 -urlextract>=1.6.0 -opencc>=1.1.6 -lxml>=4.9.1 -transformers==4.20.1 -loguru>=0.6.0 -numpy>=1.21.5 -pandas>=1.3.5 -nltk>=3.7 -fasttext>=0.9.1 -psutil>=5.6.3 -typing_extensions \ No newline at end of file +aiohttp==3.9.1 +aiosignal==1.3.1 +annotated-types==0.6.0 +antlr4-python3-runtime==4.9.3 +anyio==4.1.0 +async-timeout==4.0.3 +attrs==23.1.0 +backoff==2.2.1 +beautifulsoup4==4.12.2 +certifi==2023.11.17 +cffi==1.16.0 +chardet==5.2.0 +charset-normalizer==3.3.2 +click==8.1.7 +coloredlogs==15.0.1 +contourpy==1.2.0 +cryptography==41.0.7 +cycler==0.12.1 +dataclasses-json==0.6.3 +datasets==2.15.0 +Deprecated==1.2.14 +dill==0.3.7 +distro==1.8.0 +effdet==0.4.1 +emoji==2.9.0 +et-xmlfile==1.1.0 +exceptiongroup==1.2.0 +fasttext==0.9.2 +filelock==3.13.1 +filetype==1.2.0 +flatbuffers==23.5.26 +fonttools==4.46.0 +frozenlist==1.4.0 +fsspec==2023.10.0 +h11==0.14.0 +httpcore==1.0.2 +httpx==0.25.2 +huggingface-hub==0.19.4 +humanfriendly==10.0 +idna==3.6 +importlib-metadata==7.0.0 +importlib-resources==6.1.1 +iopath==0.1.10 +Jinja2==3.1.2 +joblib==1.3.2 +jsonlines==4.0.0 +kiwisolver==1.4.5 +langdetect==1.0.9 +layoutparser==0.3.4 +lxml==4.9.3 +Markdown==3.5.1 +MarkupSafe==2.1.3 +marshmallow==3.20.1 +matplotlib==3.8.2 +mpmath==1.3.0 +msg-parser==1.2.0 +multidict==6.0.4 +multiprocess==0.70.15 +mypy-extensions==1.0.0 +networkx==3.2.1 +nltk==3.8.1 +numpy==1.26.2 +olefile==0.47 +omegaconf==2.3.0 +onnx==1.15.0 +onnxruntime==1.15.1 +openai==0.28.0 +opencv-python==4.8.1.78 +openpyxl==3.1.2 +packaging==23.2 +pandas==2.1.3 +pdf2image==1.16.3 +pdfminer.six==20221105 +pdfplumber==0.10.3 +pikepdf==8.8.0 +Pillow==10.1.0 +portalocker==2.8.2 +protobuf==4.25.1 +psutil==5.9.6 +pyarrow==14.0.1 +pyarrow-hotfix==0.6 +pybind11==2.11.1 +pycocotools==2.0.7 +pycparser==2.21 +pydantic==2.5.2 +pydantic_core==2.14.5 +pypandoc==1.12 +pyparsing==3.1.1 +pypdf==3.17.1 +pypdfium2==4.24.0 +pytesseract==0.3.10 +python-dateutil==2.8.2 +python-docx==1.1.0 +python-iso639==2023.6.15 +python-magic==0.4.27 +python-multipart==0.0.6 +python-pptx==0.6.23 +pytz==2023.3.post1 +PyYAML==6.0.1 +rapidfuzz==3.5.2 +regex==2023.10.3 +requests==2.31.0 +safetensors==0.4.1 +scipy==1.11.4 +six==1.16.0 +sniffio==1.3.0 +soupsieve==2.5 +sympy==1.12 +tabulate==0.9.0 +timm==0.9.12 +tokenizers==0.15.0 +torch==2.1.1 +torchvision==0.16.1 +tqdm==4.66.1 +transformers==4.35.2 +typing-inspect==0.9.0 +typing_extensions==4.8.0 +tzdata==2023.3 +unstructured==0.11.2 +unstructured-inference==0.7.15 +unstructured.pytesseract==0.3.12 +urllib3==2.1.0 +wrapt==1.16.0 +xlrd==2.0.1 +XlsxWriter==3.1.9 +xxhash==3.4.1 +yarl==1.9.3 +zipp==3.17.0 From 4904a3b54a83b69f44dc7555603d79ed05cb2462 Mon Sep 17 00:00:00 2001 From: wuchengwei Date: Mon, 18 Dec 2023 15:44:42 +0800 Subject: [PATCH 09/38] update requirements.txt --- requirements.txt | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/requirements.txt b/requirements.txt index cf14ca0..6344896 100644 --- a/requirements.txt +++ b/requirements.txt @@ -126,3 +126,10 @@ XlsxWriter==3.1.9 xxhash==3.4.1 yarl==1.9.3 zipp==3.17.0 +urlextract>=1.6.0 +opencc>=1.1.6 +loguru>=0.6.0 +codescikit-learn==1.3.0 +pytorch==2.0.1 +jieba==0.42.1 +zhconv==1.4.3 \ No newline at end of file From c060a2baa746112d0d4b7dea9aba4f802eeca61a Mon Sep 17 00:00:00 2001 From: wuchengwei Date: Mon, 18 Dec 2023 17:02:03 +0800 Subject: [PATCH 10/38] update readMe --- README_zh.md | 130 +++++++++++++++++++++++++-------------------------- dedup.png | Bin 0 -> 788869 bytes 2 files changed, 63 insertions(+), 67 deletions(-) create mode 100644 dedup.png diff --git a/README_zh.md b/README_zh.md index 5ca706d..197d699 100644 --- a/README_zh.md +++ b/README_zh.md @@ -9,7 +9,7 @@ [![License](https://img.shields.io/github/license/FlagOpen/FlagData.svg?color=blue)](https://github.com/FlagOpen/FlagData/blob/main/LICENSE) ![GitHub release (release name instead of tag name)](https://img.shields.io/github/v/release/FlagOpen/FlagData?include_prereleases&style=social) - | [English](README.md) | [中文](README_zh.md) | +| [English](README.md) | [中文](README_zh.md) |

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@@ -48,36 +48,67 @@ FlagData支持以下特性： - [许可证](#许可证) ## 安装 -- Python 版本 >= 3.9 -- Pytorch 版本 >= 1.11 如果你需要使用数据压缩模块，请参考[Pytorch官方网站](https://pytorch.org/get-started/locally/)安装与你的运行环境适配的Pytorch版本。 -- 可选: 如需使用数据标注工具，请先安装[NGINX](https://www.nginx.com/resources/wiki/start/topics/tutorials/install/)，然后根据[快速上手](#快速上手)中的步骤配置应用。 -通过pip安装FlagData所有模块。通过下面的命令，你将安装所有模块的依赖包。 +- requirements.txt 文件下，是FlagData项目所有的依赖包 ```bash -pip install flagdata[all] +pip install -r requirements.txt ``` -选择性安装FlagData中所需的模块 (`module` 需要替换成模块名，如 `cleaner`、`condensation`、`analysis`等)。你将只会安装对应模块的依赖包，这适合那些只想使用某个特定模块且不想安装其他模块依赖包的使用者。 +选择性安装FlagData中所需的`cleaner`模块 。你将只会安装对应模块的依赖包，这适合那些只想使用`cleaner`模块且不想安装其他模块依赖包的使用者。 ```bash -pip install flagdata[module] +pip install flagdata[cleaner] ``` **安装main分支的最新版本** -如果你想安装/更新到main分支的最新版本，请使用以下命令： +main分支为FlagData正式发布的分支，如果你想安装/更新到main分支的最新版本，请使用以下命令： ``` -git clone https://github.com/cofe-ai/FlagData.git +git clone https://github.com/FlagOpen/FlagData.git pip install .[all] ``` **基于源码二次开发** ```bash -git clone https://github.com/cofe-ai/FlagData.git +git clone https://github.com/FlagOpen/FlagData.git pip install -r requirements.txt ``` ## 快速上手 +### 1、数据来源阶段 +我们提供了基于OpenAI接口的数据增强模块， +利用OpenAI接口，以三种不同策略，构建一系列针对不同能力的单轮SFT数据。策略包括： + ++ ImitateGenerator：以若干案例样本为模板，扩增数据。支持同时生成多种语言数据。 ++ AbilityExtractionGenerator: 利用OpenAI接口，归纳出若干案例样本中包含的能力。根据这个能力集合，生成新样本和答案。 ++ AbilityDirectGenerator: 根据指定的能力类型，或者任务类型，直接生成与该能力或任务相关的新样本。例如，指定能力为“逻辑推理”，则可生成一系列逻辑推理题目及答案。为增强生成样本的多样性，支持排除已生成样本。 + +具体示例见[数据增强模块下的readMe](flagdata/data_gen/README_zh.md) +### 2、数据准备阶段 +all2txt模块下，将pdf2txt、epub2txt等非结构化/半结构化的文件转成txt，并且可以很好的解决单栏、双栏，以及图表穿插中文本的顺序等导致问题文本内容不连贯的问题。 + +同时解析后的元素种类有"Table（表格）", "FigureCaption（图片标题）", "NarrativeText【正文】", "ListItem【参考文献】", "Title【章节标题】", "Address【邮箱地址】","PageBreak", "Header【页眉】", "Footer【页脚】", "UncategorizedText【arxiv竖排编号】", "Image(图)", "Formula（公式）" 等，工具脚本提供保留全文，以及按照类别解析保存两种形式。 + +具体示例见[all2txt模块下的readMe](flagdata/all2txt/README_zh.md) + +### 3、数据预处理阶段 +#### 3.1 语言识别 +language_identification模块下，使用 fastText 的语言分类器来做分类，fastText 的语言分类器是在 Wikipedia、Tatoeba、SETimes 上面训练的，使用了 n-grams 来作为特征，使用了层级的 softmax。支持 176 种语言的分类，并且最后会输出一个 0~1 的分数。 ++ 每个 CPU 核心上，每秒可以处理一千个文档。 ++ 对于每一个网页做一次语言分类，得到分类的分数。 ++ 对于一般清洗规则，如果大于 0.5，那么就分类为某个特定的语言，否则表示不确定是什么语言的网页并丢掉这个网页。 +#### 3.2 数据清洗 +cleaner模块，使用多进程池 mp.Pool，通过多进程方式并行处理数据。使用 SharedMemoryManager 创建可共享的数据结构，在数据处理中多进程共享数据。 + +通过多进程和共享内存的方式实现了高效的数据清洗： + +目前包含如下清洗规则： + ++ 表情符号和无意义字符（正则） ++ 清洗转载版权声明信息（知乎、csdn、简书、博客园） ++ 去除不合理的连续标点符号，换行符统一为\n ++ 去除手机号、身份证号等个人隐私、URL和额外的空格 ++ 去除开头、结尾等无关内容，去除长度小于n的文本（目前n=100） ++ 简体中文转换为繁体中文（opencc库） -### 数据清洗 使用FlagData的数据清洗功能仅需两步： 1. 修改YAML配置文件中的数据路径与格式。我们在配置文件模板中为每个参数给出了详细的注释来解释其含义。同时你也可以参考[配置](#配置)章节。 @@ -92,6 +123,23 @@ pip install -r requirements.txt 清洗后的文件会以`jsonl`的格式保存到配置文件中指定的`output`参数对应的路径。 +#### 3.3 质量评估 +选择BERT和fasttext作为评估模型，是因为它们具有以下优点： + +1. BERT模型在文本分类和理解任务中表现出色，具有强大的语言理解和表示能力，能够有效地评估文本质量。 +2. FastText模型具有高效的训练和推理速度，同时保持分类性能，可以显著减少训练和推理时间。 + +文章比较了不同的文本分类模型，包括逻辑回归、BERT和FastText，以评估它们的性能。在实验中，BERTEval和FastText模型在文本分类任务中表现良好，其中FastText模型在精度和召回率方面表现最佳。【实验结果来自ChineseWebText】 + +具体示例见[quality_assessment模块下的readMe](flagdata/quality_assessment/README_zh.md) + +#### 3.4 数据去重 +deduplication模块下，提供海量文本数据去重能力，该阶段使用的是MinHashLSH（最小哈希局部敏感哈希）通过将文本转换为一系列哈希值，以便比较文本之间的相似性。 + +我们可以通过控制参数threshold，它代表了相似性的阈值，threshold值的范围是从0到1。设置为1时意味着完全匹配，任何文本都不会被过滤掉。相反，如果设置了较低的threshold值，相似性稍微高一些的文本也会被保留，我们可以根据需要设置更高的threshold值，以便只保留那些非常相似的文本，而丢弃那些相似性稍微低一些的文本，经验默认值为0.87；同时我们利用了Spark的分布式计算能力处理大规模数据，使用了MapReduce思想来实现去重，同时经spark调优，来高效地处理大规模文本数据集。 +如下是在数据去重过程中迭代计算的相似文本，该文本在换行、编辑姓名等方面有细微区别，但是去重算法可以识别出两段文本高度相似。 + +![dedup](dedup.png) ### 数据分析 使用FlagData的数据分析功能最便捷的方式是利用我们提供的客户端请求CoreNLP官方的服务，示例代码如下： @@ -112,43 +160,8 @@ print(ners) analyzer.close() ``` -### 数据压缩 -使用FlagData的数据压缩功能仅需两步： - -1. 修改YAML配置文件的路径等参数。我们在配置文件模板中为每个参数给出了详细的注释来解释其含义。同时你也可以参考[Configuration](#configuration)章节。 - -2. 在以下代码中指定配置文件路径，运行即可 - - ```python - from flagdata.condensation.data_distillation import DataDistillationTrainer - # 需要在此处指定修改后的配置文件路径 - trainer = DataDistillationTrainer("config/distillation_config.yaml") - # 默认数据格式为jsonl，包含"text"和"label"两个键值对 - trainer.load_data() - # fit() 方法将会运行数据压缩算法并以二进制的格式保存压缩后的数据，可以使用torch.load()读取 - # 可以通过设置配置文件中的"distilled_save_path"自定义保存路径 - trainer.fit() - ``` - -### 数据标注 - -1. 将`flagdata/annotation/dist`放到nginx默认的`html`下 - -2. 修改`nginx.confg`以添加`location` - ``` - location / { - root /{your html path}/dist; # change - index index.html index.htm; - try_files $uri $uri/ /index.html; - } - ``` - -3. 重新启动nginx - -4. 根据nginx配置的ip地址访问标注系统 - ## 配置 -针对`数据清洗`、`数据压缩`模块， 我们提供了配置文件模板：[cleaner_config.yaml](https://dorc.baai.ac.cn/resources/projects/FlagData/cleaner_config.yaml)， [distillation_config.yaml](https://dorc.baai.ac.cn/resources/projects/FlagData/distillation_config.yaml)。 配置文件为易读的 [YAML](https://yaml.org) 格式，并提供了详尽的注释。使用这些模块前请确认已经在配置文件中修改好相应参数。 +针对`数据清洗`、`数据质量评估`模块， 我们提供了配置文件模板：[cleaner_config.yaml](https://dorc.baai.ac.cn/resources/projects/FlagData/cleaner_config.yaml)， [distillation_config.yaml](https://dorc.baai.ac.cn/resources/projects/FlagData/distillation_config.yaml)。 配置文件为易读的 [YAML](https://yaml.org) 格式，并提供了详尽的注释。使用这些模块前请确认已经在配置文件中修改好相应参数。 以下是一些你需要注意的重要参数： @@ -161,35 +174,18 @@ analyzer.close() output: ./demo/output.jsonl ``` -### 数据压缩 - - ```yaml - train_data_path: <训练数据路径> - test_data_path: <测试数据路径> - # huggingface上的预训练模型，目录下需要包含pytorch_model.pt, vocab.txt等 - model_name: "/data/scripts/pretrained_models/distilbert-base-uncased" - # model.fit()方法将会运行数据压缩算法并以二进制的格式保存压缩后的数据，可以使用torch.load()读取 - distilled_save_path: - # 可选: 加载压缩后的数据，用于初始化或继续训练 - distilled_load_path: null - ``` - ## 使用指南 我们提供了一系列使用指南，帮助用户快速体验FlagData的特性。 * [Tutorial 1: 清洗从互联网上获取到的原始文本](/docs/tutorial_01_cleaner.md) -* [Tutorial 2: 分析、处理文本数据](/docs/tutorial_02_analysis.md) -* [Tutorial 3: 使用数据压缩算法减少数据使用量](/docs/tutorial_03_condensation.md) -* [Tutorial 4: NLP任务标注](/docs/tutorial_04_text_annotation.md) -* [Tutorial 5: CV任务标注](/docs/tutorial_05_image_annotation.md) ## 联系我们 如果你对本项目的使用和代码有任何问题，可以提交issue。同时你也可以通过邮箱 data@baai.ac.cn 直接联系我们 ## 参考项目 本项目部分参考自以下代码： -[GeneralNewsExtractor](https://github.com/GeneralNewsExtractor/GeneralNewsExtractor), -[text-data-distillation](https://github.com/arumaekawa/text-dataset-distillation), +[GeneralNewsExtractor](https://github.com/GeneralNewsExtractor/GeneralNewsExtractor), +[text-data-distillation](https://github.com/arumaekawa/text-dataset-distillation), [emoji](https://github.com/carpedm20/emoji), [transformers](https://github.com/huggingface/transformers)。 diff --git a/dedup.png b/dedup.png new file mode 100644 index 0000000000000000000000000000000000000000..11d3b0c4c30994802266a1dfda33816ea7e2a14c GIT binary patch literal 788869 zcmeFYWmFwaw=IkY3liMjU4m>}f(Lg`kPtk$YjAgWcY?dS2X}Xejmw6+^WJmrpPTbM z_s941V|0)1(PM3@YpqpPv*wx|q9`wkjEIj20Re$5E%iwm0s{6Be3HYz13z)9$5w!V 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