From 97e3bb0a179aa3a8ce28081728accdb95bd8de85 Mon Sep 17 00:00:00 2001 From: Rehan Fazal Date: Sat, 17 Aug 2024 18:24:51 +0200 Subject: [PATCH 1/2] Develop (#32) * Intial commit of squeaky clean text * updated the sct.py script with modular code * updated the sct.py script with pipeline method, which would ideally would help to make changes in the processing easier * removed unnecessary direction code * adding to do list * adding to do list * added requiremnt.txt file * added setup.py file * added test cases * updated config file * merging back * Develop (#2) (#3) * Intial commit of squeaky clean text * updated the sct.py script with modular code * updated the sct.py script with pipeline method, which would ideally would help to make changes in the processing easier * removed unnecessary direction code * adding to do list * adding to do list * added requiremnt.txt file * added setup.py file * added test cases * updated config file * merging back * rebase * update the license * added German and Spanish support * Updated file for pypi * Updated readme file * Add GitHub Actions workflow for publishing to PyPI * Updated readme file * Updated readme file * added the username to the publish.yml * update the API vriable name * update the API user name * Bump version to 0.1.1 * updated the readme file * updated the version * Update NER Process and added tag removal * Updated congig file * updated the code to have the option to not output language * fixed the bug for NER which was refrencing to the wrong model variable names, add the gpu support * fixed the Anonomyser Engine * fixed the Anonomyser Engine * added the test.yml file * added the test.yml file * added the test.yml file * added the German and Spanish language support in lingua * added the ability in the config to change the model name * added the ability in the config to change the model name * added the ability in the config to change the model name and fixed spanish model name * squased some bugs * added the language passing support * Refactored the code * fixed typing issue * reverted the refactor --- sct/config.py | 91 +++++----------- sct/sct.py | 238 +++-------------------------------------- sct/utils/constants.py | 11 +- sct/utils/contact.py | 88 +++++---------- sct/utils/datetime.py | 19 +--- sct/utils/ner.py | 225 ++++++++------------------------------ sct/utils/normtext.py | 117 +++++--------------- sct/utils/special.py | 99 ++++------------- sct/utils/stopwords.py | 49 ++------- setup.py | 2 +- 10 files changed, 174 insertions(+), 765 deletions(-) diff --git a/sct/config.py b/sct/config.py index b95d6a4..323d872 100644 --- a/sct/config.py +++ b/sct/config.py @@ -1,100 +1,57 @@ """ -Module containing the configuration parameters for the SCT package. + detect_language : to detect the language automatically, but would consume more time if done on a batch + fix_bad_unicode : if True, fix "broken" unicode such as mojibake and garbled HTML entities + to_ascii_unicode : if True, convert non-to_ascii characters into their closest to_ascii equivalents + replace_with_url : special URL token, default "", + replace_with_email : special EMAIL token, default "", + replace_years : replace year, default "", + replace_with_phone_number : special PHONE token, default "", + replace_with_number : special NUMBER token, default "", + no_currency_symbols : if True, replace all currency symbols with the respective alphabetical ones, + ner_process : To execute NER Process to remove the positpositional tags, PER, LOC, ORG, MISC + remove_isolated_letters : remove any isolated letters which doesn't add any value to the text + remove_isolated_symbols : remove any isolated symbols which shouldn't be present in the text, usually which isn't + immediatly prefixed and suffixed by letter or number + normalize_whitespace : remove any unnecessary whitespace + statistical_model_processing : to get the statistical model text, like for fastText, SVM, LR etc + casefold : to lower the text + remove_stopwords : remove stopwords based on the language, usues NLTK stopwords + remove_punctuation : removes all the special symbols """ -# Flag to detect the language automatically. If True, the language will be detected for each text. CHECK_DETECT_LANGUAGE = True - -# Flag to fix "broken" unicode such as mojibake and garbled HTML entities. CHECK_FIX_BAD_UNICODE = True - -# Flag to convert non-ASCII characters into their closest ASCII equivalents. CHECK_TO_ASCII_UNICODE = True - -# Flag to replace HTML tags with a special token. CHECK_REPLACE_HTML = True - -# Flag to replace URLs with a special token. CHECK_REPLACE_URLS = True - -# Flag to replace email addresses with a special token. CHECK_REPLACE_EMAILS = True - -# Flag to replace years with a special token. CHECK_REPLACE_YEARS = True - -# Flag to replace phone numbers with a special token. CHECK_REPLACE_PHONE_NUMBERS = True - -# Flag to replace numbers with a special token. CHECK_REPLACE_NUMBERS = True - -# Flag to replace currency symbols with their respective alphabetical equivalents. CHECK_REPLACE_CURRENCY_SYMBOLS = True - -# Flag to execute Named Entity Recognition (NER) to remove positional tags such as PER, LOC, ORG, MISC. CHECK_NER_PROCESS = True - -# Flag to remove any isolated letters which do not add any value to the text. CHECK_REMOVE_ISOLATED_LETTERS = True - -# Flag to remove any isolated symbols which should not be present in the text. CHECK_REMOVE_ISOLATED_SPECIAL_SYMBOLS = True - -# Flag to remove any unnecessary whitespace. CHECK_NORMALIZE_WHITESPACE = True - -# Flag to get the statistical model text, such as for fastText, SVM, LR. CHECK_STATISTICAL_MODEL_PROCESSING = True - -# Flag to convert all characters to lowercase. CHECK_CASEFOLD = True - -# Flag to remove stopwords based on the language. Uses NLTK stopwords. CHECK_REMOVE_STOPWORDS = True - -# Flag to remove all special symbols. CHECK_REMOVE_PUNCTUATION = True - -# Flag to remove custom stopwords specific to the SCT package. CHECK_REMOVE_STEXT_CUSTOM_STOP_WORDS = True - -# Special token to replace URLs. REPLACE_WITH_URL = "" - -# Special token to replace HTML tags. REPLACE_WITH_HTML = "" - -# Special token to replace email addresses. REPLACE_WITH_EMAIL = "" - -# Special token to replace years. REPLACE_WITH_YEARS = "" - -# Special token to replace phone numbers. REPLACE_WITH_PHONE_NUMBERS = "" - -# Special token to replace numbers. REPLACE_WITH_NUMBERS = "" - -# Special token to replace currency symbols. If None, symbols will be replaced with their 3-letter abbreviations. REPLACE_WITH_CURRENCY_SYMBOLS = None - -# List of positional tags to be removed by NER. POSITIONAL_TAGS = ['PER', 'LOC', 'ORG'] - -# Confidence threshold for NER. NER_CONFIDENCE_THRESHOLD = 0.85 - -# Language to be used for NER. If None, the language will be detected automatically. LANGUAGE = None -# List of pre-trained NER models in order of importance. -NER_MODELS_LIST = [ - "FacebookAI/xlm-roberta-large-finetuned-conll03-english", # English Model - "FacebookAI/xlm-roberta-large-finetuned-conll02-dutch", # Dutch Model - "FacebookAI/xlm-roberta-large-finetuned-conll03-german", # German Model - "FacebookAI/xlm-roberta-large-finetuned-conll02-spanish", # Spanish Model - "Babelscape/wikineural-multilingual-ner" # Multilingual Model -] - +# Order of the model is Important : English Model, Dutch Model, German Model, Spanish Model, MULTILINGUAL Model +NER_MODELS_LIST = ["FacebookAI/xlm-roberta-large-finetuned-conll03-english", + "FacebookAI/xlm-roberta-large-finetuned-conll02-dutch", + "FacebookAI/xlm-roberta-large-finetuned-conll03-german", + "FacebookAI/xlm-roberta-large-finetuned-conll02-spanish", + "Babelscape/wikineural-multilingual-ner"] diff --git a/sct/sct.py b/sct/sct.py index 5ccdcc6..8e1b08d 100644 --- a/sct/sct.py +++ b/sct/sct.py @@ -9,46 +9,30 @@ class TextCleaner: def __init__(self): - """ - Initialize the TextCleaner object and setup the pipeline. - """ - - # Initialize all the pipeline steps - self.ProcessContacts = contact.ProcessContacts() # Handles contact processing - self.ProcessDateTime = datetime.ProcessDateTime() # Handles datetime processing - self.ProcessSpecialSymbols = special.ProcessSpecialSymbols() # Handles special symbols processing - self.NormaliseText = normtext.NormaliseText() # Handles text normalization - self.ProcessStopwords = stopwords.ProcessStopwords() # Handles stopwords processing - self.GeneralNER = ner.GeneralNER() # Handles named entity recognition - - # Initialize the pipeline and language - self.pipeline = [] # Stores the pipeline steps - self.language = None # Stores the language of the text - - # Initialize the pipeline + self.ProcessContacts = contact.ProcessContacts() + self.ProcessDateTime = datetime.ProcessDateTime() + self.ProcessSpecialSymbols = special.ProcessSpecialSymbols() + self.NormaliseText = normtext.NormaliseText() + self.ProcessStopwords = stopwords.ProcessStopwords() + self.GeneralNER = ner.GeneralNER() + self.pipeline = [] + self.language = None self.init_pipeline() def init_pipeline(self): - """ - Initialize pipeline steps based on config - """ - - # If the language is specified in the config, set it + # Initialize pipeline steps based on config language_config = config.LANGUAGE.lower() if config.LANGUAGE else None + if language_config and language_config in resources.LANGUAGE_NAME: self.language = language_config.upper() - - # Add the detect language step if any of the following are True - if any([config.CHECK_DETECT_LANGUAGE, config.CHECK_NER_PROCESS, config.CHECK_REMOVE_STOPWORDS]): + elif any([config.CHECK_DETECT_LANGUAGE, config.CHECK_NER_PROCESS, config.CHECK_REMOVE_STOPWORDS]): self.pipeline.append(self.detect_language) - # Add the Unicode fix steps if any of the following are True if config.CHECK_FIX_BAD_UNICODE: self.pipeline.append(self.fix_bad_unicode) if config.CHECK_TO_ASCII_UNICODE: self.pipeline.append(self.to_ascii_unicode) - # Add the replace steps if any of the following are True if config.CHECK_REPLACE_HTML: self.pipeline.append(self.replace_html) if config.CHECK_REPLACE_URLS: @@ -64,11 +48,9 @@ def init_pipeline(self): if config.CHECK_REPLACE_CURRENCY_SYMBOLS: self.pipeline.append(self.replace_currency_symbols) - # Add the NER step if any of the following are True if config.CHECK_NER_PROCESS: self.pipeline.append(self.ner_process) - # Add the remove isolated steps if any of the following are True if config.CHECK_REMOVE_ISOLATED_LETTERS: self.pipeline.append(self.remove_isolated_letters) if config.CHECK_REMOVE_ISOLATED_SPECIAL_SYMBOLS: @@ -91,251 +73,57 @@ def process(self, text): return text def detect_language(self, text): - """ - Detects the language of the input text and sets the language attribute. - - Args: - text (str): The input text to detect the language of. - - Returns: - str: The input text. - """ - # Detect the language of the text - detected_language = str(resources.DETECTOR.detect_language_of(text)).split(".")[-1] - - # Set the language attribute to the detected language - self.language = detected_language - - # Return the input text + self.language = str(resources.DETECTOR.detect_language_of(text)).split(".")[-1] return text def fix_bad_unicode(self, text): - """ - Fixes broken unicode text using the `ftfy` library. - - This function fixes various issues with unicode text, including mojibake, HTML entities, and other code cruft. - It also normalizes the text to a specified format, which can improve the readability and consistency of the text. - - Args: - text (str): The raw text to be fixed. - - Returns: - str: The fixed and normalized text. - """ - # Call the fix_bad_unicode method of NormaliseText class to fix the text return self.NormaliseText.fix_bad_unicode(text) def to_ascii_unicode(self, text): - """ - Transliterate unicode text into ascii characters. - - This function uses the `to_ascii_unicode` method of the `NormaliseText` class to transliterate the text. - - Args: - text (str): The input text to transliterate. - - Returns: - str: The transliterated text. - """ - # Call the to_ascii_unicode method of the NormaliseText class to transliterate the text return self.NormaliseText.to_ascii_unicode(text) def replace_html(self, text): - """ - Replace all HTML tags in the given text with the specified replacement string. - - Args: - text (str): The input text to be processed. - - Returns: - str: The processed text with all HTML tags replaced. - """ - # Call the replace_html method of the ProcessContacts class to replace HTML tags in the text return self.ProcessContacts.replace_html(text, replace_with=config.REPLACE_WITH_HTML) def replace_urls(self, text): - """ - Replace all URLs in the given text with the specified replacement string. - - This function uses the `replace_urls` method of the `ProcessContacts` class to replace URLs in the text. - - Args: - text (str): The input text to be processed. - - Returns: - str: The processed text with all URLs replaced. - """ - # Call the replace_urls method of the ProcessContacts class to replace URLs in the text return self.ProcessContacts.replace_urls(text, replace_with=config.REPLACE_WITH_URL) def replace_emails(self, text): - """ - Replace all email addresses in the given text with the specified replacement string. - - This function uses the `replace_emails` method of the `ProcessContacts` class to replace email addresses in the text. - - Args: - text (str): The input text to be processed. - - Returns: - str: The processed text with all email addresses replaced. - """ - # Call the replace_emails method of the ProcessContacts class to replace email addresses in the text return self.ProcessContacts.replace_emails(text, replace_with=config.REPLACE_WITH_EMAIL) def replace_years(self, text): - """ - Replace all years in the given text with the specified replacement string. - - This function uses the `replace_years` method of the `ProcessDateTime` class to replace years in the text. - - Args: - text (str): The input text to be processed. - - Returns: - str: The processed text with all years replaced. - """ - # Call the replace_years method of the ProcessDateTime class to replace years in the text return self.ProcessDateTime.replace_years(text, replace_with=config.REPLACE_WITH_YEARS) def replace_phone_numbers(self, text): - """ - Replace all phone numbers in the given text with the specified replacement string. - - This function uses the `replace_phone_numbers` method of the `ProcessContacts` class to replace phone numbers in the text. - - Args: - text (str): The input text to be processed. - - Returns: - str: The processed text with all phone numbers replaced. - """ - # Call the replace_phone_numbers method of the ProcessContacts class to replace phone numbers in the text return self.ProcessContacts.replace_phone_numbers(text, replace_with=config.REPLACE_WITH_PHONE_NUMBERS) def replace_numbers(self, text): - """ - Replace all numbers in the given text with the specified replacement string. - - This function uses the `replace_numbers` method of the `ProcessContacts` class to replace numbers in the text. - - Args: - text (str): The input text to be processed. - - Returns: - str: The processed text with all numbers replaced. - """ - # Call the replace_numbers method of the ProcessContacts class to replace numbers in the text return self.ProcessContacts.replace_numbers(text, replace_with=config.REPLACE_WITH_NUMBERS) def replace_currency_symbols(self, text): - """ - Replace all currency symbols in the given text with the specified replacement string. - - This function uses the `replace_currency_symbols` method of the `ProcessSpecialSymbols` class to replace currency symbols in the text. - - Args: - text (str): The input text to be processed. - - Returns: - str: The processed text with all currency symbols replaced. - """ - # Call the replace_currency_symbols method of the ProcessSpecialSymbols class to replace currency symbols in the text return self.ProcessSpecialSymbols.replace_currency_symbols(text, replace_with=config.REPLACE_WITH_CURRENCY_SYMBOLS) def ner_process(self, text): - """ - Execute named entity recognition (NER) to remove the positional tags from the text. - - Args: - text (str): The input text to be processed. - config.POSITIONAL_TAGS (list): The positional tags to be removed from the text. - config.NER_CONFIDENCE_THRESHOLD (float): The confidence threshold for the NER model. - self.language (str): The language of the text. - - Returns: - str: The processed text with the positional tags removed. - """ return self.GeneralNER.ner_process(text, config.POSITIONAL_TAGS, config.NER_CONFIDENCE_THRESHOLD, self.language) def remove_isolated_letters(self, text): - """ - Removes any isolated letters which doesn't add any value to the text. - - This method uses the `remove_isolated_letters` method of the `ProcessSpecialSymbols` class to remove isolated letters from the input text. - - Args: - text (str): The input text to be processed. - - Returns: - str: The processed text with isolated letters removed. - """ - # Call the remove_isolated_letters method of the ProcessSpecialSymbols class to remove isolated letters from the text return self.ProcessSpecialSymbols.remove_isolated_letters(text) def remove_isolated_special_symbols(self, text): - """ - Removes any isolated special symbols which doesn't add any value to the text. - - This method uses the `remove_isolated_special_symbols` method of the `ProcessSpecialSymbols` class to remove isolated special symbols from the input text. - - Args: - text (str): The input text to be processed. - - Returns: - str: The processed text with isolated special symbols removed. - """ - # Call the remove_isolated_special_symbols method of the ProcessSpecialSymbols class to remove isolated special symbols from the text return self.ProcessSpecialSymbols.remove_isolated_special_symbols(text) def normalize_whitespace(self, text): - """ - Normalize whitespace in the given text. - - This function replaces one or more spacings with a single space, and one - or more line breaks with a single newline. Also, it strips leading/trailing whitespace. - - Args: - text (str): The input text to be processed. - - Returns: - str: The processed text with normalized whitespace. - """ - # Call the normalize_whitespace method of the NormaliseText class to normalize whitespace in the text - # Set no_line_breaks flag to True to remove all line breaks return self.NormaliseText.normalize_whitespace(text, no_line_breaks=True) def statistical_model_processing(self, text): - """ - Process the given text for classical/statistical machine learning models. - - This function applies the following processing steps to the input text: - 1. Case folding (lowercase) - 2. Stopword removal - 3. Punctuation removal - 4. Removing isolated letters - 5. Normalizing whitespace - - Args: - text (str): The input text to be processed. - - Returns: - str: The processed text. - """ - stext = text if config.CHECK_CASEFOLD: - # Convert the text to lowercase - stext = text.casefold() + stext = text.casefold() # lowercase if config.CHECK_REMOVE_STOPWORDS: - # Remove stopwords from the text stext = self.ProcessStopwords.remove_stopwords(stext, self.language) if config.CHECK_REMOVE_PUNCTUATION: - # Remove punctuation from the text stext = self.ProcessSpecialSymbols.remove_punctuation(stext) if config.CHECK_REMOVE_ISOLATED_LETTERS: - # Remove isolated letters from the text stext = self.ProcessSpecialSymbols.remove_isolated_letters(stext) if config.CHECK_NORMALIZE_WHITESPACE: - # Normalize whitespace in the text stext = self.NormaliseText.normalize_whitespace(stext) return stext \ No newline at end of file diff --git a/sct/utils/constants.py b/sct/utils/constants.py index d57b950..1087256 100644 --- a/sct/utils/constants.py +++ b/sct/utils/constants.py @@ -36,6 +36,14 @@ flags=re.IGNORECASE | re.UNICODE, ) +# for more information: https://github.com/jfilter/clean-text/issues/10 +# PHONE_REGEX = re.compile( +# r"((?:^|(?<=[^\w)]))(((\+?[01])|(\+\d{2}))[ .-]?)?(\(?\d{3,4}\)?/?[ .-]?)?(\d{3}[ .-]?\d{4})(\s?(?:ext\.?|[#x-])\s?\d{2,6})?(?:$|(?=\W)))|\+?\d{4,5}[ .-/]\d{6,9}" +# ) +# PHONE_REGEX = re.compile( +# r"((?:^|(?<=[^\w)]))((\+?[01]|0{1,2}\d{0,1}|\+\d{2})[ .-]?)?(\(?\d{3,4}\)?/?[ .-]?)?(\d{3}[ .-]?\d{4})(\s?(?:ext\.?|[#x-])\s?\d{2,6})?(?:$|(?=\W)))|\+?\d{4,5}[ .-/]\d{6,9}" +# ) + PHONE_REGEX = re.compile( r"((?:^|(?<=[^\w)]))((\+?\d+|0{1,2}\d*?)[ .-]?)?(\(?\d{3,4}\)?/?[ .-]?)?(\d{3}[ .-]?\d{4})(\s?(?:ext\.?|[#x-])\s?\d{2,6})?(?:$|(?=\W)))|\+?\d{4,5}[ .-/]\d{6,9}" ) @@ -108,7 +116,6 @@ "〟", "＂", ] - strange_single_quotes = ["‘", "‛", "’", "❛", "❜", "`", "´", "‘", "’"] DOUBLE_QUOTE_REGEX = re.compile("|".join(strange_double_quotes)) @@ -120,4 +127,4 @@ ISOLATED_SPECIAL_SYMBOLS_REGEX = re.compile(r"(?<;·}@~!?+#)({,/\\\\^]+(?![a-zA-Z0-9])", flags=re.UNICODE | re.IGNORECASE) -SENTENCE_BOUNDARY_PATTERN = re.compile('(?<=[.!?])\s+(?=[^\d])') +SENTENCE_BOUNDARY_PATTERN = re.compile('(?<=[.!?])\s+(?=[^\d])') \ No newline at end of file diff --git a/sct/utils/contact.py b/sct/utils/contact.py index 3d189f5..8ea4df4 100644 --- a/sct/utils/contact.py +++ b/sct/utils/contact.py @@ -6,83 +6,45 @@ class ProcessContacts: def __init__(self): pass - def replace_urls(self, input_text: str, replacement: str = "") -> str: + def replace_urls(self, text, replace_with=""): """ - Replace all URLs in the given input text with the specified replacement string. - - Args: - input_text (str): The input text to be processed. - replacement (str, optional): The string to replace URLs with. Defaults to "". - - Returns: - str: The processed text with all URLs replaced. + Replace all URLs in ``text`` str with ``replace_with`` str. """ - return constants.URL_REGEX.sub(replacement, input_text) + # matches = constants.URL_REGEX.finditer(text) + # result = text + # # Iterate through matches in reverse order (to avoid index issues) + # for match in reversed(list(matches)): + # # Check if the matched substring contains non-ASCII characters + # if not any(ord(char) > 127 for char in match.group()): + # result = text[:match.start()] + replace_with + text[match.end():] + return constants.URL_REGEX.sub(replace_with, text) - def replace_html(self, text: str, replacement: str = "") -> str: + def replace_html(self, text, replace_with=""): """ - Replaces all HTML tags in the given text with the specified replacement string. - - Args: - text (str): The input text to be processed. - replacement (str, optional): The string to replace HTML tags with. Defaults to "". - - Returns: - str: The processed text with all HTML tags replaced. + Replace all html tags in ``text`` str with ``replace_with`` str. """ try: - # Use BeautifulSoup to parse the HTML and extract the text - cleaned_text = BeautifulSoup(text, 'html.parser').get_text() + soup = BeautifulSoup(text, 'html.parser') + text = soup.get_text() except: - # If BeautifulSoup fails, use the HTML_REGEX to remove HTML tags - cleaned_text = constants.HTML_REGEX.sub(replacement, text) + text = constants.HTML_REGEX.sub(replace_with, text) - return cleaned_text + return text - def replace_emails(self, input_text: str, replacement: str = "") -> str: + def replace_emails(self, text, replace_with=""): """ - Replace all email addresses in the input text with the specified replacement string. - - Args: - input_text (str): The text to be processed. - replacement (str, optional): The string to replace email addresses with. Defaults to "". - - Returns: - str: The processed text with all email addresses replaced. + Replace all emails in ``text`` str with ``replace_with`` str. """ - # Use the pre-compiled regular expression to find all email patterns in the text - # and replace them with the specified replacement string - return constants.EMAIL_REGEX.sub(replacement, input_text) + return constants.EMAIL_REGEX.sub(replace_with, text) - def replace_phone_numbers(self, input_text: str, replacement: str = "") -> str: + def replace_phone_numbers(self, text, replace_with=""): """ - Replaces all occurrences of phone numbers in the input text with a - specified replacement string. - - Args: - input_text (str): The text to be processed. - replacement (str, optional): The string to replace the phone numbers with. - Defaults to "". - - Returns: - str: The modified text with phone numbers replaced. + Replace all phone numbers in ``text`` str with ``replace_with`` str. """ - # Use the pre-compiled regular expression to find all phone number patterns - # in the text and replace them with the specified replacement string - return constants.PHONE_REGEX.sub(replacement, input_text) + return constants.PHONE_REGEX.sub(replace_with, text) - def replace_numbers(self, input_text: str, replacement: str = "") -> str: + def replace_numbers(self, text, replace_with=""): """ - Replace all numbers in the input text with a specified replacement string. - - Args: - input_text (str): The input text. - replacement (str, optional): The string to replace the numbers with. - Defaults to "". - - Returns: - str: The modified text with numbers replaced. + Replace all numbers in ``text`` str with ``replace_with`` str. """ - # Use the pre-compiled regular expression to find all number patterns - # in the text and replace them with the specified replacement string - return constants.NUMBERS_REGEX.sub(replacement, input_text) + return constants.NUMBERS_REGEX.sub(replace_with, text) \ No newline at end of file diff --git a/sct/utils/datetime.py b/sct/utils/datetime.py index ddfa14b..78b330a 100644 --- a/sct/utils/datetime.py +++ b/sct/utils/datetime.py @@ -1,26 +1,15 @@ from sct.utils import constants + class ProcessDateTime: def __init__(self): pass - def replace_years(self, text: str, replacement: str = "") -> str: + def replace_years(self, text, replace_with =""): """ Replaces years between 1900 to 2099 in the text with a special token. - - Args: - text (str): The input text. - replacement (str, optional): The string to replace the years with. - Defaults to "". - - Returns: - str: The modified text with years replaced. """ - # Use the pre-compiled regular expression to find all year patterns - # in the text and replace them with the specified replacement string - - # Substitute all year patterns with special token - cleaned_string = constants.YEAR_REGEX.sub(replacement, text) + cleaned_string = constants.YEAR_REGEX.sub(replace_with, text) - return cleaned_string + return cleaned_string \ No newline at end of file diff --git a/sct/utils/ner.py b/sct/utils/ner.py index 5c9c229..116daaf 100644 --- a/sct/utils/ner.py +++ b/sct/utils/ner.py @@ -21,129 +21,60 @@ class GeneralNER: """ def __init__(self): - """ - Initializes the GeneralNER object. - - This function initializes the GeneralNER object by loading the necessary models and tokenizers. - It sets the device to "cuda" if it is available, otherwise it sets it to "cpu". - It initializes the AnonymizerEngine object. - It loads the NER models and tokenizers based on the configuration. - It sets the minimum token length based on the maximum length of the tokenizers. - It sets the tokenizer based on the maximum length of the tokenizers. - """ - - # Set the device to "cuda" if it is available, otherwise set it to "cpu" + self.device = "cuda" if torch.cuda.is_available() else "cpu" - # Initialize the AnonymizerEngine object self.engine = AnonymizerEngine() - - # Load the NER models and tokenizers based on the configuration + if len(config.NER_MODELS_LIST) == 5: - # Load models from the configuration english_model_name = config.NER_MODELS_LIST[0] dutch_model_name = config.NER_MODELS_LIST[1] german_model_name = config.NER_MODELS_LIST[2] spanish_model_name = config.NER_MODELS_LIST[3] multilingual_model_name = config.NER_MODELS_LIST[4] else: - # Load default models - model_name = [ - "FacebookAI/xlm-roberta-large-finetuned-conll03-english", - "FacebookAI/xlm-roberta-large-finetuned-conll02-dutch", - "FacebookAI/xlm-roberta-large-finetuned-conll03-german", - "FacebookAI/xlm-roberta-large-finetuned-conll02-spanish", - "Babelscape/wikineural-multilingual-ner" - ] + # Load models + model_name = ["FacebookAI/xlm-roberta-large-finetuned-conll03-english", + "FacebookAI/xlm-roberta-large-finetuned-conll02-dutch", + "FacebookAI/xlm-roberta-large-finetuned-conll03-german", + "FacebookAI/xlm-roberta-large-finetuned-conll02-spanish", + "Babelscape/wikineural-multilingual-ner"] + english_model_name = model_name[0] dutch_model_name = model_name[1] german_model_name = model_name[2] spanish_model_name = model_name[3] multilingual_model_name = model_name[4] - - # Load English model and tokenizer + self.en_tokenizer = AutoTokenizer.from_pretrained(english_model_name) self.en_model = AutoModelForTokenClassification.from_pretrained(english_model_name).to(self.device) - self.en_ner_pipeline = pipeline( - "ner", - model=self.en_model, - tokenizer=self.en_tokenizer, - aggregation_strategy="simple" - ) + self.en_ner_pipeline = pipeline("ner", model=self.en_model, tokenizer=self.en_tokenizer, aggregation_strategy="simple") - # Load Dutch model and tokenizer self.nl_tokenizer = AutoTokenizer.from_pretrained(dutch_model_name) self.nl_model = AutoModelForTokenClassification.from_pretrained(dutch_model_name).to(self.device) - self.nl_ner_pipeline = pipeline( - "ner", - model=self.nl_model, - tokenizer=self.nl_tokenizer, - aggregation_strategy="simple" - ) + self.nl_ner_pipeline = pipeline("ner", model=self.nl_model, tokenizer=self.nl_tokenizer, aggregation_strategy="simple") - # Load German model and tokenizer self.de_tokenizer = AutoTokenizer.from_pretrained(german_model_name) self.de_model = AutoModelForTokenClassification.from_pretrained(german_model_name).to(self.device) - self.de_ner_pipeline = pipeline( - "ner", - model=self.de_model, - tokenizer=self.de_tokenizer, - aggregation_strategy="simple" - ) + self.de_ner_pipeline = pipeline("ner", model=self.de_model, tokenizer=self.de_tokenizer, aggregation_strategy="simple") - # Load Spanish model and tokenizer self.es_tokenizer = AutoTokenizer.from_pretrained(spanish_model_name) self.es_model = AutoModelForTokenClassification.from_pretrained(spanish_model_name).to(self.device) - self.es_ner_pipeline = pipeline( - "ner", - model=self.es_model, - tokenizer=self.es_tokenizer, - aggregation_strategy="simple" - ) + self.es_ner_pipeline = pipeline("ner", model=self.es_model, tokenizer=self.es_tokenizer, aggregation_strategy="simple") - # Load Multilingual model and tokenizer self.multi_tokenizer = AutoTokenizer.from_pretrained(multilingual_model_name) self.multi_model = AutoModelForTokenClassification.from_pretrained(multilingual_model_name).to(self.device) - self.multi_ner_pipeline = pipeline( - "ner", - model=self.multi_model, - tokenizer=self.multi_tokenizer, - aggregation_strategy="simple" - ) + self.multi_ner_pipeline = pipeline("ner", model=self.multi_model, tokenizer=self.multi_tokenizer, aggregation_strategy="simple") - # Set the minimum token length based on the maximum length of the tokenizers - self.min_token_length = math.ceil(min( - self.en_tokenizer.max_len_single_sentence, - self.multi_tokenizer.max_len_single_sentence - ) * 0.9) + self.min_token_length = math.ceil(min(self.en_tokenizer.max_len_single_sentence, self.multi_tokenizer.max_len_single_sentence) * 0.9) - # Set the tokenizer based on the maximum length of the tokenizers self.tokenizer = self.en_tokenizer if self.en_tokenizer.max_len_single_sentence <= self.multi_tokenizer.max_len_single_sentence else self.multi_tokenizer def ner_data(self, data, pos): """ - Formats the Named Entity Recognition (NER) data. - - Args: - data (List[Dict[str, Any]]): A list of dictionaries containing entity information. - pos (List[str]): A list of valid entity groups. - - Returns: - List[Dict[str, Any]]: A list of dictionaries with the formatted entity data. + Formats NER (Named Entity Recognition) files. """ - # Filter the data based on the entity group and return the desired keys - return [ - { - 'entity_group': ix['entity_group'], # The entity group - 'score': ix['score'], # The score of the entity - 'word': ix['word'], # The word of the entity - 'key': f"{ix['start']}{ix['end']}", # The unique key for the entity - 'start': ix['start'], # The start position of the entity - 'end': ix['end'] # The end position of the entity - } - for ix in data # Iterate over each entity in the data - if ix['entity_group'] in pos # Check if the entity group is in the valid entity groups - ] + return [{'entity_group': ix['entity_group'], 'score': ix['score'], 'word': ix['word'], 'key': str(ix['start']) + str(ix['end']), 'start': ix['start'], 'end': ix['end']} for ix in data if ix['entity_group'] in pos] def filter_ner_data(self, data, keys): """ @@ -156,19 +87,12 @@ def filter_ner_data(self, data, keys): Returns: list: A list of unique entity data dictionaries with the highest score for each key. """ - # Dictionary to store the unique data with the highest score for each key unique_data = {} - - # Iterate over each item in the data for item in data: - # Check if the key of the item is in the keys if item['key'] in keys: - # Check if the key is already in unique_data - # If it is, compare the scores and update the value if the new score is higher if item['key'] not in unique_data or item['score'] > unique_data[item['key']]['score']: unique_data[item['key']] = item - # Return the values of the unique_data dictionary return list(unique_data.values()) def anonymize_text(self, text, filtered_data): @@ -182,41 +106,21 @@ def anonymize_text(self, text, filtered_data): Returns: str: The anonymized text with entity type tags. """ - # Initialize an empty list to store the RecognizerResult objects - analyzer_result = [] - - # Iterate over each item in the filtered_data - for item in filtered_data: - # Check the entity_group of the item - if item['entity_group'] == 'PER': - # Create a RecognizerResult object with the corresponding entity_type and other attributes - analyzer_result.append(RecognizerResult(entity_type="PERSON", - start=item['start'], - end=item['end'], - score=item['score'])) - elif item['entity_group'] == 'LOC': - # Create a RecognizerResult object with the corresponding entity_type and other attributes - analyzer_result.append(RecognizerResult(entity_type="LOCATION", - start=item['start'], - end=item['end'], - score=item['score'])) - elif item['entity_group'] == 'ORG': - # Create a RecognizerResult object with the corresponding entity_type and other attributes - analyzer_result.append(RecognizerResult(entity_type="ORGANISATION", - start=item['start'], - end=item['end'], - score=item['score'])) - - # Get the length of the text + analyzer_result = list() + for items in filtered_data: + if items['entity_group'] == 'PER': + analyzer_result.append(RecognizerResult(entity_type="PERSON", start=items['start'], end=items['end'], score=items['score'])) + elif items['entity_group'] == 'LOC': + analyzer_result.append(RecognizerResult(entity_type="LOCATION", start=items['start'], end=items['end'], score=items['score'])) + elif items['entity_group'] == 'ORG': + analyzer_result.append(RecognizerResult(entity_type="ORGANISATION", start=items['start'], end=items['end'], score=items['score'])) + text_length = len(text) - - # Filter out the analyzer_result entries that have invalid start or end positions analyzer_result = [entry for entry in analyzer_result if 0 <= entry.start < text_length and 0 < entry.end <= text_length] - - # Replace words in the text with their entity_type tags using the anonymize function of the engine + # Replace words in the text with their entity_type tags return self.engine.anonymize(text=text, analyzer_results=analyzer_result) - def ner_ensemble(self, ner_results: List[Dict[str, Any]], t: float) -> List[Dict[str, Any]]: + def ner_ensemble(self, ner_results, t): """ Applies an ensemble method for NER. @@ -227,95 +131,63 @@ def ner_ensemble(self, ner_results: List[Dict[str, Any]], t: float) -> List[Dict Returns: List[Dict[str, Any]]: A sorted list of dictionaries containing the filtered NER results. """ - # Initialize a dictionary to store the count and sum of scores for each key ner_keys = defaultdict(lambda: [0, 0]) - - # Iterate over each item in the ner_results for entity in ner_results: - # For each item, increment the count and add the score to the sum ner_keys[entity['key']][0] += 1 ner_keys[entity['key']][1] += entity['score'] - # Filter out the keys that have a score below the threshold ner_keys = [key for key, val in ner_keys.items() if val[1] / val[0] >= t] - # Filter out the NER results that have a key not in the ner_keys filter_ner_results = self.filter_ner_data(ner_results, ner_keys) - # Sort the filtered NER results by start position filter_ner_results.sort(key=lambda x: x['start']) - # Return the filtered NER results return filter_ner_results - def ner_process(self, text: str, positional_tags: List[str], ner_confidence_threshold: float, language: str) -> str: - """ - Executes NER Process to remove the positional tags, PER, LOC, ORG, MISC. - + def ner_process(self, text, positional_tags, ner_confidence_threshold, language): + """_summary_ + Executes NER Process to remove the positional tags, PER, LOC, ORG, MISC. Args: - text (str): text from which postional tags need to be recognised + text (string): text from which postional tags need to be recognised positional_tags (list): pass tags as ['PER', 'LOC'] (default), also supports 'ORG', 'MISC' - ner_confidence_threshold (float): NER Confidence - language (str): language model which need to be used, currently supports ENGLISH, DUTCH + ner_confidence_threshold (int): NER Confidence + language (string): language model which need to be used, currently supports ENGLISH, DUTCH Returns: - str: a string of words sorted based on length for the provided positional tags which meets the threshold + list: a list of words sorted based on length for the provided positional tags which meets the threshold """ - # Initialize a list to store the cleaned text - ner_clean_text = [] - - # Get the length of the input text + ner_clean_text = list() + # text length text_token_length = len(self.tokenizer.tokenize(text)) - # Calculate the number of parts the text needs to be split into - num_parts = math.ceil(text_token_length / self.min_token_length) + # parts it need to get split + num_parts = math.ceil(text_token_length/self.min_token_length) - # If the text doesn't need to be split, just add it to the list if num_parts == 0: texts = [text] else: - # Split the text into chunks texts = self.split_text(text, self.min_token_length, self.tokenizer) - # Iterate over each chunk of text for text in texts: - # Initialize a list to store the NER results ner_results = [] - - # Get the NER results for the multi-lingual model ner_results.append(self.ner_data(self.multi_ner_pipeline(text), positional_tags)) - - # Get the NER results for the English model ner_results.append(self.ner_data(self.en_ner_pipeline(text), positional_tags)) - - # If the language is Dutch, get the NER results for the Dutch model if language == 'DUTCH': ner_results.append(self.ner_data(self.nl_ner_pipeline(text), positional_tags)) - - # If the language is German, get the NER results for the German model elif language == 'GERMAN': ner_results.append(self.ner_data(self.de_ner_pipeline(text), positional_tags)) - - # If the language is Spanish, get the NER results for the Spanish model elif language == 'SPANISH': ner_results.append(self.ner_data(self.es_ner_pipeline(text), positional_tags)) - # Flatten the list of NER results + # flat out the list ner_results = list(itertools.chain.from_iterable(ner_results)) - - # Apply the ensemble method to the NER results ner_results = self.ner_ensemble(ner_results, ner_confidence_threshold) - - # Anonymize the text using the filtered NER results ner_text = self.anonymize_text(text, ner_results).text - - # Add the anonymized text to the list ner_clean_text.append(ner_text) - - # Join the cleaned text into a single string and return it + return ' '.join(ner_clean_text) - def split_text(self, text: str, max_tokens: int, tokenizer) -> List[str]: + def split_text(self, text, max_tokens, tokenizer): """ Splits a given text into chunks based on a maximum token limit. @@ -327,32 +199,22 @@ def split_text(self, text: str, max_tokens: int, tokenizer) -> List[str]: Returns: list: A list of text chunks, each containing a maximum of max_tokens tokens. """ - # Find the sentence boundaries in the text sentence_boundaries = [(m.start(), m.end()) for m in constants.SENTENCE_BOUNDARY_PATTERN.finditer(text)] - # Initialize a list to store the text chunks chunks = [] - - # Initialize variables to keep track of the current chunk current_chunk = [] current_token_count = 0 current_position = 0 - # Iterate over the sentence boundaries for boundary_start, boundary_end in sentence_boundaries: - # Get the sentence sentence = text[current_position:boundary_start+1] - # Update the current position current_position = boundary_end - # Get the number of tokens in the sentence token_count = len(tokenizer(sentence)["input_ids"]) - # If the sentence can fit in the current chunk, add it if current_token_count + token_count <= max_tokens: current_chunk.append(sentence) current_token_count += token_count - # Otherwise, create a new chunk else: chunks.append(''.join(current_chunk)) current_chunk = [sentence] @@ -363,5 +225,4 @@ def split_text(self, text: str, max_tokens: int, tokenizer) -> List[str]: current_chunk.append(last_sentence) chunks.append(''.join(current_chunk)) - # Return the list of chunks return chunks \ No newline at end of file diff --git a/sct/utils/normtext.py b/sct/utils/normtext.py index 1003c0d..17f9274 100644 --- a/sct/utils/normtext.py +++ b/sct/utils/normtext.py @@ -11,127 +11,63 @@ def __init__(self): pass - def fix_bad_unicode( - self, - text: str, - normalization: str = "NFC", - ) -> str: + def fix_bad_unicode(self, text, normalization="NFC"): """ - Fixes broken unicode text using the `ftfy` library. - - This function fixes various issues with unicode text, including mojibake, HTML entities, and other code cruft. - It also normalizes the text to a specified format, which can improve the readability and consistency of the text. - + Fix unicode text that's "broken" using `ftfy `_; + this includes mojibake, HTML entities and other code cruft, + and non-standard forms for display purposes. Args: - text (str): The raw text to be fixed. - normalization (str, optional): The normalization form to use. - Defaults to "NFC". - Possible values are: - - "NFC" (Canonical Decomposition, followed by Canonical Composition): - Combines characters and diacritics written using separate code points, - e.g. converting "e" plus an acute accent modifier into "é". - Unicode can be converted to NFC form without any change in its meaning. - - "NFKC" (Compatibility Decomposition, followed by Canonical Composition): - Applies additional normalizations that can change the meanings of characters, - e.g. replacing ellipsis characters with three periods. - - "NFD" (Canonical Decomposition): - Decomposes characters and diacritics written using separate code points. - - "NFKD" (Compatibility Decomposition): - Decomposes characters and diacritics written using separate code points, - and applies additional normalizations. - - Returns: - str: The fixed and normalized text. + text (str): raw text + normalization ({'NFC', 'NFKC', 'NFD', 'NFKD'}): if 'NFC', + combines characters and diacritics written using separate code points, + e.g. converting "e" plus an acute accent modifier into "é"; unicode + can be converted to NFC form without any change in its meaning! + if 'NFKC', additional normalizations are applied that can change + the meanings of characters, e.g. ellipsis characters will be replaced + with three periods """ - # Trying to fix backslash-replaced strings (via https://stackoverflow.com/a/57192592/4028896) + # trying to fix backslash-replaced strings (via https://stackoverflow.com/a/57192592/4028896) try: - # Decode text using the "latin" encoding and "backslashreplace" error handler text = text.encode("latin", "backslashreplace").decode("unicode-escape") except: pass - # Use the fix_text function from the ftfy library to fix the text return fix_text(text, normalization=normalization) - def fix_strange_quotes(self, text: str) -> str: + def fix_strange_quotes(self, text): """ - Replace strange quotes, i.e., with a single quote ' or a double quote " if it fits better. - - Args: - text (str): The text to be processed. - - Returns: - str: The processed text with strange quotes replaced. + Replace strange quotes, i.e., 〞with a single quote ' or a double quote " if it fits better. """ - # Replace single quotes text = constants.SINGLE_QUOTE_REGEX.sub("'", text) - # Replace double quotes text = constants.DOUBLE_QUOTE_REGEX.sub('"', text) return text - def to_ascii_unicode( - self, - text: str, - no_emoji: bool = True, - ) -> str: + def to_ascii_unicode(self, text, no_emoji=True): """ - Transliterate unicode text into ascii characters. - - This function tries to represent unicode data in ascii characters similar to - what a human with a US keyboard would choose. It works great for languages of - Western origin, and gets worse the farther the language gets from Latin-based - alphabets. It's based on hand-tuned character mappings that also contain ascii - approximations for symbols and non-Latin alphabets. - - Args: - text (str): The text to be transliterated. - no_emoji (bool, optional): Flag to indicate whether to remove emojis or not. Defaults to True. - - Returns: - str: The transliterated text. - + Try to represent unicode data in ascii characters similar to what a human + with a US keyboard would choose. + Works great for languages of Western origin, worse the farther the language + gets from Latin-based alphabets. It's based on hand-tuned character mappings + that also contain ascii approximations for symbols and non-Latin alphabets. """ - # Normalize quotes before since this improves transliteration quality. + # normalize quotes before since this improves transliteration quality text = self.fix_strange_quotes(text) - # If no_emoji flag is set to False, remove emojis from the text. if not no_emoji: text = demojize(text, use_aliases=True) - # Convert unicode characters to their ascii approximations. text = unidecode(text) return text - def normalize_whitespace( - self, - text: str, - strip_lines: bool = True, - no_line_breaks: bool = False, - keep_two_line_breaks: bool = False, - ) -> str: + def normalize_whitespace(self, text, strip_lines=True, no_line_breaks=False, keep_two_line_breaks=False): """ - Normalize whitespace in the given text. - - This function replaces one or more spacings with a single space, and one - or more line breaks with a single newline. Also, it strips leading/trailing whitespace. - - Args: - text (str): The input text to be processed. - strip_lines (bool, optional): Flag to indicate whether to strip leading/trailing whitespace from each line. Defaults to True. - no_line_breaks (bool, optional): Flag to indicate whether to remove all line breaks or not. Defaults to False. - keep_two_line_breaks (bool, optional): Flag to indicate whether to keep two consecutive line breaks or not. Defaults to False. - - Returns: - str: The processed text with normalized whitespace. + Given ``text`` str, replace one or more spacings with a single space, and one + or more line breaks with a single newline. Also strip leading/trailing whitespace. """ - - # Strip leading/trailing whitespace from each line if strip_lines flag is set to True. if strip_lines: text = "\n".join([x.strip() for x in text.splitlines()]) - # If no_line_breaks flag is set to True, remove all line breaks. - # Otherwise, replace consecutive line breaks with one newline. if no_line_breaks: text = constants.MULTI_WHITESPACE_TO_ONE_REGEX.sub(" ", text) else: @@ -144,5 +80,4 @@ def normalize_whitespace( " ", constants.LINEBREAK_REGEX.sub(r"\n", text) ) - # Strip leading/trailing whitespace from the final processed text. - return text.strip() + return text.strip() \ No newline at end of file diff --git a/sct/utils/special.py b/sct/utils/special.py index f70a8d3..8259f47 100644 --- a/sct/utils/special.py +++ b/sct/utils/special.py @@ -8,106 +8,43 @@ class ProcessSpecialSymbols: def __init__(self): pass - def replace_currency_symbols( - self, - text: str, - replace_with: str = "" - ) -> str: + def replace_currency_symbols(self, text, replace_with=""): """ - Replaces currency symbols in the given text with the specified replacement string. - + Replace currency symbols in ``text`` str with string specified by ``replace_with`` str. Args: - text (str): The raw text. - replace_with (str, optional): If None (default), replaces symbols with their - standard 3-letter abbreviations (e.g. '$' with 'USD', '£' with 'GBP'). - Otherwise, passes in a string with which to replace all symbols - (e.g. "*CURRENCY*"). - - Returns: - str: The modified text with currency symbols replaced. + text (str): raw text + replace_with (str): if None (default), replace symbols with + their standard 3-letter abbreviations (e.g. '$' with 'USD', '£' with 'GBP'); + otherwise, pass in a string with which to replace all symbols + (e.g. "*CURRENCY*") """ - # If `replace_with` is None, replace symbols with their standard 3-letter abbreviations if replace_with is None: - # Iterate over the keys and values in CURRENCIES - for symbol, abbreviation in constants.CURRENCIES.items(): - # Replace the symbol in the text with its standard abbreviation - text = text.replace(symbol, abbreviation) + for k, v in constants.CURRENCIES.items(): + text = text.replace(k, v) return text - # If `replace_with` is not None, use regular expressions to replace all currency symbols else: - # Use the pre-compiled regular expression CURRENCY_REGEX to find all currency symbols - # in the text and replace them with the specified replacement string return constants.CURRENCY_REGEX.sub(replace_with, text) - def remove_isolated_letters( - self, - text: str - ) -> str: + def remove_isolated_letters(self, text): """ Removes any isolated letters which doesn't add any value to the text. - - Args: - text (str): The raw text to be processed. - - Returns: - str: The modified text with isolated letters removed. """ - # Use the pre-compiled regular expression ISOLATED_LETTERS_REGEX to find all isolated - # letters in the text and replace them with an empty string - cleaned_text: str = constants.ISOLATED_LETTERS_REGEX.sub('', text) + cleaned_text = constants.ISOLATED_LETTERS_REGEX.sub('', text) return cleaned_text - def remove_isolated_special_symbols( - self, - text: str - ) -> str: + def remove_isolated_special_symbols(self, text): """ Removes any isolated symbols which shouldn't be present in the text. - - This includes: - - [] content, usually images or file locations - - {} content, usually HTML links - - Any isolated special symbols which don't add any value to the text - - Args: - text (str): The raw text. - - Returns: - str: The modified text with isolated special symbols removed. """ - # Remove [] content, usually images or file locations - cleaned_text: str = re.sub(r'\[[^\]]+\]', '', text) - # Remove {} content, usually HTML links - cleaned_text: str = re.sub(r'\{[^}]+\}', '', cleaned_text) - # Remove any isolated special symbols which don't add any value to the text - cleaned_text: str = constants.ISOLATED_SPECIAL_SYMBOLS_REGEX.sub('', cleaned_text) - # Remove any remaining isolated special symbols which may have been missed - cleaned_text: str = re.sub(r"(? str: - """ - Removes all punctuation characters from the given text. - - Args: - text (str): The input text. - - Returns: - str: The modified text with punctuation characters removed. - """ - # Create a regular expression pattern of all punctuation characters - # using the string.punctuation string. + def remove_punctuation(self, text): chars = re.escape(string.punctuation) - - # Use re.sub() to replace all occurrences of punctuation characters with an empty string. - # The pattern is created by escaping all punctuation characters using re.escape(). - # The '[' and ']' are added to the pattern to match any character in the set. - # The '+' after the '[' and before the ']' is a quantifier that matches one or more occurrences. - # The '' (empty string) is the replacement string, which replaces all matches with an empty string. - return re.sub('['+chars+']', '', text) + return re.sub('['+chars+']', '',text) \ No newline at end of file diff --git a/sct/utils/stopwords.py b/sct/utils/stopwords.py index 47cc616..089f9d2 100644 --- a/sct/utils/stopwords.py +++ b/sct/utils/stopwords.py @@ -10,53 +10,26 @@ def __init__(self): self.STOP_WORDS_DE = sw.words('german') self.STOP_WORDS_ES = sw.words('spanish') - def remove_stopwords(self, text: str, lan: str) -> str: + def remove_stopwords(self, text, lan): """ Removes stopwords based on the language. - - Parameters: - text (str): The input text to have stopwords removed from. - lan (str): The language of the text. - - Returns: - str: The text with all stopwords removed. """ + if lan == 'DUTCH': + stop_words = self.STOP_WORDS_NL + elif lan == 'ENGLISH': + stop_words = self.STOP_WORDS_EN + elif lan == 'GERMAN': + stop_words = self.STOP_WORDS_DE + elif lan == 'SPANISH': + stop_words = self.STOP_WORDS_ES - # Determine the stopwords based on the language - stop_words = { - 'DUTCH': self.STOP_WORDS_NL, - 'ENGLISH': self.STOP_WORDS_EN, - 'GERMAN': self.STOP_WORDS_DE, - 'SPANISH': self.STOP_WORDS_ES - }.get(lan, []) - - # Split the text into words and remove stopwords text = text.split() return " ".join([word for word in text if word not in stop_words]) - def remove_words_from_string(self, text: str, words_to_remove: list[str]) -> str: - """ - Removes a list of words from a given text. - - This function takes a string of text and a list of words to remove, - and returns a new string with all occurrences of the words removed. - - Parameters - ---------- - text : str - The input text to have words removed from. - words_to_remove : list[str] - A list of words to remove from the text. - - Returns - ------- - str - The text with all occurrences of the words removed. - """ - + def remove_words_from_string(self, text, words_to_remove): # Join the words with the "|" (OR) operator in regex to create a pattern pattern = r'\b(?:' + '|'.join(re.escape(word) for word in words_to_remove) + r')\b' # Use re.sub() to replace all matches with an empty string result_text = re.sub(pattern, '', text) - return result_text + return result_text \ No newline at end of file diff --git a/setup.py b/setup.py index 20792e5..2642568 100644 --- a/setup.py +++ b/setup.py @@ -2,7 +2,7 @@ setup( name='SqueakyCleanText', - version='0.2.5', + version='0.2.6', author='Rehan Fazal', description='A comprehensive text cleaning and preprocessing pipeline.', long_description=open('README.md', encoding='utf-8').read(), From e1f3adaca8f0f6549ea82981d3ee68b8c6253662 Mon Sep 17 00:00:00 2001 From: Rehan Fazal Date: Sun, 18 Aug 2024 13:06:00 +0200 Subject: [PATCH 2/2] Develop (#33) * Intial commit of squeaky clean text * updated the sct.py script with modular code * updated the sct.py script with pipeline method, which would ideally would help to make changes in the processing easier * removed unnecessary direction code * adding to do list * adding to do list * added requiremnt.txt file * added setup.py file * added test cases * updated config file * merging back * Develop (#2) (#3) * Intial commit of squeaky clean text * updated the sct.py script with modular code * updated the sct.py script with pipeline method, which would ideally would help to make changes in the processing easier * removed unnecessary direction code * adding to do list * adding to do list * added requiremnt.txt file * added setup.py file * added test cases * updated config file * merging back * rebase * update the license * added German and Spanish support * Updated file for pypi * Updated readme file * Add GitHub Actions workflow for publishing to PyPI * Updated readme file * Updated readme file * added the username to the publish.yml * update the API vriable name * update the API user name * Bump version to 0.1.1 * updated the readme file * updated the version * Update NER Process and added tag removal * Updated congig file * updated the code to have the option to not output language * fixed the bug for NER which was refrencing to the wrong model variable names, add the gpu support * fixed the Anonomyser Engine * fixed the Anonomyser Engine * added the test.yml file * added the test.yml file * added the test.yml file * added the German and Spanish language support in lingua * added the ability in the config to change the model name * added the ability in the config to change the model name * added the ability in the config to change the model name and fixed spanish model name * squased some bugs * added the language passing support * Refactored the code * fixed typing issue * reverted the refactor * Added the flow diagram of the pacckage in the readme --- README.md | 2 ++ resources/sct_flow.png | Bin 0 -> 127313 bytes 2 files changed, 2 insertions(+) create mode 100644 resources/sct_flow.png diff --git a/README.md b/README.md index e8cb273..191ee4a 100644 --- a/README.md +++ b/README.md @@ -19,6 +19,8 @@ SqueakyCleanText simplifies the process by automatically addressing common text - Supports English, Dutch, German, and Spanish languages. - Provides text formatted for both Language Model processing and Statistical Model processing. +![Default Flow of cleaning Text](resources/sct_flow.png) + ##### Benefits for Statistical Models When working with statistical models, further optimization is often required, such as removing stopwords, special symbols, and punctuation. 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