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evaluation.py
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evaluation.py
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import sys
import io, os
import numpy as np
import logging
import argparse
from prettytable import PrettyTable
import torch
import transformers
from transformers import AutoModel, AutoTokenizer
# Set up logger
logging.basicConfig(format='%(asctime)s : %(message)s', level=logging.DEBUG)
# Set PATHs
PATH_TO_SENTEVAL = './SentEval'
PATH_TO_DATA = './SentEval/data'
# Import SentEval
sys.path.insert(0, PATH_TO_SENTEVAL)
import senteval
def print_table(task_names, scores):
tb = PrettyTable()
tb.field_names = task_names
tb.add_row(scores)
print(tb)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model_name_or_path", type=str,
help="Transformers' model name or path")
parser.add_argument("--pooler", type=str,
choices=['cls', 'cls_before_pooler', 'avg', 'avg_top2', 'avg_first_last'],
default='cls',
help="Which pooler to use")
parser.add_argument("--mode", type=str,
choices=['dev', 'test', 'fasttest'],
default='test',
help="What evaluation mode to use (dev: fast mode, dev results; test: full mode, test results); fasttest: fast mode, test results")
parser.add_argument("--task_set", type=str,
choices=['sts', 'transfer', 'full', 'na'],
default='sts',
help="What set of tasks to evaluate on. If not 'na', this will override '--tasks'")
parser.add_argument("--tasks", type=str, nargs='+',
default=['STS12', 'STS13', 'STS14', 'STS15', 'STS16',
'MR', 'CR', 'MPQA', 'SUBJ', 'SST2', 'TREC', 'MRPC',
'SICKRelatedness', 'STSBenchmark'],
help="Tasks to evaluate on. If '--task_set' is specified, this will be overridden")
args = parser.parse_args()
# Load transformers' model checkpoint
model = AutoModel.from_pretrained(args.model_name_or_path)
tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = model.to(device)
# Set up the tasks
if args.task_set == 'sts':
args.tasks = ['STS12', 'STS13', 'STS14', 'STS15', 'STS16', 'STSBenchmark', 'SICKRelatedness']
elif args.task_set == 'transfer':
args.tasks = ['MR', 'CR', 'MPQA', 'SUBJ', 'SST2', 'TREC', 'MRPC']
elif args.task_set == 'full':
args.tasks = ['STS12', 'STS13', 'STS14', 'STS15', 'STS16', 'STSBenchmark', 'SICKRelatedness']
args.tasks += ['MR', 'CR', 'MPQA', 'SUBJ', 'SST2', 'TREC', 'MRPC']
# Set params for SentEval
if args.mode == 'dev' or args.mode == 'fasttest':
# Fast mode
params = {'task_path': PATH_TO_DATA, 'usepytorch': True, 'kfold': 5}
params['classifier'] = {'nhid': 0, 'optim': 'rmsprop', 'batch_size': 128,
'tenacity': 3, 'epoch_size': 2}
elif args.mode == 'test':
# Full mode
params = {'task_path': PATH_TO_DATA, 'usepytorch': True, 'kfold': 10}
params['classifier'] = {'nhid': 0, 'optim': 'adam', 'batch_size': 64,
'tenacity': 5, 'epoch_size': 4}
else:
raise NotImplementedError
# SentEval prepare and batcher
def prepare(params, samples):
return
def batcher(params, batch, max_length=None):
# Handle rare token encoding issues in the dataset
if len(batch) >= 1 and len(batch[0]) >= 1 and isinstance(batch[0][0], bytes):
batch = [[word.decode('utf-8') for word in s] for s in batch]
sentences = [' '.join(s) for s in batch]
# Tokenization
if max_length is not None:
batch = tokenizer.batch_encode_plus(
sentences,
return_tensors='pt',
padding=True,
max_length=max_length,
truncation=True
)
else:
batch = tokenizer.batch_encode_plus(
sentences,
return_tensors='pt',
padding=True,
)
# Move to the correct device
for k in batch:
batch[k] = batch[k].to(device)
# Get raw embeddings
with torch.no_grad():
outputs = model(**batch, output_hidden_states=True, return_dict=True)
last_hidden = outputs.last_hidden_state
pooler_output = outputs.pooler_output
hidden_states = outputs.hidden_states
# Apply different poolers
if args.pooler == 'cls':
# There is a linear+activation layer after CLS representation
return pooler_output.cpu()
elif args.pooler == 'cls_before_pooler':
return last_hidden[:, 0].cpu()
elif args.pooler == "avg":
return ((last_hidden * batch['attention_mask'].unsqueeze(-1)).sum(1) / batch['attention_mask'].sum(-1).unsqueeze(-1)).cpu()
elif args.pooler == "avg_first_last":
first_hidden = hidden_states[1]
last_hidden = hidden_states[-1]
pooled_result = ((first_hidden + last_hidden) / 2.0 * batch['attention_mask'].unsqueeze(-1)).sum(1) / batch['attention_mask'].sum(-1).unsqueeze(-1)
return pooled_result.cpu()
elif args.pooler == "avg_top2":
second_last_hidden = hidden_states[-2]
last_hidden = hidden_states[-1]
pooled_result = ((last_hidden + second_last_hidden) / 2.0 * batch['attention_mask'].unsqueeze(-1)).sum(1) / batch['attention_mask'].sum(-1).unsqueeze(-1)
return pooled_result.cpu()
else:
raise NotImplementedError
results = {}
for task in args.tasks:
se = senteval.engine.SE(params, batcher, prepare)
result = se.eval(task)
results[task] = result
# Print evaluation results
if args.mode == 'dev':
print("------ %s ------" % (args.mode))
task_names = []
scores = []
for task in ['STSBenchmark', 'SICKRelatedness']:
task_names.append(task)
if task in results:
scores.append("%.2f" % (results[task]['dev']['spearman'][0] * 100))
else:
scores.append("0.00")
print_table(task_names, scores)
task_names = []
scores = []
for task in ['MR', 'CR', 'SUBJ', 'MPQA', 'SST2', 'TREC', 'MRPC']:
task_names.append(task)
if task in results:
scores.append("%.2f" % (results[task]['devacc']))
else:
scores.append("0.00")
task_names.append("Avg.")
scores.append("%.2f" % (sum([float(score) for score in scores]) / len(scores)))
print_table(task_names, scores)
elif args.mode == 'test' or args.mode == 'fasttest':
print("------ %s ------" % (args.mode))
task_names = []
scores = []
for task in ['STS12', 'STS13', 'STS14', 'STS15', 'STS16', 'STSBenchmark', 'SICKRelatedness']:
task_names.append(task)
if task in results:
if task in ['STS12', 'STS13', 'STS14', 'STS15', 'STS16']:
scores.append("%.2f" % (results[task]['all']['spearman']['all'] * 100))
else:
scores.append("%.2f" % (results[task]['test']['spearman'].correlation * 100))
else:
scores.append("0.00")
task_names.append("Avg.")
scores.append("%.2f" % (sum([float(score) for score in scores]) / len(scores)))
print_table(task_names, scores)
task_names = []
scores = []
for task in ['MR', 'CR', 'SUBJ', 'MPQA', 'SST2', 'TREC', 'MRPC']:
task_names.append(task)
if task in results:
scores.append("%.2f" % (results[task]['acc']))
else:
scores.append("0.00")
task_names.append("Avg.")
scores.append("%.2f" % (sum([float(score) for score in scores]) / len(scores)))
print_table(task_names, scores)
if __name__ == "__main__":
main()