File size: 20,157 Bytes
060ac52 |
1 2 3 4 5 6 7 8 9 10 11 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 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 |
import random
import torch
from transformers import BertTokenizer, BertForMaskedLM
from nltk.corpus import stopwords
import nltk
# Ensure stopwords are downloaded
try:
nltk.data.find('corpora/stopwords')
except LookupError:
nltk.download('stopwords')
class MaskingProcessor:
def __init__(self):
self.tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
self.model = BertForMaskedLM.from_pretrained("bert-base-uncased")
self.stop_words = set(stopwords.words('english'))
def remove_stopwords(self, words):
"""
Remove stopwords from the given list of words.
Args:
words (list): List of words.
Returns:
list: List of non-stop words.
"""
return [word for word in words if word.lower() not in self.stop_words]
def adjust_ngram_indices(self, original_words, common_ngrams):
"""
Adjust indices of common n-grams after removing stopwords.
Args:
original_words (list): Original list of words.
common_ngrams (dict): Common n-grams and their indices.
Returns:
dict: Adjusted common n-grams with updated indices.
"""
non_stop_words = self.remove_stopwords(original_words)
original_to_non_stop = []
non_stop_idx = 0
for original_idx, word in enumerate(original_words):
if word.lower() not in self.stop_words:
original_to_non_stop.append((original_idx, non_stop_idx))
non_stop_idx += 1
adjusted_ngrams = {}
for ngram, positions in common_ngrams.items():
adjusted_positions = []
for start, end in positions:
try:
new_start = next(non_stop for orig, non_stop in original_to_non_stop if orig == start)
new_end = next(non_stop for orig, non_stop in original_to_non_stop if orig == end)
adjusted_positions.append((new_start, new_end))
except StopIteration:
continue # Skip if indices cannot be mapped
adjusted_ngrams[ngram] = adjusted_positions
return adjusted_ngrams
def mask_sentence_random(self, sentence, common_ngrams):
"""
Mask words in the sentence based on the specified rules after removing stopwords.
"""
original_words = sentence.split()
print(f' ---- original_words : {original_words} ----- ')
non_stop_words = self.remove_stopwords(original_words)
print(f' ---- non_stop_words : {non_stop_words} ----- ')
adjusted_ngrams = self.adjust_ngram_indices(original_words, common_ngrams)
print(f' ---- common_ngrams : {common_ngrams} ----- ')
print(f' ---- adjusted_ngrams : {adjusted_ngrams} ----- ')
mask_indices = []
# Extract n-gram positions in non-stop words
ngram_positions = [pos for positions in adjusted_ngrams.values() for pos in positions]
# Mask a word before the first common n-gram
if ngram_positions:
print(f' ---- ngram_positions : {ngram_positions} ----- ')
first_ngram_start = ngram_positions[0][0]
print(f' ---- first_ngram_start : {first_ngram_start} ----- ')
if first_ngram_start > 0:
mask_index_before_ngram = random.randint(0, first_ngram_start-1)
print(f' ---- mask_index_before_ngram : {mask_index_before_ngram} ----- ')
mask_indices.append(mask_index_before_ngram)
# Mask words between common n-grams
for i in range(len(ngram_positions) - 1):
end_prev = ngram_positions[i][1]
print(f' ---- end_prev : {end_prev} ----- ') # END INDICE FROM PREV LOOP FUNKNLKNLKNLKNLKNLKNLSKDNFLKSDHJFLSDJKFH:KLSDHF:LHKSDF:HJKLDFS:HJKLDFSHJK:
start_next = ngram_positions[i + 1][0]
print(f' ---- start_next : {start_next} ----- ')
if start_next > end_prev + 1:
mask_index_between_ngrams = random.randint(end_prev + 1, start_next - 1)
print(f' ---- mask_index_between_ngrams : {mask_index_between_ngrams} ----- ')
mask_indices.append(mask_index_between_ngrams)
# Mask a word after the last common n-gram
last_ngram_end = ngram_positions[-1][1]
if last_ngram_end < len(non_stop_words) - 1:
print(f' ---- last_ngram_end : {last_ngram_end} ----- ')
mask_index_after_ngram = random.randint(last_ngram_end + 1, len(non_stop_words) - 1)
print(f' ---- mask_index_after_ngram : {mask_index_after_ngram} ----- ')
mask_indices.append(mask_index_after_ngram)
# Create mapping from non-stop words to original indices
non_stop_to_original = {}
non_stop_idx = 0
for orig_idx, word in enumerate(original_words):
if word.lower() not in self.stop_words:
non_stop_to_original[non_stop_idx] = orig_idx
non_stop_idx += 1
# Map mask indices from non-stop word positions to original positions
print(f' ---- non_stop_to_original : {non_stop_to_original} ----- ')
original_mask_indices = [non_stop_to_original[idx] for idx in mask_indices]
print(f' ---- original_mask_indices : {original_mask_indices} ----- ')
# Apply masks to the original sentence
masked_words = original_words.copy()
for idx in original_mask_indices:
masked_words[idx] = self.tokenizer.mask_token
return " ".join(masked_words)
def mask_sentence_pseudorandom(self, sentence, common_ngrams):
"""
Mask words in the sentence based on the specified rules after removing stopwords.
"""
random.seed(42)
original_words = sentence.split()
print(f' ---- original_words : {original_words} ----- ')
non_stop_words = self.remove_stopwords(original_words)
print(f' ---- non_stop_words : {non_stop_words} ----- ')
adjusted_ngrams = self.adjust_ngram_indices(original_words, common_ngrams)
print(f' ---- common_ngrams : {common_ngrams} ----- ')
print(f' ---- adjusted_ngrams : {adjusted_ngrams} ----- ')
mask_indices = []
# Extract n-gram positions in non-stop words
ngram_positions = [pos for positions in adjusted_ngrams.values() for pos in positions]
# Mask a word before the first common n-gram
if ngram_positions:
print(f' ---- ngram_positions : {ngram_positions} ----- ')
first_ngram_start = ngram_positions[0][0]
print(f' ---- first_ngram_start : {first_ngram_start} ----- ')
if first_ngram_start > 0:
mask_index_before_ngram = random.randint(0, first_ngram_start-1)
print(f' ---- mask_index_before_ngram : {mask_index_before_ngram} ----- ')
mask_indices.append(mask_index_before_ngram)
# Mask words between common n-grams
for i in range(len(ngram_positions) - 1):
end_prev = ngram_positions[i][1]
print(f' ---- end_prev : {end_prev} ----- ')
start_next = ngram_positions[i + 1][0]
print(f' ---- start_next : {start_next} ----- ')
if start_next > end_prev + 1:
mask_index_between_ngrams = random.randint(end_prev + 1, start_next - 1)
print(f' ---- mask_index_between_ngrams : {mask_index_between_ngrams} ----- ')
mask_indices.append(mask_index_between_ngrams)
# Mask a word after the last common n-gram
last_ngram_end = ngram_positions[-1][1]
if last_ngram_end < len(non_stop_words) - 1:
print(f' ---- last_ngram_end : {last_ngram_end} ----- ')
mask_index_after_ngram = random.randint(last_ngram_end + 1, len(non_stop_words) - 1)
print(f' ---- mask_index_after_ngram : {mask_index_after_ngram} ----- ')
mask_indices.append(mask_index_after_ngram)
# Create mapping from non-stop words to original indices
non_stop_to_original = {}
non_stop_idx = 0
for orig_idx, word in enumerate(original_words):
if word.lower() not in self.stop_words:
non_stop_to_original[non_stop_idx] = orig_idx
non_stop_idx += 1
# Map mask indices from non-stop word positions to original positions
print(f' ---- non_stop_to_original : {non_stop_to_original} ----- ')
original_mask_indices = [non_stop_to_original[idx] for idx in mask_indices]
print(f' ---- original_mask_indices : {original_mask_indices} ----- ')
# Apply masks to the original sentence
masked_words = original_words.copy()
for idx in original_mask_indices:
masked_words[idx] = self.tokenizer.mask_token
return " ".join(masked_words)
def calculate_word_entropy(self, sentence, word_position):
"""
Calculate entropy for a specific word position in the sentence.
Args:
sentence (str): The input sentence
word_position (int): Position of the word to calculate entropy for
Returns:
float: Entropy value for the word
"""
words = sentence.split()
masked_words = words.copy()
masked_words[word_position] = self.tokenizer.mask_token
masked_sentence = " ".join(masked_words)
input_ids = self.tokenizer(masked_sentence, return_tensors="pt")["input_ids"]
mask_token_index = torch.where(input_ids == self.tokenizer.mask_token_id)[1]
with torch.no_grad():
outputs = self.model(input_ids)
logits = outputs.logits
# Get probabilities for the masked position
probs = torch.nn.functional.softmax(logits[0, mask_token_index], dim=-1)
# Calculate entropy: -sum(p * log(p))
entropy = -torch.sum(probs * torch.log(probs + 1e-9))
return entropy.item()
def mask_sentence_entropy(self, sentence, common_ngrams):
"""
Mask words in the sentence based on entropy, following n-gram positioning rules.
Args:
sentence (str): Original sentence
common_ngrams (dict): Common n-grams and their indices
Returns:
str: Masked sentence
"""
original_words = sentence.split()
non_stop_words = self.remove_stopwords(original_words)
adjusted_ngrams = self.adjust_ngram_indices(original_words, common_ngrams)
# Create mapping from non-stop words to original indices
non_stop_to_original = {}
original_to_non_stop = {}
non_stop_idx = 0
for orig_idx, word in enumerate(original_words):
if word.lower() not in self.stop_words:
non_stop_to_original[non_stop_idx] = orig_idx
original_to_non_stop[orig_idx] = non_stop_idx
non_stop_idx += 1
ngram_positions = [pos for positions in adjusted_ngrams.values() for pos in positions]
mask_indices = []
if ngram_positions:
# Handle words before first n-gram
first_ngram_start = ngram_positions[0][0]
if first_ngram_start > 0:
# Calculate entropy for all candidate positions
candidate_positions = range(0, first_ngram_start)
entropies = [(pos, self.calculate_word_entropy(sentence, non_stop_to_original[pos]))
for pos in candidate_positions]
# Select position with highest entropy
mask_indices.append(max(entropies, key=lambda x: x[1])[0])
# Handle words between n-grams
for i in range(len(ngram_positions) - 1):
end_prev = ngram_positions[i][1]
start_next = ngram_positions[i + 1][0]
if start_next > end_prev + 1:
candidate_positions = range(end_prev + 1, start_next)
entropies = [(pos, self.calculate_word_entropy(sentence, non_stop_to_original[pos]))
for pos in candidate_positions]
mask_indices.append(max(entropies, key=lambda x: x[1])[0])
# Handle words after last n-gram
last_ngram_end = ngram_positions[-1][1]
if last_ngram_end < len(non_stop_words) - 1:
candidate_positions = range(last_ngram_end + 1, len(non_stop_words))
entropies = [(pos, self.calculate_word_entropy(sentence, non_stop_to_original[pos]))
for pos in candidate_positions]
mask_indices.append(max(entropies, key=lambda x: x[1])[0])
# Map mask indices to original sentence positions and apply masks
original_mask_indices = [non_stop_to_original[idx] for idx in mask_indices]
masked_words = original_words.copy()
for idx in original_mask_indices:
masked_words[idx] = self.tokenizer.mask_token
return " ".join(masked_words)
def calculate_mask_logits(self, masked_sentence):
"""
Calculate logits for masked tokens in the sentence using BERT.
Args:
masked_sentence (str): Sentence with [MASK] tokens.
Returns:
dict: Masked token indices and their logits.
"""
input_ids = self.tokenizer(masked_sentence, return_tensors="pt")["input_ids"]
mask_token_index = torch.where(input_ids == self.tokenizer.mask_token_id)[1]
with torch.no_grad():
outputs = self.model(input_ids)
logits = outputs.logits
mask_logits = {idx.item(): logits[0, idx].tolist() for idx in mask_token_index}
return mask_logits
def process_sentences(self, sentences, result_dict, method="random"):
"""
Process sentences and calculate logits for masked tokens.
Args:
sentences (list): List of sentences
result_dict (dict): Dictionary of common n-grams
method (str): Masking method ("random" or "entropy")
Returns:
dict: Masked sentences and logits for each sentence
"""
results = {}
for sentence, ngrams in result_dict.items():
if method == "random":
masked_sentence = self.mask_sentence_random(sentence, ngrams)
elif method == "pseudorandom":
masked_sentence = self.mask_sentence_pseudorandom(sentence, ngrams)
else: # entropy
masked_sentence = self.mask_sentence_entropy(sentence, ngrams)
logits = self.calculate_mask_logits(masked_sentence)
results[sentence] = {
"masked_sentence": masked_sentence,
"mask_logits": logits
}
return results
if __name__ == "__main__":
# !!! Working both the cases regardless if the stopword is removed or not
sentences = [
"The quick brown fox jumps over the lazy dog everyday.",
# "A speedy brown fox jumps over a lazy dog.",
# "A swift brown fox leaps over the lethargic dog."
]
result_dict ={
'The quick brown fox jumps over the lazy dog everyday.': {'brown fox': [(2, 3)], 'dog': [(8, 8)]},
# 'A speedy brown fox jumps over a lazy dog.': {'brown fox': [(2, 3)], 'dog': [(8, 8)]},
# 'A swift brown fox leaps over the lethargic dog.': {'brown fox': [(2, 3)], 'dog': [(8, 8)]}
}
processor = MaskingProcessor()
# results_random = processor.process_sentences(sentences, result_dict)
results_entropy = processor.process_sentences(sentences, result_dict, method="random")
# results_entropy = processor.process_sentences(sentences, result_dict, method="entropy", remove_stopwords=False)
for sentence, output in results_entropy.items():
print(f"Original Sentence (Random): {sentence}")
print(f"Masked Sentence (Random): {output['masked_sentence']}")
# print(f"Mask Logits (Random): {output['mask_logits']}")
print(f' type(output["mask_logits"]) : {type(output["mask_logits"])}')
print(f' length of output["mask_logits"] : {len(output["mask_logits"])}')
print(f' output["mask_logits"].keys() : {output["mask_logits"].keys()}')
print('--------------------------------')
for mask_idx, logits in output["mask_logits"].items():
print(f"Logits for [MASK] at position {mask_idx}:")
print(f' logits : {logits[:5]}') # List of logits for all vocabulary tokens
print(f' len(logits) : {len(logits)}')
# -------------------------------------------------------------------------------------------
# def mask_sentence(self, sentence, common_ngrams):
# """
# Mask words in the sentence based on the specified rules after removing stopwords.
# Args:
# sentence (str): Original sentence.
# common_ngrams (dict): Common n-grams and their indices.
# Returns:
# str: Masked sentence.
# """
# original_words = sentence.split()
# print(f' ---- original_words : {original_words} ----- ')
# non_stop_words = self.remove_stopwords(original_words)
# print(f' ---- non_stop_words : {non_stop_words} ----- ')
# adjusted_ngrams = self.adjust_ngram_indices(original_words, common_ngrams)
# print(f' ---- common_ngrams : {common_ngrams} ----- ')
# print(f' ---- adjusted_ngrams : {adjusted_ngrams} ----- ')
# mask_indices = []
# # Extract n-gram positions in non-stop words
# ngram_positions = [pos for positions in adjusted_ngrams.values() for pos in positions]
# print(f' ---- ngram_positions : {ngram_positions} ----- ')
# # Mask a word before the first common n-gram
# if ngram_positions:
# first_ngram_start = ngram_positions[0][0]
# print(f' ---- first_ngram_start : {first_ngram_start} ----- ')
# if first_ngram_start > 0:
# mask_index_before_ngram = random.randint(0, first_ngram_start-1)
# print(f' ---- mask_index_before_ngram : {mask_index_before_ngram} ----- ')
# mask_indices.append(mask_index_before_ngram)
# # Mask words between common n-grams
# for i in range(len(ngram_positions) - 1):
# end_prev = ngram_positions[i][1]
# print(f' ---- end_prev : {end_prev} ----- ')
# start_next = ngram_positions[i + 1][0]
# print(f' ---- start_next : {start_next} ----- ')
# if start_next > end_prev + 1:
# mask_index_between_ngrams = random.randint(end_prev + 1, start_next - 1)
# print(f' ---- mask_index_between_ngrams : {mask_index_between_ngrams} ----- ')
# mask_indices.append(mask_index_between_ngrams)
# # Mask a word after the last common n-gram
# last_ngram_end = ngram_positions[-1][1]
# print(f' ---- last_ngram_end : {last_ngram_end} ----- ')
# if last_ngram_end < len(non_stop_words) - 1:
# mask_index_after_ngram = random.randint(last_ngram_end + 1, len(non_stop_words) - 1)
# print(f' ---- mask_index_after_ngram : {mask_index_after_ngram} ----- ')
# mask_indices.append(mask_index_after_ngram)
# # Map mask indices back to original sentence
# adjusted_indices = [
# orig for orig, non_stop in enumerate(original_words)
# if non_stop in mask_indices
# ]
# # Apply masks to the original sentence
# for idx in adjusted_indices:
# original_words[idx] = self.tokenizer.mask_token
# return " ".join(original_words)
|