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from transformers import AutoTokenizer, AutoModelForMaskedLM
from transformers import pipeline
import random
from nltk.corpus import stopwords
import math
# Masking Model
def mask_non_stopword(sentence):
stop_words = set(stopwords.words('english'))
words = sentence.split()
non_stop_words = [word for word in words if word.lower() not in stop_words]
if not non_stop_words:
return sentence
word_to_mask = random.choice(non_stop_words)
masked_sentence = sentence.replace(word_to_mask, '[MASK]', 1)
return masked_sentence
def mask_non_stopword_pseudorandom(sentence):
stop_words = set(stopwords.words('english'))
words = sentence.split()
non_stop_words = [word for word in words if word.lower() not in stop_words]
if not non_stop_words:
return sentence
random.seed(10)
word_to_mask = random.choice(non_stop_words)
masked_sentence = sentence.replace(word_to_mask, '[MASK]', 1)
return masked_sentence
def high_entropy_words(sentence, non_melting_points):
stop_words = set(stopwords.words('english'))
words = sentence.split()
non_melting_words = set()
for _, point in non_melting_points:
non_melting_words.update(point.lower().split())
candidate_words = [word for word in words if word.lower() not in stop_words and word.lower() not in non_melting_words]
if not candidate_words:
return sentence
max_entropy = -float('inf')
max_entropy_word = None
for word in candidate_words:
masked_sentence = sentence.replace(word, '[MASK]', 1)
predictions = fill_mask(masked_sentence)
# Calculate entropy based on top 5 predictions
entropy = -sum(pred['score'] * math.log(pred['score']) for pred in predictions[:5])
if entropy > max_entropy:
max_entropy = entropy
max_entropy_word = word
return sentence.replace(max_entropy_word, '[MASK]', 1)
# Load tokenizer and model for masked language model
tokenizer = AutoTokenizer.from_pretrained("bert-large-cased-whole-word-masking")
model = AutoModelForMaskedLM.from_pretrained("bert-large-cased-whole-word-masking")
fill_mask = pipeline("fill-mask", model=model, tokenizer=tokenizer)
def mask(sentence):
predictions = fill_mask(sentence)
masked_sentences = [predictions[i]['sequence'] for i in range(len(predictions))]
return masked_sentences
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