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import pandas as pd
import numpy as np
import pickle
import torch
from torch.utils.data import Dataset, DataLoader
from transformers import BertTokenizer, BertModel
from transformers import AutoTokenizer, AutoModel
import nltk
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased', output_hidden_states = True,)
def extract_context_words(x, window = 6):
paragraph, offset_start, offset_end = x['paragraph'], x['offset_start'], x['offset_end']
target_word = paragraph[offset_start : offset_end]
paragraph = ' ' + paragraph + ' '
offset_start = offset_start + 1
offset_end = offset_end + 1
prev_space_posn = (paragraph[:offset_start].rindex(' ') + 1)
end_space_posn = (offset_end + paragraph[offset_end:].index(' '))
full_word = paragraph[prev_space_posn : end_space_posn]
prev_words = nltk.word_tokenize(paragraph[0:prev_space_posn])
next_words = nltk.word_tokenize(paragraph[end_space_posn:])
words_in_context_window = prev_words[-1*window:] + [full_word] + next_words[:window]
context_text = ' '.join(words_in_context_window)
return context_text
"""The following functions have been created with inspiration from https://github.com/arushiprakash/MachineLearning/blob/main/BERT%20Word%20Embeddings.ipynb"""
def bert_text_preparation(text, tokenizer):
"""Preparing the input for BERT
Takes a string argument and performs
pre-processing like adding special tokens,
tokenization, tokens to ids, and tokens to
segment ids. All tokens are mapped to seg-
ment id = 1.
Args:
text (str): Text to be converted
tokenizer (obj): Tokenizer object
to convert text into BERT-re-
adable tokens and ids
Returns:
list: List of BERT-readable tokens
obj: Torch tensor with token ids
obj: Torch tensor segment ids
"""
marked_text = "[CLS] " + text + " [SEP]"
tokenized_text = tokenizer.tokenize(marked_text)
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
segments_ids = [1]*len(indexed_tokens)
# Convert inputs to PyTorch tensors
tokens_tensor = torch.tensor([indexed_tokens])
segments_tensors = torch.tensor([segments_ids])
return tokenized_text, tokens_tensor, segments_tensors
def get_bert_embeddings(tokens_tensor, segments_tensors, model):
"""Get embeddings from an embedding model
Args:
tokens_tensor (obj): Torch tensor size [n_tokens]
with token ids for each token in text
segments_tensors (obj): Torch tensor size [n_tokens]
with segment ids for each token in text
model (obj): Embedding model to generate embeddings
from token and segment ids
Returns:
list: List of list of floats of size
[n_tokens, n_embedding_dimensions]
containing embeddings for each token
"""
# Gradient calculation id disabled
# Model is in inference mode
with torch.no_grad():
outputs = model(tokens_tensor, segments_tensors)
# Removing the first hidden state
# The first state is the input state
hidden_states = outputs[2][1:]
# Getting embeddings from the final BERT layer
token_embeddings = hidden_states[-1]
# Collapsing the tensor into 1-dimension
token_embeddings = torch.squeeze(token_embeddings, dim=0)
# Converting torchtensors to lists
list_token_embeddings = [token_embed.tolist() for token_embed in token_embeddings]
return list_token_embeddings
def bert_embedding_extract(context_text, word):
tokenized_text, tokens_tensor, segments_tensors = bert_text_preparation(context_text, tokenizer)
list_token_embeddings = get_bert_embeddings(tokens_tensor, segments_tensors, model)
word_tokens,tt,st = bert_text_preparation(word, tokenizer)
word_embedding_all = []
for word_tk in word_tokens:
word_index = tokenized_text.index(word_tk)
word_embedding = list_token_embeddings[word_index]
word_embedding_all.append(word_embedding)
word_embedding_mean = np.array(word_embedding_all).mean(axis=0)
return word_embedding_mean
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