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Upload fincat_utils.py

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  1. fincat_utils.py +108 -0
fincat_utils.py ADDED
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+ import pandas as pd
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+ import numpy as np
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+ import pickle
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+ import torch
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+ from torch.utils.data import Dataset, DataLoader
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+ from transformers import BertTokenizer, BertModel
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+ from transformers import AutoTokenizer, AutoModel
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+ import nltk
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+
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+ tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
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+ model = BertModel.from_pretrained('bert-base-uncased', output_hidden_states = True,)
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+
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+ def extract_context_words(x, window = 6):
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+ paragraph, offset_start, offset_end = x['paragraph'], x['offset_start'], x['offset_end']
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+ target_word = paragraph[offset_start : offset_end]
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+ paragraph = ' ' + paragraph + ' '
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+ offset_start = offset_start + 1
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+ offset_end = offset_end + 1
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+ prev_space_posn = (paragraph[:offset_start].rindex(' ') + 1)
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+ end_space_posn = (offset_end + paragraph[offset_end:].index(' '))
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+ full_word = paragraph[prev_space_posn : end_space_posn]
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+
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+ prev_words = nltk.word_tokenize(paragraph[0:prev_space_posn])
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+ next_words = nltk.word_tokenize(paragraph[end_space_posn:])
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+ words_in_context_window = prev_words[-1*window:] + [full_word] + next_words[:window]
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+ context_text = ' '.join(words_in_context_window)
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+ return context_text
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+
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+ """The following functions have been created with inspiration from https://github.com/arushiprakash/MachineLearning/blob/main/BERT%20Word%20Embeddings.ipynb"""
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+
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+ def bert_text_preparation(text, tokenizer):
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+ """Preparing the input for BERT
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+
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+ Takes a string argument and performs
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+ pre-processing like adding special tokens,
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+ tokenization, tokens to ids, and tokens to
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+ segment ids. All tokens are mapped to seg-
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+ ment id = 1.
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+
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+ Args:
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+ text (str): Text to be converted
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+ tokenizer (obj): Tokenizer object
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+ to convert text into BERT-re-
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+ adable tokens and ids
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+
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+ Returns:
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+ list: List of BERT-readable tokens
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+ obj: Torch tensor with token ids
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+ obj: Torch tensor segment ids
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+
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+ """
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+ marked_text = "[CLS] " + text + " [SEP]"
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+ tokenized_text = tokenizer.tokenize(marked_text)
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+ indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
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+ segments_ids = [1]*len(indexed_tokens)
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+
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+ # Convert inputs to PyTorch tensors
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+ tokens_tensor = torch.tensor([indexed_tokens])
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+ segments_tensors = torch.tensor([segments_ids])
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+
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+ return tokenized_text, tokens_tensor, segments_tensors
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+
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+ def get_bert_embeddings(tokens_tensor, segments_tensors, model):
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+ """Get embeddings from an embedding model
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+
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+ Args:
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+ tokens_tensor (obj): Torch tensor size [n_tokens]
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+ with token ids for each token in text
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+ segments_tensors (obj): Torch tensor size [n_tokens]
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+ with segment ids for each token in text
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+ model (obj): Embedding model to generate embeddings
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+ from token and segment ids
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+
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+ Returns:
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+ list: List of list of floats of size
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+ [n_tokens, n_embedding_dimensions]
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+ containing embeddings for each token
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+ """
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+
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+ # Gradient calculation id disabled
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+ # Model is in inference mode
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+ with torch.no_grad():
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+ outputs = model(tokens_tensor, segments_tensors)
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+ # Removing the first hidden state
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+ # The first state is the input state
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+ hidden_states = outputs[2][1:]
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+
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+ # Getting embeddings from the final BERT layer
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+ token_embeddings = hidden_states[-1]
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+ # Collapsing the tensor into 1-dimension
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+ token_embeddings = torch.squeeze(token_embeddings, dim=0)
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+ # Converting torchtensors to lists
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+ list_token_embeddings = [token_embed.tolist() for token_embed in token_embeddings]
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+
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+ return list_token_embeddings
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+
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+ def bert_embedding_extract(context_text, word):
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+ tokenized_text, tokens_tensor, segments_tensors = bert_text_preparation(context_text, tokenizer)
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+ list_token_embeddings = get_bert_embeddings(tokens_tensor, segments_tensors, model)
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+ word_tokens,tt,st = bert_text_preparation(word, tokenizer)
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+ word_embedding_all = []
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+ for word_tk in word_tokens:
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+ word_index = tokenized_text.index(word_tk)
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+ word_embedding = list_token_embeddings[word_index]
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+ word_embedding_all.append(word_embedding)
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+ word_embedding_mean = np.array(word_embedding_all).mean(axis=0)
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+ return word_embedding_mean
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+