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#!/usr/bin/env python
# coding: utf-8
# # Generating BERT embeddings from the scratch
# In[1]:
import pandas as pd
from transformers import BertTokenizer, BertModel
import torch
# Reading the main data as CSV file into a pandas DataFrame.
df = pd.read_csv('/users/deniz.bilgin/Deep Learning/socialmedia-disaster-tweets-DFE.csv', encoding='UTF-8')
#loading the pre-trained BERT model and its tokenizer.
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
# def function to create BERT embeddings for a given text.
def generate_bert_embeddings(text):
# First, tokenizing the tweets in text column.
input_ids = torch.tensor(tokenizer.encode(text, add_special_tokens=True))
# Generating BERT embeddings
with torch.no_grad():
last_hidden_states = model(input_ids.unsqueeze(0))[0] #applying the Bert model on tokenized text.
embeddings = torch.mean(last_hidden_states, dim=1) #calculating the mean of the embeddings for all the tokens in the text to generate a single embedding for the entire text.
return embeddings.numpy() # converting the tensor that contains the embeddings to a numpy array.
#applying the embeddings to every tweet in text data in the dataframe.
df['embeddings'] = df['text'].apply(generate_bert_embeddings)
# Saving the embeddings to a CSV file.
df.to_csv('bert_embeddings_tweets.csv', index=False)
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