What is BERT model in transformers?

This recipe explains what is BERT model in transformers.

Recipe Objective: What is BERT model in transformers?

BERT stands for Bidirectional Encoder Representation from Transformers. As the name suggests, it is a bidirectional transformer. It is pre-trained on a large corpus using a combination of masked language modeling objective and next sentence prediction. This corpus includes the Toronto Book Corpus and Wikipedia.

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There are two steps in BERT training -
1) Pre-train BERT to understand the language
2) Fine-tune BERT to learn a specific task

BERT can pre-train bidirectional representations by training all the layers on both the left as well as right context simultaneously. You can next fine-tune this pre-trained BERT model with just one additional output layer to provide state-of-the-art models for a variety of tasks, including question answering and language inference, without requiring significant task-specific architecture changes.

PreTrainedModel, TFPreTrainedModel, and FlaxPreTrainedModel are basic classes of BERT. These are responsible for implementing the standard methods for loading/saving a model from a local file or directory, or from a library-provided pretrained model configuration. TFPreTrainedModel and PreTrainedModel also implement a few methods that are shared by all models, such as resizing the input token embeddings when additional tokens are introduced to the vocabulary and pruning the attention heads of the model.

To employ a pre-trained BERT model, we must first transform the input data into an acceptable format, so that each sentence can be given to the model and the relevant output can be obtained. We need to tokenize the input data and convert the tokens into their IDs. This can be done using BertTokenizer.

For more related projects -

https://www.projectpro.io/projects/data-science-projects/neural-network-projects

https://www.projectpro.io/projects/data-science-projects/tensorflow-projects

Example -

#practical implementation of BertModel and BertTokenizer

#importing required libraries
import torch
from transformers import BertModel, BertTokenizer

# Load the tokenizer and model of the "bert-base-cased" pretrained model
tz = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')

#Tokenizing the input data and assigning the token their IDs
input_values = tz("The quick brown fox jumps over the lazy dog fox", return_tensors="pt")
print("input_values: ",input_values)
output_values = model(**input_values)

#last_hidden_state contains the sequence of hidden-states at the output of the last layer of the model.
last_hidden_states = output_values.last_hidden_state

#displaying the hidden-states
print("last hidden states: ",last_hidden_states)

Output -
input_values:  {'input_ids': tensor([[  101,  1996,  4248,  2829,  4419, 14523,  2058,  1996, 13971,  3899,
          4419,   102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}
last hidden states:  tensor([[[-0.4169,  0.2237, -0.0149,  ..., -0.3577,  0.4613,  0.6207],
         [-0.7176, -0.3290, -0.3350,  ..., -0.2202,  1.0999, -0.2368],
         [-0.3411, -0.5184,  0.6255,  ..., -0.2406,  0.6005, -0.0851],
         ...,
         [ 0.4100, -0.3099,  0.7197,  ..., -0.3412,  0.5724,  0.4540],
         [-0.4391, -0.2988, -0.1356,  ...,  0.4577,  0.6688, -0.0256],
         [ 0.7355,  0.0072, -0.5661,  ..., -0.0401, -0.4683, -0.2086]]],
       grad_fn=)

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