Introduction

Defining a custom BERT model class involves creating a new neural network architecture that uses the pre-trained BERT model as a base. This allows for the addition of task-specific layers that can fine-tune the model for the specific NLP task at hand.

The custom BERT model class can be defined using a deep learning framework such as PyTorch, and the process involves specifying the input and output dimensions, the BERT model architecture, and the task-specific layers. This class can then be used for fine-tuning the pre-trained BERT model on the task-specific dataset.

Defining Custom BERT Model

Here's a step-by-step guide with code examples for defining a custom BERT model class using PyTorch.

Step 1: Import the necessary libraries

import torch
from transformers import BertModel
from torch import nn

You need to import the PyTorch and Transformers libraries to define your custom BERT model.

Step 2: Define the custom BERT model architecture

class CustomBERTModel(nn.Module):
    def __init__(self, num_classes):
        super(CustomBERTModel, self).__init__()
        self.bert = BertModel.from_pretrained('bert-base-uncased')
        self.dropout = nn.Dropout(0.3)
        self.linear = nn.Linear(768, num_classes)

You need to define the architecture of your custom BERT model by subclassing the nn.Module class and customizing it according to your needs. In this example, we define a CustomBERTModel class that takes in the num_classes argument which specifies the number of output classes for the linear layer.

The __init__ method initializes the BERT model from the pre-trained BERT model in the Transformers library, adds a dropout layer with a dropout rate of 0.3, and a linear layer for output.

Step 3: Define the forward pass

    def forward(self, input_ids, attention_mask):
        output = self.bert(input_ids=input_ids, attention_mask=attention_mask)
        output = output[1] # take the pooled output
        output = self.dropout(output)
        output = self.linear(output)
        return output

In the forward pass of your model, you need to define how the input text will be processed and transformed by the BERT model. You can use the BertModel class from the Transformers library for this.

We pass the input_ids and attention_mask to the BERT model, and take the second output which is the pooled output of the last layer of the BERT model. We then apply dropout and the linear layer to the pooled output to get the final output.

Step 4: Use the custom BERT model

model = CustomBERTModel(num_classes=2)
input_ids = torch.tensor([[1, 2, 3, 0, 0], [4, 5, 6, 7, 0]])
attention_mask = torch.tensor([[1, 1, 1, 0, 0], [1, 1, 1, 1, 0]])
output = model(input_ids, attention_mask)

You can create an instance of the CustomBERTModel class and pass in the num_classes argument to specify the number of output classes for the linear layer.

You can then pass the input text as input_ids and attention_mask tensors to the model to get the output predictions.

The overall code of the example is as below.

import torch
from transformers import BertModel
from torch import nn

class CustomBERTModel(nn.Module):
    def __init__(self, num_classes):
        super(CustomBERTModel, self).__init__()
        self.bert = BertModel.from_pretrained('bert-base-uncased')
        self.dropout = nn.Dropout(0.3)
        self.linear = nn.Linear(768, num_classes)

    def forward(self, input_ids, attention_mask):
        output = self.bert(input_ids=input_ids, attention_mask=attention_mask)
        output = output[1] # take the pooled output
        output = self.dropout(output)
        output = self.linear(output)
        return output

model = CustomBERTModel(num_classes=2)
input_ids = torch.tensor([[1, 2, 3, 0, 0], [4, 5, 6, 7, 0]])
attention_mask = torch.tensor([[1, 1, 1, 0, 0], [1, 1, 1, 1, 0]])
output = model(input_ids, attention_mask)

Custom BERT Model Examples

Here are some examples of custom BERT model classes with explanations.

Fine-tuning BERT for sentiment analysis

class SentimentClassifier(nn.Module):
    def __init__(self, num_classes):
        super(SentimentClassifier, self).__init__()
        self.bert = BertModel.from_pretrained('bert-base-uncased')
        self.dropout = nn.Dropout(0.1)
        self.linear = nn.Linear(768, num_classes)

    def forward(self, input_ids, attention_mask):
        output = self.bert(input_ids=input_ids, attention_mask=attention_mask)
        output = output[1] # take the pooled output
        output = self.dropout(output)
        output = self.linear(output)
        return output

In this example, we define a custom BERT model class SentimentClassifier for sentiment analysis.

The architecture of this model is similar to the example in the previous answer. We initialize the BERT model from the pre-trained BERT model in the Transformers library, add a dropout layer with a dropout rate of 0.1, and a linear layer for output.

In the forward pass, we pass the input_ids and attention_mask to the BERT model and take the second output which is the pooled output of the last layer of the BERT model. We then apply dropout and the linear layer to the pooled output to get the final output.

Fine-tuning BERT for named entity recognition

class NERClassifier(nn.Module):
    def __init__(self, num_classes):
        super(NERClassifier, self).__init__()
        self.bert = BertModel.from_pretrained('bert-base-uncased')
        self.dropout = nn.Dropout(0.1)
        self.classifier = nn.Linear(768, num_classes)

    def forward(self, input_ids, attention_mask):
        output = self.bert(input_ids=input_ids, attention_mask=attention_mask)
        output = output.last_hidden_state
        output = self.dropout(output)
        output = self.classifier(output)
        return output

In this example, we define a custom BERT model class NERClassifier for named entity recognition (NER).

The architecture of this model is similar to the previous example, with the addition of a linear layer for classification.

In the forward pass, we pass the input_ids and attention_mask to the BERT model and take the last hidden state of the last layer of the BERT model. We then apply dropout and the linear layer to the hidden state to get the final output.

Fine-tuning BERT for question answering

class QAClassifier(nn.Module):
    def __init__(self, num_classes):
        super(QAClassifier, self).__init__()
        self.bert = BertModel.from_pretrained('bert-base-uncased')
        self.qa_outputs = nn.Linear(768, num_classes)

    def forward(self, input_ids, attention_mask):
        outputs = self.bert(input_ids, attention_mask=attention_mask)
        sequence_output = outputs.last_hidden_state
        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = logits.split(1, dim=-1)
        start_logits = start_logits.squeeze(-1)
        end_logits = end_logits.squeeze(-1)
        return start_logits, end_logits

In this example, we define a custom BERT model class QAClassifier for question answering.

The architecture of this model is similar to the previous examples, with the addition of a linear layer for question answering.

In the forward pass, we pass the input_ids and attention_mask to the BERT model and take the last hidden state of the last layer of the BERT model. We then apply a linear layer to the hidden state to get the logits. We split the logits into two parts for start and end positions of the answer using the split method. We then squeeze the last dimension of the logits and return the start and end logits separately.

Saving and Sharing Your Custom BERT Model

Saving and sharing a custom BERT PyTorch model involves a few simple steps.

Save the model

After training the custom BERT model, it can be saved to a file using the torch.save() method. This method saves the model's state dictionary, which includes the architecture and weights.

torch.save(model.state_dict(), 'my_bert_model.pth')

This will save the model's state dictionary to a file named my_bert_model.pth.

Zip the saved model file

To share the saved model with others, the saved model file can be zipped into a single file.

$ zip my_bert_model.zip my_bert_model.pth

This will create a ZIP file named my_bert_model.zip that contains the saved model file.

Share the ZIP file

The ZIP file can be shared with others via email, cloud storage services, or other means.

Load the saved model

To load the saved model in another script or application, the model's architecture must first be defined, and then the saved state dictionary can be loaded using the load_state_dict() method.

from transformers import BertModel

model = BertModel.from_pretrained('bert-base-uncased')
model.load_state_dict(torch.load('my_bert_model.pth'))

This will load the saved state dictionary into the pre-trained BERT model, creating a new PyTorch model object that can be used to make predictions on new data.