5. Advanced NLP Models

Advanced NLP Models

Transformer architecture:
The Transformer architecture, introduced by Vaswani et al. in the paper "Attention is All You Need," is a neural network architecture based entirely on self-attention mechanisms without recurrent or convolutional layers. It has been highly successful in NLP tasks due to its parallelizable nature, scalability, and ability to capture long-range dependencies.

Applications of Transformers:
Transformers have been applied to various NLP tasks, including:

• BERT (Bidirectional Encoder Representations from Transformers): A pre-trained transformer model introduced by Devlin et al. for natural language understanding tasks such as question answering, text classification, and named entity recognition.
• GPT (Generative Pre-trained Transformer): A series of autoregressive language models introduced by OpenAI, including GPT-1, GPT-2, and GPT-3, capable of generating coherent and contextually relevant text.
• T5 (Text-To-Text Transfer Transformer): A transformer model introduced by Google Research that treats all NLP tasks as text-to-text tasks, achieving state-of-the-art performance on a wide range of tasks with a unified architecture.

Lab Activity: Fine-tuning pre-trained Transformer models for text classification or named entity recognition tasks
In this lab activity, we will fine-tune a pre-trained Transformer model such as BERT or GPT for text classification or named entity recognition tasks. The steps involved include:

1. Load pre-trained model: Load the pre-trained Transformer model (e.g., BERT, GPT).
2. Prepare data: Preprocess the input text data and tokenize it for input to the model.
3. Define task-specific layers: Add task-specific layers (e.g., classification layer, CRF layer) on top of the pre-trained model.
4. Train and evaluate: Fine-tune the model on task-specific data and evaluate its performance on a validation set.

Code for Lab Activity:
Step 1: Load pre-trained model

        # Load pre-trained Transformer model (e.g., BERT, GPT)
        from transformers import BertTokenizer, BertForSequenceClassification

        tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
        model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
    

Step 2: Prepare data

        # Prepare input data
        text = ["Sample input text 1", "Sample input text 2"]
        encoded_input = tokenizer(text, padding=True, truncation=True, return_tensors='pt')

        # Prepare labels
        labels = torch.tensor([1, 0])  # Example labels
    

Step 3: Define task-specific layers

        # Add task-specific layers (e.g., classification layer)
        import torch.nn as nn

        class TextClassifier(nn.Module):
            def __init__(self, base_model):
                super(TextClassifier, self).__init__()
                self.base_model = base_model
                self.classifier = nn.Linear(base_model.config.hidden_size, num_classes)

            def forward(self, input_ids, attention_mask):
                outputs = self.base_model(input_ids=input_ids, attention_mask=attention_mask)
                logits = self.classifier(outputs[1])
                return logits

        model = TextClassifier(model.base_model)
    

Step 4: Train and evaluate

        # Fine-tune the model on task-specific data
        optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
        criterion = nn.CrossEntropyLoss()

        for epoch in range(num_epochs):
            optimizer.zero_grad()
            outputs = model(**encoded_input, labels=labels)
            loss = outputs.loss
            loss.backward()
            optimizer.step()

            # Evaluation
            # ...