[NLP with Transformers] Fine-tuning Pre-trained Transformer Models

by

NLP with Transformers

Transfer Learning and Fine-tuning Concepts:

    • Introduction to transfer learning in NLP
    • Benefits of fine-tuning pre-trained models
    • Understanding the task-specific and general-purpose layers in Transformers

    Loading and Using Pre-trained Models from HuggingFace:

         from transformers import AutoModelForSequenceClassification, AutoTokenizer

   # Load pre-trained model and tokenizer
   model_name = 'bert-base-uncased'
   model = AutoModelForSequenceClassification.from_pretrained(model_name)
   tokenizer = AutoTokenizer.from_pretrained(model_name)

      Customizing Model Architectures and Hyperparameters:

        • Modifying the architecture for specific tasks
        • Fine-tuning hyperparameters such as learning rate and batch size
        • Handling class imbalance and other task-specific considerations
           from transformers import AdamW

   # Customize model architecture
   num_labels = 2  # Example: Binary classification
   model.config.num_labels = num_labels

   # Specify hyperparameters and optimizer
   optimizer = AdamW(model.parameters(), lr=2e-5)

        Fine-tuning Example: Sentiment Analysis

          • Dataset preparation: loading and preprocessing
          • Fine-tuning the pre-trained model on the sentiment analysis task
          • Evaluating the fine-tuned model on a validation set
             import torch
   from torch.utils.data import DataLoader

   # Example: Sentiment analysis dataset
   train_dataset = load_dataset('imdb', split='train')
   train_dataloader = DataLoader(train_dataset, batch_size=32)

   # Fine-tuning loop
   model.train()
   for batch in train_dataloader:
       inputs = tokenizer(batch['text'], truncation=True, padding=True, return_tensors='pt')
       labels = batch['label']
       outputs = model(**inputs, labels=labels)
       loss = outputs.loss
       # Backpropagation and optimization steps

   # Evaluation
   model.eval()
   val_dataset = load_dataset('imdb', split='test')
   val_dataloader = DataLoader(val_dataset, batch_size=32)

   with torch.no_grad():
       for batch in val_dataloader:
           inputs = tokenizer(batch['text'], truncation=True, padding=True, return_tensors='pt')
           labels = batch['label']
           outputs = model(**inputs)
           predicted_labels = torch.argmax(outputs.logits, dim=1)
           # Compute evaluation metrics

          Don’t forget to modify the code to fit your unique NLP task and dataset. A rudimentary example of sentiment analysis utilizing a binary classification problem is shown in the provided code.

          By fine-tuning pre-trained Transformer models, you may take advantage of the knowledge and linguistic understanding that the pre-training process previously collected while customizing the model to your particular purpose and dataset.

          Leave a Comment