README.md

HTML Bad coding practices

Overview

This document provides a brief explanation of the parameters, training process, and testing phases of a code framework utilizing a T5 model for text correction and generation.

Parameters

• model: T5 model instance for training.
• tokenizer: Tokenizer associated with the T5 model.
• train_dataset: Training dataset (instance of HtmlDataset).
• epochs: Number of training epochs (default is 3).
• learning_rate: 0.001.

Training

Training is performed using a custom train function that:

• Loads batches of data.
• Tokenizes inputs and labels.
• Computes gradients and updates model parameters.

The function prints the average loss for each epoch and saves the fine-tuned model at the end of training.

Components Used

1. Custom Components:

   • HTMLAwareTokenizer: Custom HTML-aware tokenizer.
   • HtmlDataset: Custom dataset class for handling input and label data.
   • preprocess_html: Custom function for preprocessing HTML text.
   • tokenize_html_aware: Custom function for HTML-aware tokenization.

2. Predefined Components:

   • T5ForConditionalGeneration and T5Tokenizer: Classes from the Hugging Face Transformers library for T5 model and tokenizer.
   • torch.device: PyTorch class for specifying the device (CPU or GPU).
   • torch.optim.Adam: PyTorch class for the Adam optimizer.
   • torch.utils.data.DataLoader: PyTorch class for efficient data loading.

3. Training and Optimization:

   • Utilizes PyTorch's training loop for gradient computation, backpropagation, and model parameter updates.

Testing

For testing, a custom function generate_correction is used. This function generates a corrected version of the input text using the fine-tuned T5 model and tokenizer parameters.

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Note: Ensure all required dependencies and datasets are available before running the training or testing scripts.