SAWiT Model Training

Overview

This project is a machine learning model built for the SAWiT task. The model is trained on tokenized text data and is designed to solve [state the problem the model is solving].

Dataset

The dataset used for training is a tokenized dataset, which has been preprocessed and saved in the tokenized_data directory. It contains the following files:

• train.arrow
• validation.arrow
• test.arrow

These files are in Arrow format and were saved using the Hugging Face datasets library.

Model Details

The model architecture is based on [mention model type, e.g., BERT, GPT, LSTM, etc.], and was trained using the [framework, e.g., TensorFlow or PyTorch]. The model performs [state task, e.g., classification, prediction, etc.].

Training

To train the model, follow these steps:

1. Ensure that the dataset has been preprocessed and saved to the tokenized_data folder.
2. Run the training script:

   python train_model.py