Sentence Classification with Transformers

This project fine-tunes and evaluates a sentence classification model using Hugging Face's Transformers library. It supports multiple classification tasks with stratified k-fold cross-validation and fine-tuning of pre-trained models.

Features

• Fine-tuning Hugging Face models for sentence classification.
• Stratified k-fold cross-validation for robust evaluation.
• Metrics computation: Precision, Recall, F1-Score, and Accuracy.
• Support for multiple classification tasks.

Requirements

Install the required Python packages using:

pip install -r requirements.txt

Usage

Run the script with the following arguments:

python train_test_model.py <dataset_name> <output_dir_name> <model_name> <num_train_epochs>

• dataset_name: Path to the dataset file (tab-separated).
• output_dir_name: Directory to save the model and tokenizer.
• model_name: Hugging Face model name (e.g., bert-base-uncased).
• num_train_epochs: Number of training epochs.

Example

python train_test_model.py data/dataset.tsv output bert-base-uncased 3

Dataset Format

The dataset should be a tab-separated file with the following structure:

sentence<TAB>fully_curatable<TAB>partially_curatable<TAB>language_related

Output

• Fine-tuned models and tokenizers are saved in the specified output directory.
• Evaluation metrics are printed for each classification task.

Pre-trained Models

Pretrained models can be found on Hugging Face.