LLM Classification Finetuning

Welcome to the LLM Classification Finetuning repository! This project focuses on fine-tuning large language models (LLMs) for text classification tasks.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Models
• Results
• Contributing
• License
• Contact

Introduction

Large Language Models (LLMs) like GPT-3 and BERT have revolutionized natural language processing (NLP). This project demonstrates how to fine-tune these models for specific text classification tasks, improving their performance on domain-specific data.

Features

• Data preprocessing and augmentation
• Fine-tuning of various LLMs
• Model evaluation and comparison
• Visualization of classification results

Installation

To get started, clone this repository and install the required dependencies:

git clone https://github.com/yourusername/llm-classification-finetuning.git
cd llm-classification-finetuning
pip install -r requirements.txt

Usage

1. Data Preparation: Prepare your dataset for training.

   python prepare_data.py

2. Fine-tuning Models: Fine-tune the LLMs on your dataset.

   python finetune_model.py --model_name bert-base-uncased

3. Evaluating Models: Evaluate the fine-tuned models.

   python evaluate_model.py --model_name bert-base-uncased

Models

The following models are fine-tuned in this project:

• BERT (Bidirectional Encoder Representations from Transformers)
• GPT-3 (Generative Pre-trained Transformer 3)
• RoBERTa (Robustly optimized BERT approach)
• T5 (Text-to-Text Transfer Transformer)

Results

The performance of each model is evaluated using metrics such as Accuracy, Precision, Recall, and F1 Score. Visualizations of the classification results are provided to compare the models.

Contributing

Contributions are welcome! Please fork this repository and submit a pull request for any improvements or bug fixes.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Contact

If you need any more help, just let me know. 😊