Testing BERT LLM and Its Fine-Tuning

This repository contains the code and resources necessary for testing BERT (Bidirectional Encoder Representations from Transformers) as a large language model (LLM) and fine-tuning it on a specific downstream task. We will explore how BERT can be fine-tuned for various natural language processing (NLP) tasks such as text classification, sentiment analysis, and more.

Overview

BERT is a pre-trained transformer model designed for understanding bidirectional context in text. This project demonstrates:

• How to load a pre-trained BERT model
• Fine-tuning the BERT model on custom datasets
• Evaluating its performance on downstream tasks

Prerequisites

Before starting with this project, ensure you have the following:

• A Python environment (Python 3.7+ recommended)
• Access to a GPU for faster model training (optional but recommended)
• The following libraries installed:
  • transformers by Hugging Face
  • torch for PyTorch-based implementation
  • datasets for dataset management
  • scikit-learn for performance evaluation
  • pandas and numpy for data handling
• Basic understanding of BERT and NLP tasks
• Dataset prepared for fine-tuning (e.g., text classification, question answering)

Setup

To set up the environment and install necessary dependencies, run:

pip install transformers torch datasets scikit-learn pandas numpy