Hugging Face Transformers

This repository provides an overview of Hugging Face's Transformers library, a powerful tool for natural language processing (NLP) and machine learning tasks.

Table of Contents

• Hugging Face Transformers
  • Table of Contents
  • Introduction
  • Installation
  • Usage
  • Fine-Tuning Techniques
  • Resources
  • Contributing
  • License

Introduction

Hugging Face's Transformers library provides APIs and tools to easily download and train state-of-the-art pretrained models. These models support common tasks in different modalities, such as text, vision, and audio.

Installation

To install the Transformers library, use pip:

pip install transformers

For additional functionalities, such as dataset handling, consider installing the datasets library:

pip install datasets

Usage

Here's a simple example of how to use a pretrained model for text classification:

from transformers import pipeline

# Load a sentiment-analysis pipeline
classifier = pipeline('sentiment-analysis')

# Classify text
result = classifier('I love using Hugging Face Transformers!')
print(result)

This will output the sentiment classification of the input text.

Fine-Tuning Techniques

Fine-tuning pretrained models on specific tasks can lead to significant performance improvements. The Transformers library provides a Trainer class to facilitate this process.

For more advanced fine-tuning techniques, such as Low-Rank Adaptation (LoRA) and Quantized Low-Rank Adaptation (QLoRA), additional configurations and implementations are required. These techniques help in efficient fine-tuning by reducing the number of trainable parameters and memory usage.

Resources

• Transformers Documentation
• Fine-Tuning Guide
• Hugging Face Hub

Contributing

Contributions are welcome! Please refer to the official Transformers repository for guidelines.

License

This project is licensed under the Apache License 2.0. See the LICENSE file for details.