https://github.com/xcollab/huggingface
This repository provides an overview of Hugging Face's Transformers library, a powerful tool for natural language processing (NLP) and machine learning tasks.
https://github.com/xcollab/huggingface
bert bert-model gpt gpt-models huggingface huggingface-transformers llm llms models pretrained-language-model pretrained-models python transformer transformers-models
Last synced: 9 months ago
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This repository provides an overview of Hugging Face's Transformers library, a powerful tool for natural language processing (NLP) and machine learning tasks.
- Host: GitHub
- URL: https://github.com/xcollab/huggingface
- Owner: XCollab
- License: mit
- Created: 2025-01-06T08:04:29.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-23T06:17:26.000Z (12 months ago)
- Last Synced: 2025-03-24T04:12:02.997Z (10 months ago)
- Topics: bert, bert-model, gpt, gpt-models, huggingface, huggingface-transformers, llm, llms, models, pretrained-language-model, pretrained-models, python, transformer, transformers-models
- Language: Jupyter Notebook
- Homepage:
- Size: 1.57 MB
- Stars: 3
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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](#hugging-face-transformers)
- [Table of Contents](#table-of-contents)
- [Introduction](#introduction)
- [Installation](#installation)
- [Usage](#usage)
- [Fine-Tuning Techniques](#fine-tuning-techniques)
- [Resources](#resources)
- [Contributing](#contributing)
- [License](#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:
```bash
pip install transformers
```
For additional functionalities, such as dataset handling, consider installing the `datasets` library:
```bash
pip install datasets
```
## Usage
Here's a simple example of how to use a pretrained model for text classification:
```python
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](https://huggingface.co/docs/transformers/en/index)
- [Fine-Tuning Guide](https://huggingface.co/docs/transformers/en/training)
- [Hugging Face Hub](https://huggingface.co/docs/hub/en/transformers)
## Contributing
Contributions are welcome! Please refer to the [official Transformers repository](https://github.com/huggingface/transformers) for guidelines.
## License
This project is licensed under the Apache License 2.0. See the [LICENSE](https://github.com/huggingface/transformers/blob/main/LICENSE) file for details.