Transformers
Transformers is a model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training. It centralizes the model definition so that this definition is agreed upon across the ecosystem. The library provides a unified API for using all pretrained models and supports over 1M+ model checkpoints on the Hugging Face Hub. Transformers works with Python 3.11+ and PyTorch 2.6+, and it is optimized to work with PyTorch models provided by the library. The training API is designed to work with PyTorch models, and the library provides examples for each architecture to reproduce the results published by its original authors. Transformers is more than a toolkit to use pretrained models; it's a community of projects built around it and the Hugging Face Hub. The library enables developers, researchers, students, professors, engineers, and anyone else to build their dream projects. It provides an easy-to-use interface for state-of-the-art models, with high performance on natural language understanding and generation, computer vision, audio, video, and multimodal tasks. The library also provides a low barrier to entry for researchers, engineers, and developers, with few user-facing abstractions and a unified API for using all pretrained models. Additionally, Transformers allows for easy customization of models or examples to specific needs, with model internals exposed as consistently as possible and model files that can be used independently of the library for quick experiments.
- Category
- Voice & Audio
- Pricing
- Open Source
Problem solved
Transformers solves the problem of providing a unified and easy-to-use interface for state-of-the-art machine learning models across various modalities, including text, vision, audio, and multimodal models. It addresses the issue of fragmentation in the machine learning ecosystem, where different models and frameworks require different APIs and interfaces. By providing a centralized model definition and a unified API, Transformers enables developers, researchers, and engineers to easily use and integrate state-of-the-art models into their projects, without requiring extensive expertise in each individual model or framework. This makes it easier to build and deploy machine learning models, and to reproduce results published by other researchers.
Use cases
- Text generation
- Text classification
- Named entity recognition
- Question answering
- Summarization
- Translation
- Image classification
- Object detection
Audience
- Developers
- ML Engineers
- Data Scientists
- Researchers
Pros
- Easy-to-use interface
- Unified API for all pretrained models
- High performance on various tasks
- Low barrier to entry
- Customizable models and examples
Cons
- Limited support for other frameworks
- Training API optimized for PyTorch models
- May not be suitable for generic machine learning loops
Official website: https://github.com/huggingface/transformers
Canonical LambdaBase page: https://www.lambdabase.com/tools/transformers