JAX, Flax & Transformers
A model-definition framework for state-of-the-art machine learning models across text, vision, audio, and multimodal tasks.
What is JAX, Flax & Transformers?
Transformers is a Python library and model-definition framework for state-of-the-art machine learning across text, vision, audio, and multimodal domains. It provides a unified API for using, training, and sharing pretrained models, acting as a central standard that ensures compatibility with numerous ML tools and frameworks. The library simplifies access to over a million models, reducing the barrier to entry for advanced AI applications.
Machine learning researchers, engineers, and developers who need to work with state-of-the-art models for NLP, computer vision, audio, or multimodal tasks, whether for research, prototyping, or production deployment.
Developers choose Transformers for its extensive model support, framework interoperability, and the vast Hugging Face Hub ecosystem. Its unified API and central model definition reduce complexity and enable seamless integration with the broader ML toolchain, from training to inference.
Overview
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
Use Cases
Best For
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