HuggingFace Transformers
A model-definition framework for state-of-the-art machine learning models across text, vision, audio, and multimodal tasks.
What is HuggingFace Transformers?
Transformers is a Python library that provides a unified framework for working with state-of-the-art machine learning models across text, vision, audio, and multimodal domains. It solves the problem of model definition fragmentation by offering a central, compatible definition that works with numerous training and inference frameworks, making advanced AI models accessible and easy to use.
Machine learning researchers, engineers, and developers who need to train, fine-tune, or deploy pretrained models for NLP, computer vision, audio, or multimodal tasks. It's also valuable for students and educators in AI.
Developers choose Transformers for its vast repository of pretrained models, unified API across modalities, and framework interoperability, which significantly reduces development time and computational costs compared to training models from scratch.
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
- Quick prototyping with pretrained models using the high-level Pipeline API
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