Hugging Face Transformers

Hugging Face Transformers is a machine learning framework for training and inference with text, vision, audio, and multimodal models.

Transformers is a model-definition framework designed for machine learning researchers, engineers, and developers. It centralizes model definitions to ensure compatibility across training frameworks, inference engines, and adjacent modeling libraries. The library supports text, computer vision, audio, video, and multimodal inputs and outputs. It features a pipeline class that handles input preprocessing and output generation for various tasks. Users can train models in a few lines of code and move models between PyTorch, JAX, and TensorFlow frameworks.

Hugging Face Transformers at a glance

Best for: Software engineer, Researcher
Runs on: Python, CLI, open source
Pricing: Free
Released: October 2018
Category: AI coding tool
Website: github.com/huggingface/transformers

Key features

Unified API for all pretrained models
Pipeline class for high-level inference
Support for text, vision, audio, video, and multimodal tasks
Compatibility with multiple training frameworks and inference engines
Access to over 1M+ pretrained model checkpoints
Model definition customization and training

Use it for

Automatic speech recognition
Image classification
Text generation and chat
Visual question answering
Document understanding

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