Transformers: Model framework for NLP and vision
Unified API for pre-trained transformer models across frameworks.
Learn more about Transformers
Transformers is a Python library that centralizes model definitions for transformer architectures and related deep learning models. It implements model classes compatible with PyTorch and other frameworks, allowing the same model definition to work across different training and inference backends. The library includes pre-trained model weights, tokenizers, and feature extractors for various tasks including natural language processing, computer vision, speech recognition, and multimodal applications. Models defined in Transformers integrate with training frameworks like DeepSpeed, FSDP, and PyTorch-Lightning, as well as inference engines like vLLM.
Framework-Agnostic Models
Model definitions are decoupled from specific frameworks, enabling the same model to be used across PyTorch, TensorFlow, and JAX backends. Training and inference tools build on a shared model specification without reimplementation.
Unified Model Hub
Direct integration with a centralized repository containing thousands of pre-trained checkpoints. Single function call loads models with weights, configurations, and tokenizers, eliminating manual download and setup.
Multi-Modal Architecture Support
Provides unified interfaces for text, vision, audio, and multi-modal models. Process different data types with consistent APIs, enabling straightforward development of applications combining multiple modalities.
from transformers import pipeline
classifier = pipeline("sentiment-analysis")
result = classifier("I love using transformers!")
print(result)See how people are using Transformers
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