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Hugging Face Transformers is an open-source Python library providing state-of-the-art transformer model implementations, pretrained weights, and utilities for NLP and broader ML tasks. It enables model training, fine-tuning, inference, and model hub integration across PyTorch, TensorFlow, and JAX. Widely adopted for research and production use.
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Hugging Face Transformers is an open-source library and model platform for pretrained Transformer models and their use in machine-learning applications.
The approach traces to the Transformer architecture introduced by Google researchers in the 2017 paper “Attention Is All You Need.” From 2018 onward, Hugging Face turned it into an accessible library and community platform for pretrained models in NLP, vision, and other tasks.
A model and tokenizer turn inputs into tensors. A pipeline or explicit model call performs inference or training; configuration, weights, and auto classes connect task, architecture, and data format.
Attention-based Transformers process relationships within an input sequence.
Text or other inputs become representations expected by the model.
Versioned weights, configurations, and tokenizers are shared and loaded.
Transformers speeds experimentation and product development with pretrained models. License, training data, resource use, fine-tuning, and safety behavior still require model-specific review.
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