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# 🤗 Transformers
State-of-the-art Machine Learning for PyTorch, TensorFlow and JAX.
State-of-the-art Machine Learning for [PyTorch](https://pytorch.org/), [TensorFlow](https://www.tensorflow.org/), and [JAX](https://jax.readthedocs.io/en/latest/).
🤗 Transformers provides APIs to easily download and train state-of-the-art pretrained models. Using pretrained models can reduce your compute costs, carbon footprint, and save you time from training a model from scratch. The models can be used across different modalities such as:
🤗 Transformers provides APIs and tools to easily download and train state-of-the-art pretrained models. Using pretrained models can reduce your compute costs, carbon footprint, and save you the time and resources required to train a model from scratch. These models support common tasks in different modalities, such as:
* 📝 Text: text classification, information extraction, question answering, summarization, translation, and text generation in over 100 languages.
* 🖼️ Images: image classification, object detection, and segmentation.
* 🗣️ Audio: speech recognition and audio classification.
* 🐙 Multimodal: table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.
📝 **Natural Language Processing**: text classification, named entity recognition, question answering, language modeling, summarization, translation, multiple choice, and text generation.<br>
🖼️ **Computer Vision**: image classification, object detection, and segmentation.<br>
🗣️ **Audio**: automatic speech recognition and audio classification.<br>
🐙 **Multimodal**: table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.
Our library supports seamless integration between three of the most popular deep learning libraries: [PyTorch](https://pytorch.org/), [TensorFlow](https://www.tensorflow.org/) and [JAX](https://jax.readthedocs.io/en/latest/). Train your model in three lines of code in one framework, and load it for inference with another.
🤗 Transformers support framework interoperability between PyTorch, TensorFlow, and JAX. This provides the flexibility to use a different framework at each stage of a model's life; train a model in three lines of code in one framework, and load it for inference in another. Models can also be exported to a format like ONNX and TorchScript for deployment in production environments.
Each 🤗 Transformers architecture is defined in a standalone Python module so they can be easily customized for research and experiments.
Join the growing community on the [Hub](https://huggingface.co/models), [forum](https://discuss.huggingface.co/), or [Discord](https://discord.com/invite/JfAtkvEtRb) today!
## If you are looking for custom support from the Hugging Face team
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## Contents
The documentation is organized in five parts:
The documentation is organized into five sections:
- **GET STARTED** contains a quick tour and installation instructions to get up and running with 🤗 Transformers.
- **TUTORIALS** are a great place to begin if you are new to our library. This section will help you gain the basic skills you need to start using 🤗 Transformers.
- **HOW-TO GUIDES** will show you how to achieve a specific goal like fine-tuning a pretrained model for language modeling or how to create a custom model head.
- **CONCEPTUAL GUIDES** provides more discussion and explanation of the underlying concepts and ideas behind models, tasks, and the design philosophy of 🤗 Transformers.
- **API** describes each class and function, grouped in:
- **GET STARTED** provides a quick tour of the library and installation instructions to get up and running.
- **TUTORIALS** are a great place to start if you're a beginner. This section will help you gain the basic skills you need to start using the library.
- **HOW-TO GUIDES** show you how to achieve a specific goal, like finetuning a pretrained model for language modeling or how to write and share a custom model.
- **CONCEPTUAL GUIDES** offers more discussion and explanation of the underlying concepts and ideas behind models, tasks, and the design philosophy of 🤗 Transformers.
- **API** describes all classes and functions:
- **MAIN CLASSES** for the main classes exposing the important APIs of the library.
- **MODELS** for the classes and functions related to each model implemented in the library.
- **INTERNAL HELPERS** for the classes and functions we use internally.
The library currently contains JAX, PyTorch and TensorFlow implementations, pretrained model weights, usage scripts and conversion utilities for the following models.
- **MAIN CLASSES** details the most important classes like configuration, model, tokenizer, and pipeline.
- **MODELS** details the classes and functions related to each model implemented in the library.
- **INTERNAL HELPERS** details utility classes and functions used internally.
### Supported models