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2022 is the year of multi-modality (#14610)

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* 2022 is the year of multi-modality

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Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>

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* Update README.md

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Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
Co-authored-by: lewtun <lewis.c.tunstall@gmail.com>
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@@ -12,12 +12,21 @@ specific language governing permissions and limitations under the License.
# 🤗 Transformers
State-of-the-art Natural Language Processing for Jax, Pytorch and TensorFlow
State-of-the-art Machine Learning for Jax, Pytorch and TensorFlow
🤗 Transformers (formerly known as _pytorch-transformers_ and _pytorch-pretrained-bert_) provides general-purpose
architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural Language Understanding (NLU) and Natural
Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between Jax,
PyTorch and TensorFlow.
🤗 Transformers (formerly known as _pytorch-transformers_ and _pytorch-pretrained-bert_) provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.
These models can applied on:
* 📝 Text, for tasks like text classification, information extraction, question answering, summarization, translation, text generation, in over 100 languages.
* 🖼️ Images, for tasks like image classification, object detection, and segmentation.
* 🗣️ Audio, for tasks like speech recognition and audio classification.
Transformer models can also perform tasks on **several modalities combined**, such as table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.
🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community on our [model hub](https://huggingface.co/models). At the same time, each python module defining an architecture is fully standalone and can be modified to enable quick research experiments.
🤗 Transformers is backed by the three most popular deep learning libraries — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) and [TensorFlow](https://www.tensorflow.org/) — with a seamless integration between them. It's straightforward to train your models with one before loading them for inference with the other.
This is the documentation of our repository [transformers](https://github.com/huggingface/transformers). You can
also follow our [online course](https://huggingface.co/course) that teaches how to use this library, as well as the
@@ -31,29 +40,26 @@ other libraries developed by Hugging Face and the Hub.
## Features
- High performance on NLU and NLG tasks
- Low barrier to entry for educators and practitioners
1. Easy-to-use state-of-the-art models:
- High performance on natural language understanding & generation, computer vision, and audio tasks.
- Low barrier to entry for educators and practitioners.
- Few user-facing abstractions with just three classes to learn.
- A unified API for using all our pretrained models.
State-of-the-art NLP for everyone:
1. Lower compute costs, smaller carbon footprint:
- Researchers can share trained models instead of always retraining.
- Practitioners can reduce compute time and production costs.
- Dozens of architectures with over 20,000 pretrained models, some in more than 100 languages.
- Deep learning researchers
- Hands-on practitioners
- AI/ML/NLP teachers and educators
1. Choose the right framework for every part of a model's lifetime:
- Train state-of-the-art models in 3 lines of code.
- Move a single model between TF2.0/PyTorch/JAX frameworks at will.
- Seamlessly pick the right framework for training, evaluation and production.
Lower compute costs, smaller carbon footprint:
- Researchers can share trained models instead of always retraining
- Practitioners can reduce compute time and production costs
- 8 architectures with over 30 pretrained models, some in more than 100 languages
Choose the right framework for every part of a model's lifetime:
- Train state-of-the-art models in 3 lines of code
- Deep interoperability between Jax, Pytorch and TensorFlow models
- Move a single model between Jax/PyTorch/TensorFlow frameworks at will
- Seamlessly pick the right framework for training, evaluation, production
The support for Jax is still experimental (with a few models right now), expect to see it grow in the coming months!
1. Easily customize a model or an example to your needs:
- We provide examples for each architecture to reproduce the results published by its original authors.
- Model internals are exposed as consistently as possible.
- Model files can be used independently of the library for quick experiments.
[All the model checkpoints](https://huggingface.co/models) are seamlessly integrated from the huggingface.co [model
hub](https://huggingface.co) where they are uploaded directly by [users](https://huggingface.co/users) and