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Using 🤗 transformers at Hugging Face

🤗 `transformers` is a library maintained by Hugging Face and the community, for state-of-the-art Machine Learning for Pytorch, TensorFlow and JAX. It provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. We are a bit biased, but we really like 🤗 `transformers`!

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Exploring 🤗 transformers in the Hub

There are over 630,000 `transformers` models in the Hub which you can find by filtering at the left of the models page.

You can find models for many different tasks:

- Extracting the answer from a context (question-answering).
- Creating summaries from a large text (summarization).
- Classify text (e.g. as spam or not spam, text-classification).
- Generate a new text with models such as GPT (text-generation).
- Identify parts of speech (verb, subject, etc.) or entities (country, organization, etc.) in a sentence (token-classification).
- Transcribe audio files to text (automatic-speech-recognition).
- Classify the speaker or language in an audio file (audio-classification).
- Detect objects in an image (object-detection).
- Segment an image (image-segmentation).
- Do Reinforcement Learning (reinforcement-learning)!

You can try out the models directly in the browser if you want to test them out without downloading them thanks to the in-browser widgets!

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Using existing models

All `transformer` models are a line away from being used! Depending on how you want to use them, you can use the high-level API using the `pipeline` function or you can use `AutoModel` for more control.

    # With pipeline, just specify the task and the model id from the Hub.
    from transformers import pipeline
    pipe = pipeline("text-generation", model="distilbert/distilgpt2")

    # If you want more control, you will need to define the tokenizer and model.
    from transformers import AutoTokenizer, AutoModelForCausalLM
    tokenizer = AutoTokenizer.from_pretrained("distilbert/distilgpt2")
    model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")

You can also load a model from a specific version (based on commit hash, tag name, or branch) as follows:

    model = AutoModel.from_pretrained(
        "julien-c/EsperBERTo-small", revision="v2.0.1"  # tag name, or branch name, or commit hash
    )

If you want to see how to load a specific model, you can click `Use in Transformers` and you will be given a working snippet that you can load it! If you need further information about the model architecture, you can also click the “Read model documentation” at the bottom of the snippet.

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Sharing your models

To read all about sharing models with `transformers`, please head out to the Share a model guide in the official documentation.

Many classes in `transformers`, such as the models and tokenizers, have a `push_to_hub` method that allows to easily upload the files to a repository.

    # Pushing model to your own account
    model.push_to_hub("my-awesome-model")

    # Pushing your tokenizer
    tokenizer.push_to_hub("my-awesome-model")

    # Pushing all things after training
    trainer.push_to_hub()

There is much more you can do, so we suggest to review the Share a model guide.

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Additional resources

- Transformers library.
- Transformers docs.
- Share a model guide.

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