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# Philosophy

🤗 Transformers is an opinionated library built for:

- NLP researchers and educators seeking to use/study/extend large-scale transformers models
- hands-on practitioners who want to fine-tune those models and/or serve them in production
- engineers who just want to download a pretrained model and use it to solve a given NLP task.

The library was designed with two strong goals in mind:

- Be as easy and fast to use as possible:

  - We strongly limited the number of user-facing abstractions to learn, in fact, there are almost no abstractions,
    just three standard classes required to use each model: [configuration](main_classes/configuration),
    [models](main_classes/model) and [tokenizer](main_classes/tokenizer).
  - All of these classes can be initialized in a simple and unified way from pretrained instances by using a common
    `from_pretrained()` instantiation method which will take care of downloading (if needed), caching and
    loading the related class instance and associated data (configurations' hyper-parameters, tokenizers' vocabulary,
    and models' weights) from a pretrained checkpoint provided on [Hugging Face Hub](https://huggingface.co/models) or your own saved checkpoint.
  - On top of those three base classes, the library provides two APIs: [`pipeline`] for quickly
    using a model (plus its associated tokenizer and configuration) on a given task and
    [`Trainer`]/`Keras.fit` to quickly train or fine-tune a given model.
  - As a consequence, this library is NOT a modular toolbox of building blocks for neural nets. If you want to
    extend/build-upon the library, just use regular Python/PyTorch/TensorFlow/Keras modules and inherit from the base
    classes of the library to reuse functionalities like model loading/saving.

- Provide state-of-the-art models with performances as close as possible to the original models:

  - We provide at least one example for each architecture which reproduces a result provided by the official authors
    of said architecture.
  - The code is usually as close to the original code base as possible which means some PyTorch code may be not as
    *pytorchic* as it could be as a result of being converted TensorFlow code and vice versa.

A few other goals:

- Expose the models' internals as consistently as possible:

  - We give access, using a single API, to the full hidden-states and attention weights.
  - Tokenizer and base model's API are standardized to easily switch between models.

- Incorporate a subjective selection of promising tools for fine-tuning/investigating these models:

  - A simple/consistent way to add new tokens to the vocabulary and embeddings for fine-tuning.
  - Simple ways to mask and prune transformer heads.

- Switch easily between PyTorch and TensorFlow 2.0, allowing training using one framework and inference using another.

## Main concepts

The library is built around three types of classes for each model:

- **Model classes** such as [`BertModel`], which are 30+ PyTorch models ([torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)) or Keras models ([tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model)) that work with the pretrained weights provided in the
  library.
- **Configuration classes** such as [`BertConfig`], which store all the parameters required to build
  a model. You don't always need to instantiate these yourself. In particular, if you are using a pretrained model
  without any modification, creating the model will automatically take care of instantiating the configuration (which
  is part of the model).
- **Tokenizer classes** such as [`BertTokenizer`], which store the vocabulary for each model and
  provide methods for encoding/decoding strings in a list of token embeddings indices to be fed to a model.

All these classes can be instantiated from pretrained instances and saved locally using two methods:

- `from_pretrained()` lets you instantiate a model/configuration/tokenizer from a pretrained version either
  provided by the library itself (the supported models can be found on the [Model Hub](https://huggingface.co/models)) or
  stored locally (or on a server) by the user,
- `save_pretrained()` lets you save a model/configuration/tokenizer locally so that it can be reloaded using
  `from_pretrained()`.

