philosophy.rst

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: :doc:`configuration <main_classes/configuration>`, 
      :doc:`models <main_classes/model>` and :doc:`tokenizer <main_classes/tokenizer>`.
    - All of these classes can be initialized in a simple and unified way from pretrained instances by using a common
      :obj:`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: :func:`~transformers.pipeline` for quickly
      using a model (plus its associated tokenizer and configuration) on a given task and 
      :func:`~transformers.Trainer`/:func:`~transformers.TFTrainer` 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 framwork and inference using another.

Main concepts
~~~~~~~~~~~~~

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

- **Model classes**  such as :class:`~transformers.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 :class:`~transformers.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 :class:`~transformers.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:

- :obj:`from_pretrained()` let you instantiate a model/configuration/tokenizer from a pretrained version either
  provided by the library itself (the suported models are provided in the list :doc:`here <pretrained_models>`
  or stored locally (or on a server) by the user,
- :obj:`save_pretrained()` let you save a model/configuration/tokenizer locally so that it can be reloaded using
  :obj:`from_pretrained()`.