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 `, :doc:`models ` and :doc:`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 `__ 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 framework and inference using another. Main concepts ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The library is built around three types of classes for each model: - **Model classes** such as :class:`~transformers.BertModel`, which are 30+ PyTorch models (`torch.nn.Module `__) or Keras models (`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()` lets 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 ` or stored locally (or on a server) by the user, - :obj:`save_pretrained()` lets you save a model/configuration/tokenizer locally so that it can be reloaded using :obj:`from_pretrained()`.