74 lines
4.5 KiB
ReStructuredText
74 lines
4.5 KiB
ReStructuredText
Philosophy
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==========
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🤗 Transformers is an opinionated library built for:
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- NLP researchers and educators seeking to use/study/extend large-scale transformers models
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- hands-on practitioners who want to fine-tune those models and/or serve them in production
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- engineers who just want to download a pretrained model and use it to solve a given NLP task.
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The library was designed with two strong goals in mind:
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- Be as easy and fast to use as possible:
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- We strongly limited the number of user-facing abstractions to learn, in fact, there are almost no abstractions,
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just three standard classes required to use each model: :doc:`configuration <main_classes/configuration>`,
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:doc:`models <main_classes/model>` and :doc:`tokenizer <main_classes/tokenizer>`.
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- All of these classes can be initialized in a simple and unified way from pretrained instances by using a common
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:obj:`from_pretrained()` instantiation method which will take care of downloading (if needed), caching and
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loading the related class instance and associated data (configurations' hyper-parameters, tokenizers' vocabulary,
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and models' weights) from a pretrained checkpoint provided on
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`Hugging Face Hub <https://huggingface.co/models>`__ or your own saved checkpoint.
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- On top of those three base classes, the library provides two APIs: :func:`~transformers.pipeline` for quickly
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using a model (plus its associated tokenizer and configuration) on a given task and
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:func:`~transformers.Trainer`/:func:`~transformers.TFTrainer` to quickly train or fine-tune a given model.
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- As a consequence, this library is NOT a modular toolbox of building blocks for neural nets. If you want to
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extend/build-upon the library, just use regular Python/PyTorch/TensorFlow/Keras modules and inherit from the base
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classes of the library to reuse functionalities like model loading/saving.
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- Provide state-of-the-art models with performances as close as possible to the original models:
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- We provide at least one example for each architecture which reproduces a result provided by the official authors
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of said architecture.
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- The code is usually as close to the original code base as possible which means some PyTorch code may be not as
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*pytorchic* as it could be as a result of being converted TensorFlow code and vice versa.
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A few other goals:
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- Expose the models' internals as consistently as possible:
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- We give access, using a single API, to the full hidden-states and attention weights.
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- Tokenizer and base model's API are standardized to easily switch between models.
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- Incorporate a subjective selection of promising tools for fine-tuning/investigating these models:
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- A simple/consistent way to add new tokens to the vocabulary and embeddings for fine-tuning.
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- Simple ways to mask and prune transformer heads.
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- Switch easily between PyTorch and TensorFlow 2.0, allowing training using one framework and inference using another.
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Main concepts
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~~~~~~~~~~~~~
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The library is built around three types of classes for each model:
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- **Model classes** such as :class:`~transformers.BertModel`, which are 30+ PyTorch models
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(`torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__) or Keras models
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(`tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__) that work with the pretrained
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weights provided in the library.
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- **Configuration classes** such as :class:`~transformers.BertConfig`, which store all the parameters required to build
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a model. You don't always need to instantiate these yourself. In particular, if you are using a pretrained model
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without any modification, creating the model will automatically take care of instantiating the configuration (which
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is part of the model).
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- **Tokenizer classes** such as :class:`~transformers.BertTokenizer`, which store the vocabulary for each model and
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provide methods for encoding/decoding strings in a list of token embeddings indices to be fed to a model.
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All these classes can be instantiated from pretrained instances and saved locally using two methods:
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- :obj:`from_pretrained()` lets you instantiate a model/configuration/tokenizer from a pretrained version either
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provided by the library itself (the suported models are provided in the list :doc:`here <pretrained_models>`
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or stored locally (or on a server) by the user,
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- :obj:`save_pretrained()` lets you save a model/configuration/tokenizer locally so that it can be reloaded using
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:obj:`from_pretrained()`.
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