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This commit is contained in:
Sylvain Gugger
2020-10-26 18:26:02 -04:00
committed by GitHub
parent 04a17f8550
commit 08f534d2da
271 changed files with 9726 additions and 8991 deletions
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docs/source/philosophy.rst
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@@ -12,15 +12,15 @@ 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>`,
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.
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
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
@@ -52,10 +52,10 @@ 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 <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.
- **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
@@ -66,8 +66,8 @@ The library is built around three types of classes for each 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 supported models are provided in the list :doc:`here <pretrained_models>`
or stored locally (or on a server) by the user,
provided by the library itself (the supported models are provided in the list :doc:`here <pretrained_models>` 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()`.