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typos (#6505)

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Stas Bekman
2020-08-16 19:57:36 -07:00
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parent 6d38ab1cc3
commit df15c7c226
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docs/source/philosophy.rst
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@@ -45,12 +45,12 @@ A few other goals:
- A simple/consistent way to add new tokens to the vocabulary and embeddings for fine-tuning. - A simple/consistent way to add new tokens to the vocabulary and embeddings for fine-tuning.
- Simple ways to mask and prune transformer heads. - 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. - Switch easily between PyTorch and TensorFlow 2.0, allowing training using one framework and inference using another.
Main concepts Main concepts
~~~~~~~~~~~~~ ~~~~~~~~~~~~~
The library is build around three types of classes for each model: The library is built around three types of classes for each model:
- **Model classes** such as :class:`~transformers.BertModel`, which are 30+ PyTorch models - **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 (`torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__) or Keras models
@@ -65,9 +65,9 @@ The library is build around three types of classes for each model:
All these classes can be instantiated from pretrained instances and saved locally using two methods: 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 - :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 <pretrained_models>` 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, 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:`save_pretrained()` lets you save a model/configuration/tokenizer locally so that it can be reloaded using
:obj:`from_pretrained()`. :obj:`from_pretrained()`.