From df15c7c226b5651d00c20093756cfdc7189e2a17 Mon Sep 17 00:00:00 2001 From: Stas Bekman Date: Sun, 16 Aug 2020 19:57:36 -0700 Subject: [PATCH] typos (#6505) --- docs/source/philosophy.rst | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/source/philosophy.rst b/docs/source/philosophy.rst index ed6173f6fc..9fd17683e7 100644 --- a/docs/source/philosophy.rst +++ b/docs/source/philosophy.rst @@ -45,12 +45,12 @@ A few other goals: - 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. +- Switch easily between PyTorch and TensorFlow 2.0, allowing training using one framework and inference using another. 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 (`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: -- :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 ` 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()`.