typos (#6505)

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 <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:
 
-- :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>`
   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()`.