6 -> 8 models

docs/source/quickstart.md

@@ -33,7 +33,7 @@ A few other goals:
 
 The library is build around three type of classes for each models:
 
-- **model classes** which are PyTorch models (`torch.nn.Modules`) of the 6 models architectures currently provided in the library, e.g. `BertModel`
+- **model classes** which are PyTorch models (`torch.nn.Modules`) of the 8 models architectures currently provided in the library, e.g. `BertModel`
 - **configuration classes** which store all the parameters required to build a model, e.g. `BertConfig`. You don't always need to instantiate these your-self, in particular if you are using a pretrained model without any modification, creating the model will automatically take care of instantiating the configuration (which is part of the model)
 - **tokenizer classes** which store the vocabulary for each model and provide methods for encoding/decoding strings in list of token embeddings indices to be fed to a model, e.g. `BertTokenizer`
 

transformers/tokenization_utils.py

@@ -430,7 +430,7 @@ class PreTrainedTokenizer(object):
                 - tokenizer instantiation positional and keywords inputs (e.g. do_lower_case for Bert).
 
             This won't save modifications other than (added tokens and special token mapping) you may have
-            applied to the tokenizer after the instantion (e.g. modifying tokenizer.do_lower_case after creation).
+            applied to the tokenizer after the instantiation (e.g. modifying tokenizer.do_lower_case after creation).
 
             This method make sure the full tokenizer can then be re-loaded using the :func:`~transformers.PreTrainedTokenizer.from_pretrained` class method.
         """