fix typos

README.md

@@ -169,7 +169,7 @@ where
 
 - `BERT_CLASS` is either the `BertTokenizer` class (to load the vocabulary) or one of the six PyTorch model classes (to load the pre-trained weights): `BertModel`, `BertForMaskedLM`, `BertForNextSentencePrediction`, `BertForPreTraining`, `BertForSequenceClassification` or `BertForQuestionAnswering`, and
 
-- `PRE_TRAINED_MODEL_NAME` is either:
+- `PRE_TRAINED_MODEL_NAME_OR_PATH` is either:
 
   - the shortcut name of a Google AI's pre-trained model selected in the list:
 
@@ -184,7 +184,7 @@ where
       - `bert_config.json` a configuration file for the model, and
       - `pytorch_model.bin` a PyTorch dump of a pre-trained instance `BertForPreTraining` (saved with the usual `torch.save()`)
 
-If `PRE_TRAINED_MODEL_NAME` is a shortcut name, the pre-trained weights will be downloaded from AWS S3 (see the links [here](pytorch_pretrained_bert/modeling.py)) and stored in a cache folder to avoid future download (the cache folder can be found at `~/.pytorch_pretrained_bert/`).
+If `PRE_TRAINED_MODEL_NAME_OR_PATH` is a shortcut name, the pre-trained weights will be downloaded from AWS S3 (see the links [here](pytorch_pretrained_bert/modeling.py)) and stored in a cache folder to avoid future download (the cache folder can be found at `~/.pytorch_pretrained_bert/`).
 
 Example:
 ```python
@@ -203,15 +203,15 @@ We detail them here. This model takes as *inputs*:
 
 - `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] with the word token indices in the vocabulary (see the tokens preprocessing logic in the scripts `extract_features.py`, `run_classifier.py` and `run_squad.py`), and
 - `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to a `sentence B` token (see BERT paper for more details).
-- `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max input sequence length in the current batch. It's the mask that we typically use for attention when a batch has varying length sentences.
+- `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [0, 1]. It's a mask to be used if some input sequence lengths are smaller than the max input sequence length of the current batch. It's the mask that we typically use for attention when a batch has varying length sentences.
 - `output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`.
 
 This model *outputs* a tuple composed of:
 
 - `encoded_layers`: controled by the value of the `output_encoded_layers` argument:
 
-  . `output_all_encoded_layers=True`: outputs a list of the encoded-hidden-states at the end of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
-  . `output_all_encoded_layers=False`: outputs only the encoded-hidden-states corresponding to the last attention block,
+  - `output_all_encoded_layers=True`: outputs a list of the encoded-hidden-states at the end of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
+  - `output_all_encoded_layers=False`: outputs only the encoded-hidden-states corresponding to the last attention block, i.e. a single torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
 
 - `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a classifier pretrained on top of the hidden state associated to the first character of the input (`CLF`) to train on the Next-Sentence task (see BERT's paper).
 
@@ -233,6 +233,7 @@ An example on how to use this class is given in the `extract_features.py` script
 
 - if `masked_lm_labels` and `next_sentence_label` are not `None`: Outputs the total_loss which is the sum of the masked language modeling loss and the next sentence classification loss.
 - if `masked_lm_labels` or `next_sentence_label` is `None`: Outputs a tuple comprising
+
   - the masked language modeling logits, and
   - the next sentence classification logits.