update readme

README.md

@@ -796,8 +796,7 @@ This model *outputs*:
   - `multiple_choice_logits`: the multiple choice logits as a torch.FloatTensor of size [batch_size, num_choices]
   - `presents`: a list of pre-computed hidden-states (key and values in each attention blocks) as a torch.FloatTensors. They can be reused to speed up sequential decoding (see the `run_gpt2.py` example).
 
-
-### Tokenizers:
+### Tokenizers
 
 #### `BertTokenizer`
 
@@ -816,6 +815,7 @@ and three methods:
 - `tokenize(text)`: convert a `str` in a list of `str` tokens by (1) performing basic tokenization and (2) WordPiece tokenization.
 - `convert_tokens_to_ids(tokens)`: convert a list of `str` tokens in a list of `int` indices in the vocabulary.
 - `convert_ids_to_tokens(tokens)`: convert a list of `int` indices in a list of `str` tokens in the vocabulary.
+- `save_vocabulary(directory_path)`: save the vocabulary file to `directory_path`. Return the path to the saved vocabulary file: `vocab_file_path`. The vocabulary can be reloaded with `BertTokenizer.from_pretrained('vocab_file_path')` or `BertTokenizer.from_pretrained('directory_path')`.
 
 Please refer to the doc strings and code in [`tokenization.py`](./pytorch_pretrained_bert/tokenization.py) for the details of the `BasicTokenizer` and `WordpieceTokenizer` classes. In general it is recommended to use `BertTokenizer` unless you know what you are doing.
 
@@ -837,6 +837,7 @@ and five methods:
 - `convert_ids_to_tokens(tokens)`: convert a list of `int` indices in a list of `str` tokens in the vocabulary.
 - `set_special_tokens(self, special_tokens)`: update the list of special tokens (see above arguments)
 - `decode(ids, skip_special_tokens=False, clean_up_tokenization_spaces=False)`: decode a list of `int` indices in a string and do some post-processing if needed: (i) remove special tokens from the output and (ii) clean up tokenization spaces.
+- `save_vocabulary(directory_path)`: save the vocabulary, merge and special tokens files to `directory_path`. Return the path to the three files: `vocab_file_path`, `merge_file_path`, `special_tokens_file_path`. The vocabulary can be reloaded with `OpenAIGPTTokenizer.from_pretrained('directory_path')`.
 
 Please refer to the doc strings and code in [`tokenization_openai.py`](./pytorch_pretrained_bert/tokenization_openai.py) for the details of the `OpenAIGPTTokenizer`.
 
@@ -844,6 +845,8 @@ Please refer to the doc strings and code in [`tokenization_openai.py`](./pytorch
 
 `TransfoXLTokenizer` perform word tokenization. This tokenizer can be used for adaptive softmax and has utilities for counting tokens in a corpus to create a vocabulary ordered by toekn frequency (for adaptive softmax). See the adaptive softmax paper ([Efficient softmax approximation for GPUs](http://arxiv.org/abs/1609.04309)) for more details.
 
+The API is similar to the API of `BertTokenizer` (see above).
+
 Please refer to the doc strings and code in [`tokenization_transfo_xl.py`](./pytorch_pretrained_bert/tokenization_transfo_xl.py) for the details of these additional methods in `TransfoXLTokenizer`.
 
 #### `GPT2Tokenizer`
@@ -860,11 +863,11 @@ and two methods:
 
 - `encode(text)`: convert a `str` in a list of `int` tokens by performing byte-level BPE.
 - `decode(tokens)`: convert back a list of `int` tokens in a `str`.
+- `save_vocabulary(directory_path)`: save the vocabulary, merge and special tokens files to `directory_path`. Return the path to the three files: `vocab_file_path`, `merge_file_path`, `special_tokens_file_path`. The vocabulary can be reloaded with `OpenAIGPTTokenizer.from_pretrained('directory_path')`.
 
 Please refer to [`tokenization_gpt2.py`](./pytorch_pretrained_bert/tokenization_gpt2.py) for more details on the `GPT2Tokenizer`.
 
-
-### Optimizers:
+### Optimizers
 
 #### `BertAdam`