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Tokenizers and Config classes are referenced.

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LysandreJik
2019-07-05 17:44:59 -04:00
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docs/source/model_doc/gpt2.rst
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OpenAI GPT2
----------------------------------------------------
``GPT2Config``
~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: pytorch_pretrained_bert.GPT2Config
:members:
``GPT2Tokenizer``
~~~~~~~~~~~~~~~~~~~~~
``GPT2Tokenizer`` perform byte-level Byte-Pair-Encoding (BPE) tokenization.
This class has three arguments:
* ``vocab_file``\ : path to a vocabulary file.
* ``merges_file``\ : path to a file containing the BPE merges.
* ``errors``\ : How to handle unicode decoding errors. **Default = ``replace``\ **
and two methods:
* ``tokenize(text)``\ : convert a ``str`` in a list of ``str`` tokens by performing byte-level BPE.
* ``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.
* ``set_special_tokens(self, special_tokens)``\ : update the list of special tokens (see above arguments)
* ``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``.
.. autoclass:: pytorch_pretrained_bert.GPT2Tokenizer
:members:
14. ``GPT2Model``