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Single file documentation for each model, accompanied by the Documentation overview.
This commit is contained in:
LysandreJik
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Home
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====
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@ -4,16 +4,29 @@ Pytorch-Transformers: The Big & Extending Repository of pretrained Transformers
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.. toctree::
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:maxdepth: 2
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:caption: Notes
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installation
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usage
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doc
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examples
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notebooks
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tpu
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cli
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.. toctree::
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:maxdepth: 2
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:caption: Package Reference
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model_doc/overview
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model_doc/bert
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model_doc/gpt
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model_doc/transformerxl
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model_doc/gpt2
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model_doc/xlm
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model_doc/xlnet
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.. image:: https://circleci.com/gh/huggingface/pytorch-pretrained-BERT.svg?style=svg
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:target: https://circleci.com/gh/huggingface/pytorch-pretrained-BERT
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:alt: CircleCI
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110
docs/source/model_doc/bert.rst
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110
docs/source/model_doc/bert.rst
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BERT
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----------------------------------------------------
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``BertTokenizer``
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~~~~~~~~~~~~~~~~~~~~~
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``BertTokenizer`` perform end-to-end tokenization, i.e. basic tokenization followed by WordPiece tokenization.
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This class has five arguments:
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* ``vocab_file``\ : path to a vocabulary file.
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* ``do_lower_case``\ : convert text to lower-case while tokenizing. **Default = True**.
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* ``max_len``\ : max length to filter the input of the Transformer. Default to pre-trained value for the model if ``None``. **Default = None**
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* ``do_basic_tokenize``\ : Do basic tokenization before wordpice tokenization. Set to false if text is pre-tokenized. **Default = True**.
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* ``never_split``\ : a list of tokens that should not be splitted during tokenization. **Default = ``["[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]"]``\ **
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and three methods:
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* ``tokenize(text)``\ : convert a ``str`` in a list of ``str`` tokens by (1) performing basic tokenization and (2) WordPiece tokenization.
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* ``convert_tokens_to_ids(tokens)``\ : convert a list of ``str`` tokens in a list of ``int`` indices in the vocabulary.
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* ``convert_ids_to_tokens(tokens)``\ : convert a list of ``int`` indices in a list of ``str`` tokens in the vocabulary.
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* `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')``.
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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.
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``BertAdam``
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~~~~~~~~~~~~~~~~
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``BertAdam`` is a ``torch.optimizer`` adapted to be closer to the optimizer used in the TensorFlow implementation of Bert. The differences with PyTorch Adam optimizer are the following:
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* BertAdam implements weight decay fix,
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* BertAdam doesn't compensate for bias as in the regular Adam optimizer.
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The optimizer accepts the following arguments:
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* ``lr`` : learning rate
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* ``warmup`` : portion of ``t_total`` for the warmup, ``-1`` means no warmup. Default : ``-1``
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* ``t_total`` : total number of training steps for the learning
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rate schedule, ``-1`` means constant learning rate. Default : ``-1``
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* ``schedule`` : schedule to use for the warmup (see above).
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Can be ``'warmup_linear'``\ , ``'warmup_constant'``\ , ``'warmup_cosine'``\ , ``'none'``\ , ``None`` or a ``_LRSchedule`` object (see below).
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If ``None`` or ``'none'``\ , learning rate is always kept constant.
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Default : ``'warmup_linear'``
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* ``b1`` : Adams b1. Default : ``0.9``
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* ``b2`` : Adams b2. Default : ``0.999``
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* ``e`` : Adams epsilon. Default : ``1e-6``
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* ``weight_decay:`` Weight decay. Default : ``0.01``
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* ``max_grad_norm`` : Maximum norm for the gradients (\ ``-1`` means no clipping). Default : ``1.0``
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1. ``BertModel``
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~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.BertModel
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:members:
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2. ``BertForPreTraining``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.BertForPreTraining
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:members:
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3. ``BertForMaskedLM``
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~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.BertForMaskedLM
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:members:
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4. ``BertForNextSentencePrediction``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.BertForNextSentencePrediction
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:members:
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5. ``BertForSequenceClassification``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.BertForSequenceClassification
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:members:
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6. ``BertForMultipleChoice``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.BertForMultipleChoice
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:members:
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7. ``BertForTokenClassification``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.BertForTokenClassification
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:members:
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8. ``BertForQuestionAnswering``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.BertForQuestionAnswering
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:members:
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59
docs/source/model_doc/gpt.rst
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59
docs/source/model_doc/gpt.rst
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OpenAI GPT
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----------------------------------------------------
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``OpenAIGPTTokenizer``
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~~~~~~~~~~~~~~~~~~~~~~~~~~
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``OpenAIGPTTokenizer`` perform Byte-Pair-Encoding (BPE) tokenization.
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This class has four arguments:
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* ``vocab_file``\ : path to a vocabulary file.
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* ``merges_file``\ : path to a file containing the BPE merges.
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* ``max_len``\ : max length to filter the input of the Transformer. Default to pre-trained value for the model if ``None``. **Default = None**
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* ``special_tokens``\ : a list of tokens to add to the vocabulary for fine-tuning. If SpaCy is not installed and BERT's ``BasicTokenizer`` is used as the pre-BPE tokenizer, these tokens are not split. **Default= None**
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and five methods:
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* ``tokenize(text)``\ : convert a ``str`` in a list of ``str`` tokens by performing BPE tokenization.
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* ``convert_tokens_to_ids(tokens)``\ : convert a list of ``str`` tokens in a list of ``int`` indices in the vocabulary.
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* ``convert_ids_to_tokens(tokens)``\ : convert a list of ``int`` indices in a list of ``str`` tokens in the vocabulary.
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* ``set_special_tokens(self, special_tokens)``\ : update the list of special tokens (see above arguments)
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* ``encode(text)``\ : convert a ``str`` in a list of ``int`` tokens by performing BPE encoding.
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* `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.
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* `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')``.
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Please refer to the doc strings and code in `\ ``tokenization_openai.py`` <./pytorch_pretrained_bert/tokenization_openai.py>`_ for the details of the ``OpenAIGPTTokenizer``.
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``OpenAIAdam``
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~~~~~~~~~~~~~~~~~~
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``OpenAIAdam`` is similar to ``BertAdam``.
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The differences with ``BertAdam`` is that ``OpenAIAdam`` compensate for bias as in the regular Adam optimizer.
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``OpenAIAdam`` accepts the same arguments as ``BertAdam``.
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9. ``OpenAIGPTModel``
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~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.OpenAIGPTModel
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:members:
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10. ``OpenAIGPTLMHeadModel``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.OpenAIGPTLMHeadModel
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:members:
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11. ``OpenAIGPTDoubleHeadsModel``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.OpenAIGPTDoubleHeadsModel
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:members:
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49
docs/source/model_doc/gpt2.rst
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49
docs/source/model_doc/gpt2.rst
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OpenAI GPT2
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----------------------------------------------------
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``GPT2Tokenizer``
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~~~~~~~~~~~~~~~~~~~~~
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``GPT2Tokenizer`` perform byte-level Byte-Pair-Encoding (BPE) tokenization.
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This class has three arguments:
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* ``vocab_file``\ : path to a vocabulary file.
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* ``merges_file``\ : path to a file containing the BPE merges.
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* ``errors``\ : How to handle unicode decoding errors. **Default = ``replace``\ **
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and two methods:
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* ``tokenize(text)``\ : convert a ``str`` in a list of ``str`` tokens by performing byte-level BPE.
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* ``convert_tokens_to_ids(tokens)``\ : convert a list of ``str`` tokens in a list of ``int`` indices in the vocabulary.
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* ``convert_ids_to_tokens(tokens)``\ : convert a list of ``int`` indices in a list of ``str`` tokens in the vocabulary.
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* ``set_special_tokens(self, special_tokens)``\ : update the list of special tokens (see above arguments)
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* ``encode(text)``\ : convert a ``str`` in a list of ``int`` tokens by performing byte-level BPE.
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* ``decode(tokens)``\ : convert back a list of ``int`` tokens in a ``str``.
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* `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')``.
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Please refer to `\ ``tokenization_gpt2.py`` <./pytorch_pretrained_bert/tokenization_gpt2.py>`_ for more details on the ``GPT2Tokenizer``.
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14. ``GPT2Model``
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~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.GPT2Model
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:members:
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15. ``GPT2LMHeadModel``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.GPT2LMHeadModel
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:members:
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16. ``GPT2DoubleHeadsModel``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.GPT2DoubleHeadsModel
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:members:
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@ -1,8 +1,7 @@
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Docs
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Overview
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================================================
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Here is a detailed documentation of the classes in the package and how to use them:
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.. list-table::
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@ -24,6 +23,31 @@ Here is a detailed documentation of the classes in the package and how to use th
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- API of the optimizers
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Configurations
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^^^^^^^^^^^^^^
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Models (BERT, GPT, GPT-2 and Transformer-XL) are defined and build from configuration classes which contains the
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parameters of the models (number of layers, dimensionalities...) and a few utilities to read and write from JSON
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configuration files. The respective configuration classes are:
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* ``BertConfig`` for ``BertModel`` and BERT classes instances.
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* ``OpenAIGPTConfig`` for ``OpenAIGPTModel`` and OpenAI GPT classes instances.
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* ``GPT2Config`` for ``GPT2Model`` and OpenAI GPT-2 classes instances.
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* ``TransfoXLConfig`` for ``TransfoXLModel`` and Transformer-XL classes instances.
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These configuration classes contains a few utilities to load and save configurations:
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* ``from_dict(cls, json_object)``\ : A class method to construct a configuration from a Python dictionary of parameters.
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Returns an instance of the configuration class.
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* ``from_json_file(cls, json_file)``\ : A class method to construct a configuration from a json file of parameters.
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Returns an instance of the configuration class.
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* ``to_dict()``\ : Serializes an instance to a Python dictionary. Returns a dictionary.
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* ``to_json_string()``\ : Serializes an instance to a JSON string. Returns a string.
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* ``to_json_file(json_file_path)``\ : Save an instance to a json file.
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Loading Google AI or OpenAI pre-trained weights or PyTorch dump
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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@ -124,7 +148,7 @@ Usually, if you don't set any specific environment variable, ``pytorch_pretraine
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You can alsways safely delete ``pytorch_pretrained_bert`` cache but the pretrained model weights and vocabulary files wil have to be re-downloaded from our S3.
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Serialization best-practices
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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This section explain how you can save and re-load a fine-tuned model (BERT, GPT, GPT-2 and Transformer-XL).
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There are three types of files you need to save to be able to reload a fine-tuned model:
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@ -212,267 +236,8 @@ Here is another way you can save and reload the model if you want to use specifi
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model.load_state_dict(state_dict)
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tokenizer = OpenAIGPTTokenizer(output_vocab_file)
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Configurations
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^^^^^^^^^^^^^^
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Models (BERT, GPT, GPT-2 and Transformer-XL) are defined and build from configuration classes which containes the parameters of the models (number of layers, dimensionalities...) and a few utilities to read and write from JSON configuration files. The respective configuration classes are:
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* ``BertConfig`` for ``BertModel`` and BERT classes instances.
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* ``OpenAIGPTConfig`` for ``OpenAIGPTModel`` and OpenAI GPT classes instances.
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* ``GPT2Config`` for ``GPT2Model`` and OpenAI GPT-2 classes instances.
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* ``TransfoXLConfig`` for ``TransfoXLModel`` and Transformer-XL classes instances.
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These configuration classes contains a few utilities to load and save configurations:
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* ``from_dict(cls, json_object)``\ : A class method to construct a configuration from a Python dictionary of parameters. Returns an instance of the configuration class.
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* ``from_json_file(cls, json_file)``\ : A class method to construct a configuration from a json file of parameters. Returns an instance of the configuration class.
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* ``to_dict()``\ : Serializes an instance to a Python dictionary. Returns a dictionary.
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* ``to_json_string()``\ : Serializes an instance to a JSON string. Returns a string.
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* ``to_json_file(json_file_path)``\ : Save an instance to a json file.
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Models
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^^^^^^
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1. ``BertModel``
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~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.BertModel
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:members:
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2. ``BertForPreTraining``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.BertForPreTraining
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:members:
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3. ``BertForMaskedLM``
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~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.BertForMaskedLM
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:members:
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4. ``BertForNextSentencePrediction``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.BertForNextSentencePrediction
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:members:
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5. ``BertForSequenceClassification``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.BertForSequenceClassification
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:members:
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6. ``BertForMultipleChoice``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.BertForMultipleChoice
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:members:
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7. ``BertForTokenClassification``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.BertForTokenClassification
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:members:
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8. ``BertForQuestionAnswering``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.BertForQuestionAnswering
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:members:
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9. ``OpenAIGPTModel``
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~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.OpenAIGPTModel
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:members:
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10. ``OpenAIGPTLMHeadModel``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.OpenAIGPTLMHeadModel
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:members:
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11. ``OpenAIGPTDoubleHeadsModel``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.OpenAIGPTDoubleHeadsModel
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:members:
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12. ``TransfoXLModel``
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~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.TransfoXLModel
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:members:
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13. ``TransfoXLLMHeadModel``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.TransfoXLLMHeadModel
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:members:
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14. ``GPT2Model``
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~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.GPT2Model
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:members:
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15. ``GPT2LMHeadModel``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.GPT2LMHeadModel
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:members:
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16. ``GPT2DoubleHeadsModel``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_pretrained_bert.GPT2DoubleHeadsModel
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:members:
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Tokenizers
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^^^^^^^^^^
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``BertTokenizer``
|
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~~~~~~~~~~~~~~~~~~~~~
|
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|
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``BertTokenizer`` perform end-to-end tokenization, i.e. basic tokenization followed by WordPiece tokenization.
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This class has five arguments:
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|
||||
* ``vocab_file``\ : path to a vocabulary file.
|
||||
* ``do_lower_case``\ : convert text to lower-case while tokenizing. **Default = True**.
|
||||
* ``max_len``\ : max length to filter the input of the Transformer. Default to pre-trained value for the model if ``None``. **Default = None**
|
||||
* ``do_basic_tokenize``\ : Do basic tokenization before wordpice tokenization. Set to false if text is pre-tokenized. **Default = True**.
|
||||
* ``never_split``\ : a list of tokens that should not be splitted during tokenization. **Default = ``["[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]"]``\ **
|
||||
|
||||
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.
|
||||
|
||||
``OpenAIGPTTokenizer``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
``OpenAIGPTTokenizer`` perform Byte-Pair-Encoding (BPE) tokenization.
|
||||
|
||||
This class has four arguments:
|
||||
|
||||
|
||||
* ``vocab_file``\ : path to a vocabulary file.
|
||||
* ``merges_file``\ : path to a file containing the BPE merges.
|
||||
* ``max_len``\ : max length to filter the input of the Transformer. Default to pre-trained value for the model if ``None``. **Default = None**
|
||||
* ``special_tokens``\ : a list of tokens to add to the vocabulary for fine-tuning. If SpaCy is not installed and BERT's ``BasicTokenizer`` is used as the pre-BPE tokenizer, these tokens are not split. **Default= None**
|
||||
|
||||
and five methods:
|
||||
|
||||
|
||||
* ``tokenize(text)``\ : convert a ``str`` in a list of ``str`` tokens by performing BPE 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.
|
||||
* ``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 BPE encoding.
|
||||
* `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``.
|
||||
|
||||
``TransfoXLTokenizer``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
``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``
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
``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``.
|
||||
|
||||
Optimizers
|
||||
^^^^^^^^^^
|
||||
|
||||
``BertAdam``
|
||||
~~~~~~~~~~~~~~~~
|
||||
|
||||
``BertAdam`` is a ``torch.optimizer`` adapted to be closer to the optimizer used in the TensorFlow implementation of Bert. The differences with PyTorch Adam optimizer are the following:
|
||||
|
||||
|
||||
* BertAdam implements weight decay fix,
|
||||
* BertAdam doesn't compensate for bias as in the regular Adam optimizer.
|
||||
|
||||
The optimizer accepts the following arguments:
|
||||
|
||||
|
||||
* ``lr`` : learning rate
|
||||
* ``warmup`` : portion of ``t_total`` for the warmup, ``-1`` means no warmup. Default : ``-1``
|
||||
* ``t_total`` : total number of training steps for the learning
|
||||
rate schedule, ``-1`` means constant learning rate. Default : ``-1``
|
||||
* ``schedule`` : schedule to use for the warmup (see above).
|
||||
Can be ``'warmup_linear'``\ , ``'warmup_constant'``\ , ``'warmup_cosine'``\ , ``'none'``\ , ``None`` or a ``_LRSchedule`` object (see below).
|
||||
If ``None`` or ``'none'``\ , learning rate is always kept constant.
|
||||
Default : ``'warmup_linear'``
|
||||
* ``b1`` : Adams b1. Default : ``0.9``
|
||||
* ``b2`` : Adams b2. Default : ``0.999``
|
||||
* ``e`` : Adams epsilon. Default : ``1e-6``
|
||||
* ``weight_decay:`` Weight decay. Default : ``0.01``
|
||||
* ``max_grad_norm`` : Maximum norm for the gradients (\ ``-1`` means no clipping). Default : ``1.0``
|
||||
|
||||
``OpenAIAdam``
|
||||
~~~~~~~~~~~~~~~~~~
|
||||
|
||||
``OpenAIAdam`` is similar to ``BertAdam``.
|
||||
The differences with ``BertAdam`` is that ``OpenAIAdam`` compensate for bias as in the regular Adam optimizer.
|
||||
|
||||
``OpenAIAdam`` accepts the same arguments as ``BertAdam``.
|
||||
|
||||
Learning Rate Schedules
|
||||
~~~~~~~~~~~~~~~~~~~~~~~
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
The ``.optimization`` module also provides additional schedules in the form of schedule objects that inherit from ``_LRSchedule``.
|
||||
All ``_LRSchedule`` subclasses accept ``warmup`` and ``t_total`` arguments at construction.
|
||||
26
docs/source/model_doc/transformerxl.rst
Normal file
26
docs/source/model_doc/transformerxl.rst
Normal file
|
|
@ -0,0 +1,26 @@
|
|||
Transformer XL
|
||||
----------------------------------------------------
|
||||
|
||||
|
||||
``TransfoXLTokenizer``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
``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``.
|
||||
|
||||
|
||||
12. ``TransfoXLModel``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: pytorch_pretrained_bert.TransfoXLModel
|
||||
:members:
|
||||
|
||||
|
||||
13. ``TransfoXLLMHeadModel``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: pytorch_pretrained_bert.TransfoXLLMHeadModel
|
||||
:members:
|
||||
2
docs/source/model_doc/xlm.rst
Normal file
2
docs/source/model_doc/xlm.rst
Normal file
|
|
@ -0,0 +1,2 @@
|
|||
XLM
|
||||
----------------------------------------------------
|
||||
2
docs/source/model_doc/xlnet.rst
Normal file
2
docs/source/model_doc/xlnet.rst
Normal file
|
|
@ -0,0 +1,2 @@
|
|||
XLNet
|
||||
----------------------------------------------------
|
||||
Loading…
Reference in a new issue