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LysandreJik
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6 changed files with 327 additions and 125 deletions
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@ -47,3 +47,4 @@ The library currently contains PyTorch implementations, pre-trained model weight
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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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model_doc/roberta
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36
docs/source/model_doc/roberta.rst
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36
docs/source/model_doc/roberta.rst
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@ -0,0 +1,36 @@
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RoBERTa
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----------------------------------------------------
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``RobertaConfig``
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~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_transformers.RobertaConfig
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:members:
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``RobertaTokenizer``
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~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_transformers.RobertaTokenizer
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:members:
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``RobertaModel``
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~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_transformers.RobertaModel
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:members:
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``RobertaForMaskedLM``
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~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_transformers.RobertaForMaskedLM
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:members:
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``RobertaForSequenceClassification``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_transformers.RobertaForSequenceClassification
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:members:
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@ -4,97 +4,109 @@ Pretrained models
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Here is the full list of the currently provided pretrained models together with a short presentation of each model.
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+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| Architecture | Shortcut name | Details of the model |
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+===================+============================================================+===========================================================================================================================+
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| BERT | ``bert-base-uncased`` | 12-layer, 768-hidden, 12-heads, 110M parameters |
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| | | Trained on lower-cased English text |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-large-uncased`` | 24-layer, 1024-hidden, 16-heads, 340M parameters |
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| | | Trained on lower-cased English text |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-base-cased`` | 12-layer, 768-hidden, 12-heads, 110M parameters |
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| | | Trained on cased English text |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-large-cased`` | 24-layer, 1024-hidden, 16-heads, 340M parameters |
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| | | Trained on cased English text |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-base-multilingual-uncased`` | (Original, not recommended) 12-layer, 768-hidden, 12-heads, 110M parameters |
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| | | Trained on lower-cased text in the top 102 languages with the largest Wikipedias |
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| | | (see `details <https://github.com/google-research/bert/blob/master/multilingual.md>`__) |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-base-multilingual-cased`` | (New, **recommended**) 12-layer, 768-hidden, 12-heads, 110M parameters |
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| | | Trained on cased text in the top 104 languages with the largest Wikipedias |
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| | | (see `details <https://github.com/google-research/bert/blob/master/multilingual.md>`__) |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-base-chinese`` | 12-layer, 768-hidden, 12-heads, 110M parameters |
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| | | Trained on cased Chinese Simplified and Traditional text |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-base-german-cased`` | 12-layer, 768-hidden, 12-heads, 110M parameters |
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| | | Trained on cased German text by Deepset.ai |
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| | | (see `details on deepset.ai website <https://deepset.ai/german-bert>`__) |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-large-uncased-whole-word-masking`` | 24-layer, 1024-hidden, 16-heads, 340M parameters |
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| | | Trained on lower-cased English text using Whole-Word-Masking |
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| | | (see `details <https://github.com/google-research/bert/#bert>`__) |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-large-cased-whole-word-masking`` | 24-layer, 1024-hidden, 16-heads, 340M parameters |
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| | | Trained on cased English text using Whole-Word-Masking |
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| | | (see `details <https://github.com/google-research/bert/#bert>`__) |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-large-uncased-whole-word-masking-finetuned-squad`` | 24-layer, 1024-hidden, 16-heads, 340M parameters |
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| | | The ``bert-large-uncased-whole-word-masking`` model fine-tuned on SQuAD (see details of fine-tuning in the |
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| | | `example section <https://github.com/huggingface/pytorch-transformers/tree/master/examples>`__) |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-large-cased-whole-word-masking-finetuned-squad`` | 24-layer, 1024-hidden, 16-heads, 340M parameters |
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| | | The ``bert-large-cased-whole-word-masking`` model fine-tuned on SQuAD |
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| | | (see `details of fine-tuning in the example section <https://huggingface.co/pytorch-transformers/examples.html>`__) |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-base-cased-finetuned-mrpc`` | 12-layer, 768-hidden, 12-heads, 110M parameters |
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| | | The ``bert-base-cased`` model fine-tuned on MRPC |
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| | | (see `details of fine-tuning in the example section <https://huggingface.co/pytorch-transformers/examples.html>`__) |
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+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| GPT | ``openai-gpt`` | 12-layer, 768-hidden, 12-heads, 110M parameters |
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| | | OpenAI GPT English model |
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+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| GPT-2 | ``gpt2`` | 12-layer, 768-hidden, 12-heads, 117M parameters |
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| | | OpenAI GPT-2 English model |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| | ``gpt2-medium`` | 24-layer, 1024-hidden, 16-heads, 345M parameters |
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| | | OpenAI's Medium-sized GPT-2 English model |
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+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| Transformer-XL | ``transfo-xl-wt103`` | 18-layer, 1024-hidden, 16-heads, 257M parameters |
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| | | English model trained on wikitext-103 |
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+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| XLNet | ``xlnet-base-cased`` | 12-layer, 768-hidden, 12-heads, 110M parameters |
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| | | XLNet English model |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| | ``xlnet-large-cased`` | 24-layer, 1024-hidden, 16-heads, 340M parameters |
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| | | XLNet Large English model |
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+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| XLM | ``xlm-mlm-en-2048`` | 12-layer, 1024-hidden, 8-heads |
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| | | XLM English model |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| | ``xlm-mlm-ende-1024`` | 12-layer, 1024-hidden, 8-heads |
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| | | XLM English-German Multi-language model |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| | ``xlm-mlm-enfr-1024`` | 12-layer, 1024-hidden, 8-heads |
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| | | XLM English-French Multi-language model |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| | ``xlm-mlm-enro-1024`` | 12-layer, 1024-hidden, 8-heads |
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| | | XLM English-Romanian Multi-language model |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| | ``xlm-mlm-xnli15-1024`` | 12-layer, 1024-hidden, 8-heads |
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| | | XLM Model pre-trained with MLM on the `15 XNLI languages <https://github.com/facebookresearch/XNLI>`__. |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| | ``xlm-mlm-tlm-xnli15-1024`` | 12-layer, 1024-hidden, 8-heads |
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| | | XLM Model pre-trained with MLM + TLM on the `15 XNLI languages <https://github.com/facebookresearch/XNLI>`__. |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| | ``xlm-clm-enfr-1024`` | 12-layer, 1024-hidden, 8-heads |
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| | | XLM English model trained with CLM (Causal Language Modeling) |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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| | ``xlm-clm-ende-1024`` | 12-layer, 1024-hidden, 8-heads |
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| | | XLM English-German Multi-language model trained with CLM (Causal Language Modeling) |
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+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+
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+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| Architecture | Shortcut name | Details of the model |
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+===================+============================================================+=======================================================================================================================================+
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| BERT | ``bert-base-uncased`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | Trained on lower-cased English text. |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-large-uncased`` | | 24-layer, 1024-hidden, 16-heads, 340M parameters. |
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| | | | Trained on lower-cased English text. |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-base-cased`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | Trained on cased English text. |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-large-cased`` | | 24-layer, 1024-hidden, 16-heads, 340M parameters. |
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| | | | Trained on cased English text. |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-base-multilingual-uncased`` | | (Original, not recommended) 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | Trained on lower-cased text in the top 102 languages with the largest Wikipedias |
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| | | (see `details <https://github.com/google-research/bert/blob/master/multilingual.md>`__). |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-base-multilingual-cased`` | | (New, **recommended**) 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | Trained on cased text in the top 104 languages with the largest Wikipedias |
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| | | (see `details <https://github.com/google-research/bert/blob/master/multilingual.md>`__). |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-base-chinese`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | Trained on cased Chinese Simplified and Traditional text. |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-base-german-cased`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | Trained on cased German text by Deepset.ai |
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| | | (see `details on deepset.ai website <https://deepset.ai/german-bert>`__). |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-large-uncased-whole-word-masking`` | | 24-layer, 1024-hidden, 16-heads, 340M parameters. |
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| | | | Trained on lower-cased English text using Whole-Word-Masking |
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| | | (see `details <https://github.com/google-research/bert/#bert>`__). |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-large-cased-whole-word-masking`` | | 24-layer, 1024-hidden, 16-heads, 340M parameters. |
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| | | | Trained on cased English text using Whole-Word-Masking |
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| | | (see `details <https://github.com/google-research/bert/#bert>`__). |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-large-uncased-whole-word-masking-finetuned-squad`` | | 24-layer, 1024-hidden, 16-heads, 340M parameters. |
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| | | | The ``bert-large-uncased-whole-word-masking`` model fine-tuned on SQuAD |
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| | | (see details of fine-tuning in the `example section <https://github.com/huggingface/pytorch-transformers/tree/master/examples>`__). |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-large-cased-whole-word-masking-finetuned-squad`` | | 24-layer, 1024-hidden, 16-heads, 340M parameters |
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| | | | The ``bert-large-cased-whole-word-masking`` model fine-tuned on SQuAD |
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| | | (see `details of fine-tuning in the example section <https://huggingface.co/pytorch-transformers/examples.html>`__) |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-base-cased-finetuned-mrpc`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | The ``bert-base-cased`` model fine-tuned on MRPC |
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| | | (see `details of fine-tuning in the example section <https://huggingface.co/pytorch-transformers/examples.html>`__) |
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+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| GPT | ``openai-gpt`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | OpenAI GPT English model |
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+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| GPT-2 | ``gpt2`` | | 12-layer, 768-hidden, 12-heads, 117M parameters. |
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| | | | OpenAI GPT-2 English model |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``gpt2-medium`` | | 24-layer, 1024-hidden, 16-heads, 345M parameters. |
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| | | | OpenAI's Medium-sized GPT-2 English model |
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+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| Transformer-XL | ``transfo-xl-wt103`` | | 18-layer, 1024-hidden, 16-heads, 257M parameters. |
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| | | | English model trained on wikitext-103 |
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+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| XLNet | ``xlnet-base-cased`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | XLNet English model |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``xlnet-large-cased`` | | 24-layer, 1024-hidden, 16-heads, 340M parameters. |
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| | | | XLNet Large English model |
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+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| XLM | ``xlm-mlm-en-2048`` | | 12-layer, 1024-hidden, 8-heads |
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| | | | XLM English model |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``xlm-mlm-ende-1024`` | | 12-layer, 1024-hidden, 8-heads |
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| | | | XLM English-German Multi-language model |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``xlm-mlm-enfr-1024`` | | 12-layer, 1024-hidden, 8-heads |
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| | | | XLM English-French Multi-language model |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``xlm-mlm-enro-1024`` | | 12-layer, 1024-hidden, 8-heads |
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| | | | XLM English-Romanian Multi-language model |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``xlm-mlm-xnli15-1024`` | | 12-layer, 1024-hidden, 8-heads |
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| | | | XLM Model pre-trained with MLM on the `15 XNLI languages <https://github.com/facebookresearch/XNLI>`__. |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``xlm-mlm-tlm-xnli15-1024`` | | 12-layer, 1024-hidden, 8-heads |
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| | | | XLM Model pre-trained with MLM + TLM on the `15 XNLI languages <https://github.com/facebookresearch/XNLI>`__. |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``xlm-clm-enfr-1024`` | | 12-layer, 1024-hidden, 8-heads |
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| | | | XLM English model trained with CLM (Causal Language Modeling) |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``xlm-clm-ende-1024`` | | 12-layer, 1024-hidden, 8-heads |
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| | | | XLM English-German Multi-language model trained with CLM (Causal Language Modeling) |
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+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| RoBERTa | ``roberta-base`` | | 12-layer, 768-hidden, 12-heads, 125M parameters |
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| | | | RoBERTa using the BERT-base architecture |
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| | | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__) |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``roberta-large`` | | 24-layer, 1024-hidden, 16-heads, 355M parameters |
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| | | | RoBERTa using the BERT-large architecture |
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| | | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__) |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``roberta-large-mnli`` | | 24-layer, 1024-hidden, 16-heads, 355M parameters |
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| | | | ``roberta-large`` fine-tuned on `MNLI <http://www.nyu.edu/projects/bowman/multinli/>`__. |
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| | | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__) |
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+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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.. <https://huggingface.co/pytorch-transformers/examples.html>`__
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@ -29,6 +29,8 @@ from pytorch_transformers.modeling_bert import (BertConfig, BertEmbeddings,
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BertLayerNorm, BertModel,
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BertPreTrainedModel, gelu)
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from pytorch_transformers.modeling_utils import add_start_docstrings
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logger = logging.getLogger(__name__)
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ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP = {
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@ -65,11 +67,93 @@ class RobertaEmbeddings(BertEmbeddings):
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class RobertaConfig(BertConfig):
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pretrained_config_archive_map = ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP
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ROBERTA_START_DOCSTRING = r""" The RoBERTa model was proposed in
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`RoBERTa: A Robustly Optimized BERT Pretraining Approach`_
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by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer,
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Veselin Stoyanov. It is based on Google's BERT model released in 2018.
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It builds on BERT and modifies key hyperparameters, removing the next-sentence pretraining
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objective and training with much larger mini-batches and learning rates.
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This implementation is the same as BertModel with a tiny embeddings tweak as well as a setup for Roberta pretrained
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models.
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This model is a PyTorch `torch.nn.Module`_ sub-class. Use it as a regular PyTorch Module and
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refer to the PyTorch documentation for all matter related to general usage and behavior.
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.. _`RoBERTa: A Robustly Optimized BERT Pretraining Approach`:
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https://arxiv.org/abs/1907.11692
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.. _`torch.nn.Module`:
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https://pytorch.org/docs/stable/nn.html#module
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Parameters:
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config (:class:`~pytorch_transformers.RobertaConfig`): Model configuration class with all the parameters of the
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model.
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"""
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ROBERTA_INPUTS_DOCSTRING = r"""
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Inputs:
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**input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
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Indices of input sequence tokens in the vocabulary.
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To match pre-training, RoBERTa input sequence should be formatted with [CLS] and [SEP] tokens as follows:
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(a) For sequence pairs:
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``tokens: [CLS] is this jack ##son ##ville ? [SEP][SEP] no it is not . [SEP]``
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(b) For single sequences:
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``tokens: [CLS] the dog is hairy . [SEP]``
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Fully encoded sequences or sequence pairs can be obtained using the RobertaTokenizer.encode function with
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the ``add_special_tokens`` parameter set to ``True``.
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See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
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:func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
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**position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
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Indices of positions of each input sequence tokens in the position embeddings.
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Selected in the range ``[0, config.max_position_embeddings - 1[``.
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**attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
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Mask to avoid performing attention on padding token indices.
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Mask values selected in ``[0, 1]``:
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``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
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**head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
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Mask to nullify selected heads of the self-attention modules.
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Mask values selected in ``[0, 1]``:
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``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
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"""
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@add_start_docstrings("The bare RoBERTa Model transformer outputing raw hidden-states without any specific head on top.",
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ROBERTA_START_DOCSTRING, ROBERTA_INPUTS_DOCSTRING)
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class RobertaModel(BertModel):
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"""
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Same as BertModel with:
|
||||
- a tiny embeddings tweak.
|
||||
- setup for Roberta pretrained models
|
||||
r"""
|
||||
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
|
||||
**last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
|
||||
Sequence of hidden-states at the output of the last layer of the model.
|
||||
**pooler_output**: ``torch.FloatTensor`` of shape ``(batch_size, hidden_size)``
|
||||
Last layer hidden-state of the first token of the sequence (classification token)
|
||||
further processed by a Linear layer and a Tanh activation function. The Linear
|
||||
layer weights are trained from the next sentence prediction (classification)
|
||||
objective during Bert pretraining. This output is usually *not* a good summary
|
||||
of the semantic content of the input, you're often better with averaging or pooling
|
||||
the sequence of hidden-states for the whole input sequence.
|
||||
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
|
||||
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
|
||||
of shape ``(batch_size, sequence_length, hidden_size)``:
|
||||
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
|
||||
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
|
||||
list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
|
||||
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
|
||||
|
||||
Examples::
|
||||
|
||||
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
|
||||
model = RobertaModel.from_pretrained('roberta-base')
|
||||
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
|
||||
outputs = model(input_ids)
|
||||
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
|
||||
|
||||
"""
|
||||
config_class = RobertaConfig
|
||||
pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
|
||||
|
|
@ -82,9 +166,37 @@ class RobertaModel(BertModel):
|
|||
self.apply(self.init_weights)
|
||||
|
||||
|
||||
@add_start_docstrings("""RoBERTa Model with a `language modeling` head on top. """,
|
||||
ROBERTA_START_DOCSTRING, ROBERTA_INPUTS_DOCSTRING)
|
||||
class RobertaForMaskedLM(BertPreTrainedModel):
|
||||
"""
|
||||
Roberta Model with a `language modeling` head on top.
|
||||
r"""
|
||||
**masked_lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
|
||||
Labels for computing the masked language modeling loss.
|
||||
Indices should be in ``[-1, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
|
||||
Tokens with indices set to ``-1`` are ignored (masked), the loss is only computed for the tokens with labels
|
||||
in ``[0, ..., config.vocab_size]``
|
||||
|
||||
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
|
||||
**loss**: (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
|
||||
Masked language modeling loss.
|
||||
**prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
|
||||
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
|
||||
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
|
||||
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
|
||||
of shape ``(batch_size, sequence_length, hidden_size)``:
|
||||
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
|
||||
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
|
||||
list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
|
||||
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
|
||||
|
||||
Examples::
|
||||
|
||||
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
|
||||
model = RobertaForMaskedLM.from_pretrained('roberta-base')
|
||||
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
|
||||
outputs = model(input_ids, masked_lm_labels=input_ids)
|
||||
loss, prediction_scores = outputs[:2]
|
||||
|
||||
"""
|
||||
config_class = RobertaConfig
|
||||
pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
|
||||
|
|
@ -112,14 +224,14 @@ class RobertaForMaskedLM(BertPreTrainedModel):
|
|||
sequence_output = outputs[0]
|
||||
prediction_scores = self.lm_head(sequence_output)
|
||||
|
||||
outputs = (prediction_scores,) + outputs[2:]
|
||||
outputs = (prediction_scores,) + outputs[2:] # Add hidden states and attention if they are here
|
||||
|
||||
if masked_lm_labels is not None:
|
||||
loss_fct = CrossEntropyLoss(ignore_index=-1)
|
||||
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1))
|
||||
outputs = (masked_lm_loss,) + outputs
|
||||
|
||||
return outputs
|
||||
return outputs # (masked_lm_loss), prediction_scores, (hidden_states), (attentions)
|
||||
|
||||
|
||||
class RobertaLMHead(nn.Module):
|
||||
|
|
@ -144,9 +256,39 @@ class RobertaLMHead(nn.Module):
|
|||
return x
|
||||
|
||||
|
||||
@add_start_docstrings("""RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer
|
||||
on top of the pooled output) e.g. for GLUE tasks. """,
|
||||
ROBERTA_START_DOCSTRING, ROBERTA_INPUTS_DOCSTRING)
|
||||
class RobertaForSequenceClassification(BertPreTrainedModel):
|
||||
"""
|
||||
Roberta Model with a classifier head on top.
|
||||
r"""
|
||||
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
|
||||
Labels for computing the sequence classification/regression loss.
|
||||
Indices should be in ``[0, ..., config.num_labels]``.
|
||||
If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss),
|
||||
If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy).
|
||||
|
||||
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
|
||||
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
|
||||
Classification (or regression if config.num_labels==1) loss.
|
||||
**logits**: ``torch.FloatTensor`` of shape ``(batch_size, config.num_labels)``
|
||||
Classification (or regression if config.num_labels==1) scores (before SoftMax).
|
||||
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
|
||||
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
|
||||
of shape ``(batch_size, sequence_length, hidden_size)``:
|
||||
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
|
||||
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
|
||||
list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
|
||||
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
|
||||
|
||||
Examples::
|
||||
|
||||
tokenizer = RoertaTokenizer.from_pretrained('roberta-base')
|
||||
model = RobertaForSequenceClassification.from_pretrained('roberta-base')
|
||||
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
|
||||
labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
|
||||
outputs = model(input_ids, labels=labels)
|
||||
loss, logits = outputs[:2]
|
||||
|
||||
"""
|
||||
config_class = RobertaConfig
|
||||
pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
|
||||
|
|
|
|||
|
|
@ -65,8 +65,7 @@ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
|
|||
|
||||
class RobertaTokenizer(PreTrainedTokenizer):
|
||||
"""
|
||||
RoBERTa BPE tokenizer, derived from the GPT-2 tokenizer. Peculiarities:
|
||||
- Byte-level BPE
|
||||
RoBERTa BPE tokenizer, derived from the GPT-2 tokenizer. Peculiarities: Byte-level BPE
|
||||
"""
|
||||
vocab_files_names = VOCAB_FILES_NAMES
|
||||
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
|
||||
|
|
|
|||
|
|
@ -180,9 +180,10 @@ class PreTrainedTokenizer(object):
|
|||
|
||||
@classmethod
|
||||
def from_pretrained(cls, *inputs, **kwargs):
|
||||
r""" Instantiate a :class:`~pytorch_transformers.PreTrainedTokenizer` (or a derived class) from a predefined tokenizer.
|
||||
r"""
|
||||
Instantiate a :class:`~pytorch_transformers.PreTrainedTokenizer` (or a derived class) from a predefined tokenizer.
|
||||
|
||||
Parameters:
|
||||
Args:
|
||||
pretrained_model_name_or_path: either:
|
||||
|
||||
- a string with the `shortcut name` of a predefined tokenizer to load from cache or download, e.g.: ``bert-base-uncased``.
|
||||
|
|
@ -383,14 +384,15 @@ class PreTrainedTokenizer(object):
|
|||
|
||||
|
||||
def add_tokens(self, new_tokens):
|
||||
""" Add a list of new tokens to the tokenizer class. If the new tokens are not in the
|
||||
"""
|
||||
Add a list of new tokens to the tokenizer class. If the new tokens are not in the
|
||||
vocabulary, they are added to it with indices starting from length of the current vocabulary.
|
||||
|
||||
Parameters:
|
||||
new_tokens: list of string. Each string is a token to add. Tokens are only added if they are not already in the vocabulary (tested by checking if the tokenizer assign the index of the ``unk_token`` to them).
|
||||
Args:
|
||||
new_tokens: list of string. Each string is a token to add. Tokens are only added if they are not already in the vocabulary (tested by checking if the tokenizer assign the index of the ``unk_token`` to them).
|
||||
|
||||
Returns:
|
||||
Number of tokens added to the vocabulary.
|
||||
Returns:
|
||||
Number of tokens added to the vocabulary.
|
||||
|
||||
Examples::
|
||||
|
||||
|
|
@ -422,17 +424,20 @@ class PreTrainedTokenizer(object):
|
|||
|
||||
|
||||
def add_special_tokens(self, special_tokens_dict):
|
||||
""" Add a dictionary of special tokens (eos, pad, cls...) to the encoder and link them
|
||||
to class attributes. If special tokens are NOT in the vocabulary, they are added
|
||||
to it (indexed starting from the last index of the current vocabulary).
|
||||
"""
|
||||
Add a dictionary of special tokens (eos, pad, cls...) to the encoder and link them
|
||||
to class attributes. If special tokens are NOT in the vocabulary, they are added
|
||||
to it (indexed starting from the last index of the current vocabulary).
|
||||
|
||||
Parameters:
|
||||
special_tokens_dict: dict of string. Keys should be in the list of predefined special attributes: [``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``, ``mask_token``, ``additional_special_tokens``].
|
||||
|
||||
Tokens are only added if they are not already in the vocabulary (tested by checking if the tokenizer assign the index of the ``unk_token`` to them).
|
||||
Args:
|
||||
special_tokens_dict: dict of string. Keys should be in the list of predefined special attributes:
|
||||
[``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``, ``mask_token``,
|
||||
``additional_special_tokens``].
|
||||
|
||||
Returns:
|
||||
Number of tokens added to the vocabulary.
|
||||
Tokens are only added if they are not already in the vocabulary (tested by checking if the tokenizer assign the index of the ``unk_token`` to them).
|
||||
|
||||
Returns:
|
||||
Number of tokens added to the vocabulary.
|
||||
|
||||
Examples::
|
||||
|
||||
|
|
@ -520,9 +525,16 @@ class PreTrainedTokenizer(object):
|
|||
raise NotImplementedError
|
||||
|
||||
def encode(self, text, text_pair=None, add_special_tokens=False):
|
||||
""" Converts a string in a sequence of ids (integer), using the tokenizer and vocabulary.
|
||||
"""
|
||||
Converts a string in a sequence of ids (integer), using the tokenizer and vocabulary.
|
||||
|
||||
Same doing ``self.convert_tokens_to_ids(self.tokenize(text))``.
|
||||
Same as doing ``self.convert_tokens_to_ids(self.tokenize(text))``.
|
||||
|
||||
Args:
|
||||
text: The first sequence to be encoded.
|
||||
text_pair: Optional second sequence to be encoded.
|
||||
add_special_tokens: if set to ``True``, the sequences will be encoded with the special tokens relative
|
||||
to their model.
|
||||
"""
|
||||
if text_pair is None:
|
||||
if add_special_tokens:
|
||||
|
|
@ -577,9 +589,9 @@ class PreTrainedTokenizer(object):
|
|||
return ' '.join(self.convert_ids_to_tokens(tokens))
|
||||
|
||||
def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
|
||||
""" Converts a sequence of ids (integer) in a string, using the tokenizer and vocabulary
|
||||
with options to remove special tokens and clean up tokenization spaces.
|
||||
|
||||
"""
|
||||
Converts a sequence of ids (integer) in a string, using the tokenizer and vocabulary
|
||||
with options to remove special tokens and clean up tokenization spaces.
|
||||
Similar to doing ``self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))``.
|
||||
"""
|
||||
filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens)
|
||||
|
|
|
|||
Loading…
Reference in a new issue