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827d6d6ef0
* First pass on utility classes and python tokenizers * finishing cleanup pass * style and quality * Fix tests * Updating following @mfuntowicz comment * style and quality * Fix Roberta * fix batch_size/seq_length inBatchEncoding * add alignement methods + tests * Fix OpenAI and Transfo-XL tokenizers * adding trim_offsets=True default for GPT2 et RoBERTa * style and quality * fix tests * add_prefix_space in roberta * bump up tokenizers to rc7 * style * unfortunately tensorfow does like these - removing shape/seq_len for now * Update src/transformers/tokenization_utils.py Co-Authored-By: Stefan Schweter <stefan@schweter.it> * Adding doc and docstrings * making flake8 happy Co-authored-by: Stefan Schweter <stefan@schweter.it>
109 lines
3.6 KiB
ReStructuredText
109 lines
3.6 KiB
ReStructuredText
RoBERTa
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The RoBERTa model was proposed in `RoBERTa: A Robustly Optimized BERT Pretraining Approach <https://arxiv.org/abs/1907.11692>`_
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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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The abstract from the paper is the following:
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*Language model pretraining has led to significant performance gains but careful comparison between different
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approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes,
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and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication
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study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and
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training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of
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every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These
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results highlight the importance of previously overlooked design choices, and raise questions about the source
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of recently reported improvements. We release our models and code.*
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Tips:
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- This implementation is the same as :class:`~transformers.BertModel` with a tiny embeddings tweak as well as a
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setup for Roberta pretrained models.
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- RoBERTa has the same architecture as BERT, but uses a byte-level BPE as a tokenizer (same as GPT-2) and uses a
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different pre-training scheme.
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- RoBERTa doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just separate your segments with the separation token `tokenizer.sep_token` (or `</s>`)
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- `Camembert <./camembert.html>`__ is a wrapper around RoBERTa. Refer to this page for usage examples.
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The original code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`_.
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RobertaConfig
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~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.RobertaConfig
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:members:
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RobertaTokenizer
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~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.RobertaTokenizer
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:members: build_inputs_with_special_tokens, get_special_tokens_mask,
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create_token_type_ids_from_sequences, save_vocabulary
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RobertaTokenizerFast
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~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.RobertaTokenizerFast
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:members: build_inputs_with_special_tokens
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RobertaModel
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~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.RobertaModel
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:members:
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RobertaForMaskedLM
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~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.RobertaForMaskedLM
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:members:
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RobertaForSequenceClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.RobertaForSequenceClassification
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:members:
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RobertaForTokenClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.RobertaForTokenClassification
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:members:
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TFRobertaModel
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~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFRobertaModel
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:members:
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TFRobertaForMaskedLM
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~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFRobertaForMaskedLM
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:members:
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TFRobertaForSequenceClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFRobertaForSequenceClassification
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:members:
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TFRobertaForTokenClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFRobertaForTokenClassification
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:members:
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