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* Clean up model documentation * Formatting * Preparation work * Long lines * Main work on rst files * Cleanup all config files * Syntax fix * Clean all tokenizers * Work on first models * Models beginning * FaluBERT * All PyTorch models * All models * Long lines again * Fixes * More fixes * Update docs/source/model_doc/bert.rst Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Update docs/source/model_doc/electra.rst Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Last fixes Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
142 lines
5.9 KiB
ReStructuredText
142 lines
5.9 KiB
ReStructuredText
XLM-RoBERTa
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The XLM-RoBERTa model was proposed in `Unsupervised Cross-lingual Representation Learning at Scale
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<https://arxiv.org/abs/1911.02116>`__ by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume
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Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's
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RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl
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data.
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The abstract from the paper is the following:
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*This paper shows that pretraining multilingual language models at scale leads to significant performance gains for
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a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred
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languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly
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outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +13.8% average accuracy
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on XNLI, +12.3% average F1 score on MLQA, and +2.1% average F1 score on NER. XLM-R performs particularly well on
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low-resource languages, improving 11.8% in XNLI accuracy for Swahili and 9.2% for Urdu over the previous XLM model.
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We also present a detailed empirical evaluation of the key factors that are required to achieve these gains,
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including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and
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low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling
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without sacrificing per-language performance; XLM-Ris very competitive with strong monolingual models on the GLUE
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and XNLI benchmarks. We will make XLM-R code, data, and models publicly available.*
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Tips:
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- XLM-RoBERTa is a multilingual model trained on 100 different languages. Unlike some XLM multilingual models, it does
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not require :obj:`lang` tensors to understand which language is used, and should be able to determine the correct
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language from the input ids.
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- This implementation is the same as RoBERTa. Refer to the :doc:`documentation of RoBERTa <roberta>` for usage
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examples as well as the information relative to the inputs and outputs.
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The original code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/xlmr>`__.
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XLMRobertaConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.XLMRobertaConfig
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:members:
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XLMRobertaTokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.XLMRobertaTokenizer
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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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XLMRobertaModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.XLMRobertaModel
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:members: forward
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XLMRobertaForCausalLM
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.XLMRobertaForCausalLM
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:members: forward
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XLMRobertaForMaskedLM
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.XLMRobertaForMaskedLM
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:members: forward
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XLMRobertaForSequenceClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.XLMRobertaForSequenceClassification
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:members: forward
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XLMRobertaForMultipleChoice
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.XLMRobertaForMultipleChoice
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:members: forward
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XLMRobertaForTokenClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.XLMRobertaForTokenClassification
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:members: forward
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XLMRobertaForQuestionAnswering
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.XLMRobertaForQuestionAnswering
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:members: forward
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TFXLMRobertaModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFXLMRobertaModel
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:members: call
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TFXLMRobertaForMaskedLM
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFXLMRobertaForMaskedLM
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:members: call
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TFXLMRobertaForSequenceClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFXLMRobertaForSequenceClassification
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:members: call
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TFXLMRobertaForMultipleChoice
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFXLMRobertaForMultipleChoice
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:members: call
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TFXLMRobertaForTokenClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFXLMRobertaForTokenClassification
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:members: call
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TFXLMRobertaForQuestionAnswering
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFXLMRobertaForQuestionAnswering
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:members: call
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