mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-06 14:20:04 +06:00
08f534d2da
* Important files
* Styling them all
* Revert "Styling them all"
This reverts commit 7d029395fd.
* Syling them for realsies
* Fix syntax error
* Fix benchmark_utils
* More fixes
* Fix modeling auto and script
* Remove new line
* Fixes
* More fixes
* Fix more files
* Style
* Add FSMT
* More fixes
* More fixes
* More fixes
* More fixes
* Fixes
* More fixes
* More fixes
* Last fixes
* Make sphinx happy
142 lines
5.9 KiB
ReStructuredText
142 lines
5.9 KiB
ReStructuredText
XLM-RoBERTa
|
|
-----------------------------------------------------------------------------------------------------------------------
|
|
|
|
Overview
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
The XLM-RoBERTa model was proposed in `Unsupervised Cross-lingual Representation Learning at Scale
|
|
<https://arxiv.org/abs/1911.02116>`__ by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume
|
|
Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's
|
|
RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl
|
|
data.
|
|
|
|
The abstract from the paper is the following:
|
|
|
|
*This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a
|
|
wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred
|
|
languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly
|
|
outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +13.8% average accuracy on
|
|
XNLI, +12.3% average F1 score on MLQA, and +2.1% average F1 score on NER. XLM-R performs particularly well on
|
|
low-resource languages, improving 11.8% in XNLI accuracy for Swahili and 9.2% for Urdu over the previous XLM model. We
|
|
also present a detailed empirical evaluation of the key factors that are required to achieve these gains, including the
|
|
trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource
|
|
languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing
|
|
per-language performance; XLM-Ris very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We
|
|
will make XLM-R code, data, and models publicly available.*
|
|
|
|
Tips:
|
|
|
|
- XLM-RoBERTa is a multilingual model trained on 100 different languages. Unlike some XLM multilingual models, it does
|
|
not require :obj:`lang` tensors to understand which language is used, and should be able to determine the correct
|
|
language from the input ids.
|
|
- This implementation is the same as RoBERTa. Refer to the :doc:`documentation of RoBERTa <roberta>` for usage examples
|
|
as well as the information relative to the inputs and outputs.
|
|
|
|
The original code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/xlmr>`__.
|
|
|
|
|
|
XLMRobertaConfig
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.XLMRobertaConfig
|
|
:members:
|
|
|
|
|
|
XLMRobertaTokenizer
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.XLMRobertaTokenizer
|
|
:members: build_inputs_with_special_tokens, get_special_tokens_mask,
|
|
create_token_type_ids_from_sequences, save_vocabulary
|
|
|
|
|
|
XLMRobertaModel
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.XLMRobertaModel
|
|
:members: forward
|
|
|
|
|
|
XLMRobertaForCausalLM
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.XLMRobertaForCausalLM
|
|
:members: forward
|
|
|
|
|
|
XLMRobertaForMaskedLM
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.XLMRobertaForMaskedLM
|
|
:members: forward
|
|
|
|
|
|
XLMRobertaForSequenceClassification
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.XLMRobertaForSequenceClassification
|
|
:members: forward
|
|
|
|
|
|
XLMRobertaForMultipleChoice
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.XLMRobertaForMultipleChoice
|
|
:members: forward
|
|
|
|
|
|
XLMRobertaForTokenClassification
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.XLMRobertaForTokenClassification
|
|
:members: forward
|
|
|
|
|
|
XLMRobertaForQuestionAnswering
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.XLMRobertaForQuestionAnswering
|
|
:members: forward
|
|
|
|
|
|
TFXLMRobertaModel
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.TFXLMRobertaModel
|
|
:members: call
|
|
|
|
|
|
TFXLMRobertaForMaskedLM
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.TFXLMRobertaForMaskedLM
|
|
:members: call
|
|
|
|
|
|
TFXLMRobertaForSequenceClassification
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.TFXLMRobertaForSequenceClassification
|
|
:members: call
|
|
|
|
|
|
TFXLMRobertaForMultipleChoice
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.TFXLMRobertaForMultipleChoice
|
|
:members: call
|
|
|
|
|
|
TFXLMRobertaForTokenClassification
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.TFXLMRobertaForTokenClassification
|
|
:members: call
|
|
|
|
|
|
TFXLMRobertaForQuestionAnswering
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.TFXLMRobertaForQuestionAnswering
|
|
:members: call
|