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# Wav2Vec2
## Overview
The Wav2Vec2 model was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
The abstract from the paper is the following:
*We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on
transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks
the speech input in the latent space and solves a contrastive task defined over a quantization of the latent
representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the
clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state
of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and
pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech
recognition with limited amounts of labeled data.*
Tips:
- Wav2Vec2 is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
- Wav2Vec2 model was trained using connectionist temporal classification (CTC) so the model output has to be decoded
using [`Wav2Vec2CTCTokenizer`].
This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten).
## Wav2Vec2Config
[[autodoc]] Wav2Vec2Config
## Wav2Vec2CTCTokenizer
[[autodoc]] Wav2Vec2CTCTokenizer
- __call__
- save_vocabulary
## Wav2Vec2FeatureExtractor
[[autodoc]] Wav2Vec2FeatureExtractor
- __call__
## Wav2Vec2Processor
[[autodoc]] Wav2Vec2Processor
- __call__
- pad
- from_pretrained
- save_pretrained
- batch_decode
- decode
- as_target_processor
## Wav2Vec2ProcessorWithLM
[[autodoc]] Wav2Vec2ProcessorWithLM
- __call__
- pad
- from_pretrained
- save_pretrained
- batch_decode
- decode
- as_target_processor
## Wav2Vec2 specific outputs
[[autodoc]] models.wav2vec2_with_lm.processing_wav2vec2_with_lm.Wav2Vec2DecoderWithLMOutput
[[autodoc]] models.wav2vec2.modeling_wav2vec2.Wav2Vec2BaseModelOutput
[[autodoc]] models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTrainingOutput
[[autodoc]] models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2BaseModelOutput
[[autodoc]] models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2ForPreTrainingOutput
## Wav2Vec2Model
[[autodoc]] Wav2Vec2Model
- forward
## Wav2Vec2ForCTC
[[autodoc]] Wav2Vec2ForCTC
- forward
## Wav2Vec2ForSequenceClassification
[[autodoc]] Wav2Vec2ForSequenceClassification
- forward
## Wav2Vec2ForAudioFrameClassification
[[autodoc]] Wav2Vec2ForAudioFrameClassification
- forward
## Wav2Vec2ForXVector
[[autodoc]] Wav2Vec2ForXVector
- forward
## Wav2Vec2ForPreTraining
[[autodoc]] Wav2Vec2ForPreTraining
- forward
## TFWav2Vec2Model
[[autodoc]] TFWav2Vec2Model
- call
## TFWav2Vec2ForCTC
[[autodoc]] TFWav2Vec2ForCTC
- call
## FlaxWav2Vec2Model
[[autodoc]] FlaxWav2Vec2Model
- __call__
## FlaxWav2Vec2ForCTC
[[autodoc]] FlaxWav2Vec2ForCTC
- __call__
## FlaxWav2Vec2ForPreTraining
[[autodoc]] FlaxWav2Vec2ForPreTraining
- __call__
docs/source/model_doc/wav2vec2.rst deleted 100644 → 0
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..
Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
Wav2Vec2
-----------------------------------------------------------------------------------------------------------------------
Overview
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The Wav2Vec2 model was proposed in `wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
<https://arxiv.org/abs/2006.11477>`__ by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
The abstract from the paper is the following:
*We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on
transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks
the speech input in the latent space and solves a contrastive task defined over a quantization of the latent
representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the
clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state
of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and
pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech
recognition with limited amounts of labeled data.*
Tips:
- Wav2Vec2 is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
- Wav2Vec2 model was trained using connectionist temporal classification (CTC) so the model output has to be decoded
using :class:`~transformers.Wav2Vec2CTCTokenizer`.
This model was contributed by `patrickvonplaten <https://huggingface.co/patrickvonplaten>`__.
Wav2Vec2Config
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.Wav2Vec2Config
:members:
Wav2Vec2CTCTokenizer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.Wav2Vec2CTCTokenizer
:members: __call__, save_vocabulary
Wav2Vec2FeatureExtractor
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.Wav2Vec2FeatureExtractor
:members: __call__
Wav2Vec2Processor
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.Wav2Vec2Processor
:members: __call__, pad, from_pretrained, save_pretrained, batch_decode, decode, as_target_processor
Wav2Vec2ProcessorWithLM
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.Wav2Vec2ProcessorWithLM
:members: __call__, pad, from_pretrained, save_pretrained, batch_decode, decode, as_target_processor
Wav2Vec2 specific outputs
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.models.wav2vec2_with_lm.processing_wav2vec2_with_lm.Wav2Vec2DecoderWithLMOutput
:members:
.. autoclass:: transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2BaseModelOutput
:members:
.. autoclass:: transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTrainingOutput
:members:
.. autoclass:: transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2BaseModelOutput
:members:
.. autoclass:: transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2ForPreTrainingOutput
:members:
Wav2Vec2Model
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.Wav2Vec2Model
:members: forward
Wav2Vec2ForCTC
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.Wav2Vec2ForCTC
:members: forward
Wav2Vec2ForSequenceClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.Wav2Vec2ForSequenceClassification
:members: forward
Wav2Vec2ForAudioFrameClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.Wav2Vec2ForAudioFrameClassification
:members: forward
Wav2Vec2ForXVector
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.Wav2Vec2ForXVector
:members: forward
Wav2Vec2ForPreTraining
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.Wav2Vec2ForPreTraining
:members: forward
TFWav2Vec2Model
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFWav2Vec2Model
:members: call
TFWav2Vec2ForCTC
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFWav2Vec2ForCTC
:members: call
FlaxWav2Vec2Model
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.FlaxWav2Vec2Model
:members: __call__
FlaxWav2Vec2ForCTC
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.FlaxWav2Vec2ForCTC
:members: __call__
FlaxWav2Vec2ForPreTraining
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.FlaxWav2Vec2ForPreTraining
:members: __call__
docs/source/model_doc/wavlm.rst → docs/source/model_doc/wavlm.mdx
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..
Copyright 2021 The HuggingFace Team. All rights reserved.
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
WavLM
-----------------------------------------------------------------------------------------------------------------------
# WavLM
Overview
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Overview
The WavLM model was proposed in `WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
<https://arxiv.org/abs/2110.13900>`__ by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen,
The WavLM model was proposed in [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen,
Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu,
Michael Zeng, Furu Wei.
......@@ -37,61 +34,46 @@ benchmark, and brings significant improvements for various speech processing tas
Tips:
- WavLM is a speech model that accepts a float array corresponding to the raw waveform of the speech signal. Please use
:class:`~transformers.Wav2Vec2Processor` for the feature extraction.
[`Wav2Vec2Processor`] for the feature extraction.
- WavLM model can be fine-tuned using connectionist temporal classification (CTC) so the model output has to be decoded
using :class:`~transformers.Wav2Vec2CTCTokenizer`.
using [`Wav2Vec2CTCTokenizer`].
- WavLM performs especially well on speaker verification, speaker identification, and speaker diarization tasks.
Relevant checkpoints can be found under https://huggingface.co/models?other=wavlm.
This model was contributed by `patrickvonplaten <https://huggingface.co/patrickvonplaten>`__. The Authors' code can be
found `here <https://github.com/microsoft/unilm/tree/master/wavlm>`__.
This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten). The Authors' code can be
found [here](https://github.com/microsoft/unilm/tree/master/wavlm).
WavLMConfig
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## WavLMConfig
.. autoclass:: transformers.WavLMConfig
:members:
[[autodoc]] WavLMConfig
## WavLM specific outputs
WavLM specific outputs
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
[[autodoc]] models.wavlm.modeling_wavlm.WavLMBaseModelOutput
.. autoclass:: transformers.models.wavlm.modeling_wavlm.WavLMBaseModelOutput
:members:
## WavLMModel
[[autodoc]] WavLMModel
- forward
WavLMModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## WavLMForCTC
.. autoclass:: transformers.WavLMModel
:members: forward
[[autodoc]] WavLMForCTC
- forward
## WavLMForSequenceClassification
WavLMForCTC
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
[[autodoc]] WavLMForSequenceClassification
- forward
.. autoclass:: transformers.WavLMForCTC
:members: forward
## WavLMForAudioFrameClassification
[[autodoc]] WavLMForAudioFrameClassification
- forward
WavLMForSequenceClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## WavLMForXVector
.. autoclass:: transformers.WavLMForSequenceClassification
:members: forward
WavLMForAudioFrameClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.WavLMForAudioFrameClassification
:members: forward
WavLMForXVector
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.WavLMForXVector
:members: forward
[[autodoc]] WavLMForXVector
- forward
docs/source/model_doc/xlm.mdx 0 → 100644
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<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# XLM
## Overview
The XLM model was proposed in [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by
Guillaume Lample, Alexis Conneau. It's a transformer pretrained using one of the following objectives:
- a causal language modeling (CLM) objective (next token prediction),
- a masked language modeling (MLM) objective (BERT-like), or
- a Translation Language Modeling (TLM) object (extension of BERT's MLM to multiple language inputs)
The abstract from the paper is the following:
*Recent studies have demonstrated the efficiency of generative pretraining for English natural language understanding.
In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining. We
propose two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual
data, and one supervised that leverages parallel data with a new cross-lingual language model objective. We obtain
state-of-the-art results on cross-lingual classification, unsupervised and supervised machine translation. On XNLI, our
approach pushes the state of the art by an absolute gain of 4.9% accuracy. On unsupervised machine translation, we
obtain 34.3 BLEU on WMT'16 German-English, improving the previous state of the art by more than 9 BLEU. On supervised
machine translation, we obtain a new state of the art of 38.5 BLEU on WMT'16 Romanian-English, outperforming the
previous best approach by more than 4 BLEU. Our code and pretrained models will be made publicly available.*
Tips:
- XLM has many different checkpoints, which were trained using different objectives: CLM, MLM or TLM. Make sure to
select the correct objective for your task (e.g. MLM checkpoints are not suitable for generation).
- XLM has multilingual checkpoints which leverage a specific `lang` parameter. Check out the [multi-lingual](../multilingual) page for more information.
This model was contributed by [thomwolf](https://huggingface.co/thomwolf). The original code can be found [here](https://github.com/facebookresearch/XLM/).
## XLMConfig
[[autodoc]] XLMConfig
## XLMTokenizer
[[autodoc]] XLMTokenizer
- build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary
## XLM specific outputs
[[autodoc]] models.xlm.modeling_xlm.XLMForQuestionAnsweringOutput
## XLMModel
[[autodoc]] XLMModel
- forward
## XLMWithLMHeadModel
[[autodoc]] XLMWithLMHeadModel
- forward
## XLMForSequenceClassification
[[autodoc]] XLMForSequenceClassification
- forward
## XLMForMultipleChoice
[[autodoc]] XLMForMultipleChoice
- forward
## XLMForTokenClassification
[[autodoc]] XLMForTokenClassification
- forward
## XLMForQuestionAnsweringSimple
[[autodoc]] XLMForQuestionAnsweringSimple
- forward
## XLMForQuestionAnswering
[[autodoc]] XLMForQuestionAnswering
- forward
## TFXLMModel
[[autodoc]] TFXLMModel
- call
## TFXLMWithLMHeadModel
[[autodoc]] TFXLMWithLMHeadModel
- call
## TFXLMForSequenceClassification
[[autodoc]] TFXLMForSequenceClassification
- call
## TFXLMForMultipleChoice
[[autodoc]] TFXLMForMultipleChoice
- call
## TFXLMForTokenClassification
[[autodoc]] TFXLMForTokenClassification
- call
## TFXLMForQuestionAnsweringSimple
[[autodoc]] TFXLMForQuestionAnsweringSimple
- call
docs/source/model_doc/xlm.rst deleted 100644 → 0
View file @ b0c7d2ec
..
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
XLM
-----------------------------------------------------------------------------------------------------------------------
Overview
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The XLM model was proposed in `Cross-lingual Language Model Pretraining <https://arxiv.org/abs/1901.07291>`__ by
Guillaume Lample, Alexis Conneau. It's a transformer pretrained using one of the following objectives:
- a causal language modeling (CLM) objective (next token prediction),
- a masked language modeling (MLM) objective (BERT-like), or
- a Translation Language Modeling (TLM) object (extension of BERT's MLM to multiple language inputs)
The abstract from the paper is the following:
*Recent studies have demonstrated the efficiency of generative pretraining for English natural language understanding.
In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining. We
propose two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual
data, and one supervised that leverages parallel data with a new cross-lingual language model objective. We obtain
state-of-the-art results on cross-lingual classification, unsupervised and supervised machine translation. On XNLI, our
approach pushes the state of the art by an absolute gain of 4.9% accuracy. On unsupervised machine translation, we
obtain 34.3 BLEU on WMT'16 German-English, improving the previous state of the art by more than 9 BLEU. On supervised
machine translation, we obtain a new state of the art of 38.5 BLEU on WMT'16 Romanian-English, outperforming the
previous best approach by more than 4 BLEU. Our code and pretrained models will be made publicly available.*
Tips:
- XLM has many different checkpoints, which were trained using different objectives: CLM, MLM or TLM. Make sure to
select the correct objective for your task (e.g. MLM checkpoints are not suitable for generation).
- XLM has multilingual checkpoints which leverage a specific :obj:`lang` parameter. Check out the :doc:`multi-lingual
<../multilingual>` page for more information.
This model was contributed by `thomwolf <https://huggingface.co/thomwolf>`__. The original code can be found `here
<https://github.com/facebookresearch/XLM/>`__.
XLMConfig
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.XLMConfig
:members:
XLMTokenizer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.XLMTokenizer
:members: build_inputs_with_special_tokens, get_special_tokens_mask,
create_token_type_ids_from_sequences, save_vocabulary
XLM specific outputs
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.models.xlm.modeling_xlm.XLMForQuestionAnsweringOutput
:members:
XLMModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.XLMModel
:members: forward
XLMWithLMHeadModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.XLMWithLMHeadModel
:members: forward
XLMForSequenceClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.XLMForSequenceClassification
:members: forward
XLMForMultipleChoice
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.XLMForMultipleChoice
:members: forward
XLMForTokenClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.XLMForTokenClassification
:members: forward
XLMForQuestionAnsweringSimple
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.XLMForQuestionAnsweringSimple
:members: forward
XLMForQuestionAnswering
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.XLMForQuestionAnswering
:members: forward
TFXLMModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFXLMModel
:members: call
TFXLMWithLMHeadModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFXLMWithLMHeadModel
:members: call
TFXLMForSequenceClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFXLMForSequenceClassification
:members: call
TFXLMForMultipleChoice
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFXLMForMultipleChoice
:members: call
TFXLMForTokenClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFXLMForTokenClassification
:members: call
TFXLMForQuestionAnsweringSimple
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFXLMForQuestionAnsweringSimple
:members: call
docs/source/model_doc/xlmprophetnet.rst → docs/source/model_doc/xlmprophetnet.mdx
View file @ 207594be
..
Copyright 2020 The HuggingFace Team. All rights reserved.
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
XLM-ProphetNet
-----------------------------------------------------------------------------------------------------------------------
# XLM-ProphetNet
**DISCLAIMER:** If you see something strange, file a `Github Issue
<https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
**DISCLAIMER:** If you see something strange, file a [Github Issue](https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title) and assign
@patrickvonplaten
Overview
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Overview
The XLM-ProphetNet model was proposed in `ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training,
<https://arxiv.org/abs/2001.04063>`__ by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei
The XLM-ProphetNet model was proposed in [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training,](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei
Zhang, Ming Zhou on 13 Jan, 2020.
XLM-ProphetNet is an encoder-decoder model and can predict n-future tokens for "ngram" language modeling instead of
......@@ -41,47 +37,32 @@ dataset (160GB) respectively. Then we conduct experiments on CNN/DailyMail, Giga
abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new
state-of-the-art results on all these datasets compared to the models using the same scale pretraining corpus.*
The Authors' code can be found `here <https://github.com/microsoft/ProphetNet>`__.
The Authors' code can be found [here](https://github.com/microsoft/ProphetNet).
XLMProphetNetConfig
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## XLMProphetNetConfig
.. autoclass:: transformers.XLMProphetNetConfig
:members:
[[autodoc]] XLMProphetNetConfig
## XLMProphetNetTokenizer
XLMProphetNetTokenizer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
[[autodoc]] XLMProphetNetTokenizer
.. autoclass:: transformers.XLMProphetNetTokenizer
:members:
## XLMProphetNetModel
[[autodoc]] XLMProphetNetModel
XLMProphetNetModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## XLMProphetNetEncoder
.. autoclass:: transformers.XLMProphetNetModel
[[autodoc]] XLMProphetNetEncoder
## XLMProphetNetDecoder
XLMProphetNetEncoder
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
[[autodoc]] XLMProphetNetDecoder
.. autoclass:: transformers.XLMProphetNetEncoder
## XLMProphetNetForConditionalGeneration
[[autodoc]] XLMProphetNetForConditionalGeneration
XLMProphetNetDecoder
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## XLMProphetNetForCausalLM
.. autoclass:: transformers.XLMProphetNetDecoder
XLMProphetNetForConditionalGeneration
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.XLMProphetNetForConditionalGeneration
XLMProphetNetForCausalLM
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.XLMProphetNetForCausalLM
[[autodoc]] XLMProphetNetForCausalLM
docs/source/model_doc/xlmroberta.mdx 0 → 100644
View file @ 207594be
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# 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 `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 [documentation of RoBERTa](roberta) for usage examples
as well as the information relative to the inputs and outputs.
This model was contributed by [stefan-it](https://huggingface.co/stefan-it). The original code can be found [here](https://github.com/pytorch/fairseq/tree/master/examples/xlmr).
## XLMRobertaConfig
[[autodoc]] XLMRobertaConfig
## XLMRobertaTokenizer
[[autodoc]] XLMRobertaTokenizer
- build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary
## XLMRobertaTokenizerFast
[[autodoc]] XLMRobertaTokenizerFast
## XLMRobertaModel
[[autodoc]] XLMRobertaModel
- forward
## XLMRobertaForCausalLM
[[autodoc]] XLMRobertaForCausalLM
- forward
## XLMRobertaForMaskedLM
[[autodoc]] XLMRobertaForMaskedLM
- forward
## XLMRobertaForSequenceClassification
[[autodoc]] XLMRobertaForSequenceClassification
- forward
## XLMRobertaForMultipleChoice
[[autodoc]] XLMRobertaForMultipleChoice
- forward
## XLMRobertaForTokenClassification
[[autodoc]] XLMRobertaForTokenClassification
- forward
## XLMRobertaForQuestionAnswering
[[autodoc]] XLMRobertaForQuestionAnswering
- forward
## TFXLMRobertaModel
[[autodoc]] TFXLMRobertaModel
- call
## TFXLMRobertaForMaskedLM
[[autodoc]] TFXLMRobertaForMaskedLM
- call
## TFXLMRobertaForSequenceClassification
[[autodoc]] TFXLMRobertaForSequenceClassification
- call
## TFXLMRobertaForMultipleChoice
[[autodoc]] TFXLMRobertaForMultipleChoice
- call
## TFXLMRobertaForTokenClassification
[[autodoc]] TFXLMRobertaForTokenClassification
- call
## TFXLMRobertaForQuestionAnswering
[[autodoc]] TFXLMRobertaForQuestionAnswering
- call
docs/source/model_doc/xlmroberta.rst deleted 100644 → 0
View file @ b0c7d2ec
..
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
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.
This model was contributed by `stefan-it <https://huggingface.co/stefan-it>`__. 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
XLMRobertaTokenizerFast
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.XLMRobertaTokenizerFast
:members:
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
docs/source/model_doc/xlnet.mdx 0 → 100644
View file @ 207594be
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# XLNet
## Overview
The XLNet model was proposed in [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov,
Quoc V. Le. XLnet is an extension of the Transformer-XL model pre-trained using an autoregressive method to learn
bidirectional contexts by maximizing the expected likelihood over all permutations of the input sequence factorization
order.
The abstract from the paper is the following:
*With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves
better performance than pretraining approaches based on autoregressive language modeling. However, relying on
corrupting the input with masks, BERT neglects dependency between the masked positions and suffers from a
pretrain-finetune discrepancy. In light of these pros and cons, we propose XLNet, a generalized autoregressive
pretraining method that (1) enables learning bidirectional contexts by maximizing the expected likelihood over all
permutations of the factorization order and (2) overcomes the limitations of BERT thanks to its autoregressive
formulation. Furthermore, XLNet integrates ideas from Transformer-XL, the state-of-the-art autoregressive model, into
pretraining. Empirically, under comparable experiment settings, XLNet outperforms BERT on 20 tasks, often by a large
margin, including question answering, natural language inference, sentiment analysis, and document ranking.*
Tips:
- The specific attention pattern can be controlled at training and test time using the `perm_mask` input.
- Due to the difficulty of training a fully auto-regressive model over various factorization order, XLNet is pretrained
using only a sub-set of the output tokens as target which are selected with the `target_mapping` input.
- To use XLNet for sequential decoding (i.e. not in fully bi-directional setting), use the `perm_mask` and
`target_mapping` inputs to control the attention span and outputs (see examples in
*examples/pytorch/text-generation/run_generation.py*)
- XLNet is one of the few models that has no sequence length limit.
This model was contributed by [thomwolf](https://huggingface.co/thomwolf). The original code can be found [here](https://github.com/zihangdai/xlnet/).
## XLNetConfig
[[autodoc]] XLNetConfig
## XLNetTokenizer
[[autodoc]] XLNetTokenizer
- build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary
## XLNetTokenizerFast
[[autodoc]] XLNetTokenizerFast
## XLNet specific outputs
[[autodoc]] models.xlnet.modeling_xlnet.XLNetModelOutput
[[autodoc]] models.xlnet.modeling_xlnet.XLNetLMHeadModelOutput
[[autodoc]] models.xlnet.modeling_xlnet.XLNetForSequenceClassificationOutput
[[autodoc]] models.xlnet.modeling_xlnet.XLNetForMultipleChoiceOutput
[[autodoc]] models.xlnet.modeling_xlnet.XLNetForTokenClassificationOutput
[[autodoc]] models.xlnet.modeling_xlnet.XLNetForQuestionAnsweringSimpleOutput
[[autodoc]] models.xlnet.modeling_xlnet.XLNetForQuestionAnsweringOutput
[[autodoc]] models.xlnet.modeling_tf_xlnet.TFXLNetModelOutput
[[autodoc]] models.xlnet.modeling_tf_xlnet.TFXLNetLMHeadModelOutput
[[autodoc]] models.xlnet.modeling_tf_xlnet.TFXLNetForSequenceClassificationOutput
[[autodoc]] models.xlnet.modeling_tf_xlnet.TFXLNetForMultipleChoiceOutput
[[autodoc]] models.xlnet.modeling_tf_xlnet.TFXLNetForTokenClassificationOutput
[[autodoc]] models.xlnet.modeling_tf_xlnet.TFXLNetForQuestionAnsweringSimpleOutput
## XLNetModel
[[autodoc]] XLNetModel
- forward
## XLNetLMHeadModel
[[autodoc]] XLNetLMHeadModel
- forward
## XLNetForSequenceClassification
[[autodoc]] XLNetForSequenceClassification
- forward
## XLNetForMultipleChoice
[[autodoc]] XLNetForMultipleChoice
- forward
## XLNetForTokenClassification
[[autodoc]] XLNetForTokenClassification
- forward
## XLNetForQuestionAnsweringSimple
[[autodoc]] XLNetForQuestionAnsweringSimple
- forward
## XLNetForQuestionAnswering
[[autodoc]] XLNetForQuestionAnswering
- forward
## TFXLNetModel
[[autodoc]] TFXLNetModel
- call
## TFXLNetLMHeadModel
[[autodoc]] TFXLNetLMHeadModel
- call
## TFXLNetForSequenceClassification
[[autodoc]] TFXLNetForSequenceClassification
- call
## TFLNetForMultipleChoice
[[autodoc]] TFXLNetForMultipleChoice
- call
## TFXLNetForTokenClassification
[[autodoc]] TFXLNetForTokenClassification
- call
## TFXLNetForQuestionAnsweringSimple
[[autodoc]] TFXLNetForQuestionAnsweringSimple
- call
docs/source/model_doc/xlnet.rst deleted 100644 → 0
View file @ b0c7d2ec
..
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
XLNet
-----------------------------------------------------------------------------------------------------------------------
Overview
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The XLNet model was proposed in `XLNet: Generalized Autoregressive Pretraining for Language Understanding
<https://arxiv.org/abs/1906.08237>`_ by Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov,
Quoc V. Le. XLnet is an extension of the Transformer-XL model pre-trained using an autoregressive method to learn
bidirectional contexts by maximizing the expected likelihood over all permutations of the input sequence factorization
order.
The abstract from the paper is the following:
*With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves
better performance than pretraining approaches based on autoregressive language modeling. However, relying on
corrupting the input with masks, BERT neglects dependency between the masked positions and suffers from a
pretrain-finetune discrepancy. In light of these pros and cons, we propose XLNet, a generalized autoregressive
pretraining method that (1) enables learning bidirectional contexts by maximizing the expected likelihood over all
permutations of the factorization order and (2) overcomes the limitations of BERT thanks to its autoregressive
formulation. Furthermore, XLNet integrates ideas from Transformer-XL, the state-of-the-art autoregressive model, into
pretraining. Empirically, under comparable experiment settings, XLNet outperforms BERT on 20 tasks, often by a large
margin, including question answering, natural language inference, sentiment analysis, and document ranking.*
Tips:
- The specific attention pattern can be controlled at training and test time using the :obj:`perm_mask` input.
- Due to the difficulty of training a fully auto-regressive model over various factorization order, XLNet is pretrained
using only a sub-set of the output tokens as target which are selected with the :obj:`target_mapping` input.
- To use XLNet for sequential decoding (i.e. not in fully bi-directional setting), use the :obj:`perm_mask` and
:obj:`target_mapping` inputs to control the attention span and outputs (see examples in
`examples/pytorch/text-generation/run_generation.py`)
- XLNet is one of the few models that has no sequence length limit.
This model was contributed by `thomwolf <https://huggingface.co/thomwolf>`__. The original code can be found `here
<https://github.com/zihangdai/xlnet/>`__.
XLNetConfig
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.XLNetConfig
:members:
XLNetTokenizer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.XLNetTokenizer
:members: build_inputs_with_special_tokens, get_special_tokens_mask,
create_token_type_ids_from_sequences, save_vocabulary
XLNetTokenizerFast
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.XLNetTokenizerFast
:members:
XLNet specific outputs
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.models.xlnet.modeling_xlnet.XLNetModelOutput
:members:
.. autoclass:: transformers.models.xlnet.modeling_xlnet.XLNetLMHeadModelOutput
:members:
.. autoclass:: transformers.models.xlnet.modeling_xlnet.XLNetForSequenceClassificationOutput
:members:
.. autoclass:: transformers.models.xlnet.modeling_xlnet.XLNetForMultipleChoiceOutput
:members:
.. autoclass:: transformers.models.xlnet.modeling_xlnet.XLNetForTokenClassificationOutput
:members:
.. autoclass:: transformers.models.xlnet.modeling_xlnet.XLNetForQuestionAnsweringSimpleOutput
:members:
.. autoclass:: transformers.models.xlnet.modeling_xlnet.XLNetForQuestionAnsweringOutput
:members:
.. autoclass:: transformers.models.xlnet.modeling_tf_xlnet.TFXLNetModelOutput
:members:
.. autoclass:: transformers.models.xlnet.modeling_tf_xlnet.TFXLNetLMHeadModelOutput
:members:
.. autoclass:: transformers.models.xlnet.modeling_tf_xlnet.TFXLNetForSequenceClassificationOutput
:members:
.. autoclass:: transformers.models.xlnet.modeling_tf_xlnet.TFXLNetForMultipleChoiceOutput
:members:
.. autoclass:: transformers.models.xlnet.modeling_tf_xlnet.TFXLNetForTokenClassificationOutput
:members:
.. autoclass:: transformers.models.xlnet.modeling_tf_xlnet.TFXLNetForQuestionAnsweringSimpleOutput
:members:
XLNetModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.XLNetModel
:members: forward
XLNetLMHeadModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.XLNetLMHeadModel
:members: forward
XLNetForSequenceClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.XLNetForSequenceClassification
:members: forward
XLNetForMultipleChoice
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.XLNetForMultipleChoice
:members: forward
XLNetForTokenClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.XLNetForTokenClassification
:members: forward
XLNetForQuestionAnsweringSimple
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.XLNetForQuestionAnsweringSimple
:members: forward
XLNetForQuestionAnswering
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.XLNetForQuestionAnswering
:members: forward
TFXLNetModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFXLNetModel
:members: call
TFXLNetLMHeadModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFXLNetLMHeadModel
:members: call
TFXLNetForSequenceClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFXLNetForSequenceClassification
:members: call
TFLNetForMultipleChoice
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFXLNetForMultipleChoice
:members: call
TFXLNetForTokenClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFXLNetForTokenClassification
:members: call
TFXLNetForQuestionAnsweringSimple
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFXLNetForQuestionAnsweringSimple
:members: call
docs/source/model_doc/xls_r.rst → docs/source/model_doc/xls_r.mdx
View file @ 207594be
..
Copyright 2021 The HuggingFace Team. All rights reserved.
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
XLS-R
-----------------------------------------------------------------------------------------------------------------------
# XLS-R
Overview
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Overview
The XLS-R model was proposed in `XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale
<https://arxiv.org/abs/2111.09296>`__ by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman
The XLS-R model was proposed in [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman
Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
The abstract from the paper is the following:
......@@ -37,11 +34,10 @@ Tips:
- XLS-R is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
- XLS-R model was trained using connectionist temporal classification (CTC) so the model output has to be decoded using
:class:`~transformers.Wav2Vec2CTCTokenizer`.
[`Wav2Vec2CTCTokenizer`].
Relevant checkpoints can be found under https://huggingface.co/models?other=xls_r.
XLS-R's architecture is based on the Wav2Vec2 model, so one can refer to :doc:`Wav2Vec2's documentation page
<wav2vec2>`.
XLS-R's architecture is based on the Wav2Vec2 model, so one can refer to [Wav2Vec2's documentation page](wav2vec2).
The original code can be found `here <https://github.com/pytorch/fairseq/tree/master/fairseq/models/wav2vec>`__.
The original code can be found [here](https://github.com/pytorch/fairseq/tree/master/fairseq/models/wav2vec).
docs/source/model_doc/xlsr_wav2vec2.rst → docs/source/model_doc/xlsr_wav2vec2.mdx
View file @ 207594be
..
Copyright 2021 The HuggingFace Team. All rights reserved.
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
XLSR-Wav2Vec2
-----------------------------------------------------------------------------------------------------------------------
# XLSR-Wav2Vec2
Overview
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Overview
The XLSR-Wav2Vec2 model was proposed in `Unsupervised Cross-Lingual Representation Learning For Speech Recognition
<https://arxiv.org/abs/2006.13979>`__ by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael
The XLSR-Wav2Vec2 model was proposed in [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael
Auli.
The abstract from the paper is the following:
......@@ -37,9 +34,8 @@ Tips:
- XLSR-Wav2Vec2 is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
- XLSR-Wav2Vec2 model was trained using connectionist temporal classification (CTC) so the model output has to be
decoded using :class:`~transformers.Wav2Vec2CTCTokenizer`.
decoded using [`Wav2Vec2CTCTokenizer`].
XLSR-Wav2Vec2's architecture is based on the Wav2Vec2 model, so one can refer to :doc:`Wav2Vec2's documentation page
<wav2vec2>`.
XLSR-Wav2Vec2's architecture is based on the Wav2Vec2 model, so one can refer to [Wav2Vec2's documentation page](wav2vec2).
The original code can be found `here <https://github.com/pytorch/fairseq/tree/master/fairseq/models/wav2vec>`__.
The original code can be found [here](https://github.com/pytorch/fairseq/tree/master/fairseq/models/wav2vec).
docs/source/model_sharing.rst → docs/source/model_sharing.mdx
View file @ 207594be
..
Copyright 2020 The HuggingFace Team. All rights reserved.
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
Model sharing and uploading
=======================================================================================================================
# Model sharing and uploading
In this page, we will show you how to share a model you have trained or fine-tuned on new data with the community on
the `model hub <https://huggingface.co/models>`__.
the [model hub](https://huggingface.co/models).
.. raw:: html
<iframe width="560" height="315" src="https://www.youtube.com/embed/XvSGPZFEjDY" title="YouTube video player"
frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
picture-in-picture" allowfullscreen></iframe>
<iframe width="560" height="315" src="https://www.youtube.com/embed/XvSGPZFEjDY" title="YouTube video player"
frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
picture-in-picture" allowfullscreen></iframe>
<Tip>
.. note::
You will need to create an account on [huggingface.co](https://huggingface.co/join) for this.
You will need to create an account on `huggingface.co <https://huggingface.co/join>`__ for this.
Optionally, you can join an existing organization or create a new one.
Optionally, you can join an existing organization or create a new one.
</Tip>
We have seen in the [training tutorial](training): how to fine-tune a model on a given task. You have probably
done something similar on your task, either using the model `fit()` method, directly in your own training loop or using the
[`Trainer`] class. Let's see how you can share the result on the
[model hub](https://huggingface.co/models).
We have seen in the :doc:`training tutorial <training>`: how to fine-tune a model on a given task. You have probably
done something similar on your task, either using the model directly in your own training loop or using the
:class:`~.transformers.Trainer`/:class:`~.transformers.TFTrainer` class. Let's see how you can share the result on the
`model hub <https://huggingface.co/models>`__.
Model versioning
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Model versioning
Since version v3.5.0, the model hub has built-in model versioning based on git and git-lfs. It is based on the paradigm
that one model *is* one repo.
......@@ -51,89 +48,80 @@ branch.
For instance:
.. code-block::
>>> model = AutoModel.from_pretrained(
>>> "julien-c/EsperBERTo-small",
>>> revision="v2.0.1" # tag name, or branch name, or commit hash
>>> )
```python
>>> model = AutoModel.from_pretrained(
>>> "julien-c/EsperBERTo-small",
>>> revision="v2.0.1" # tag name, or branch name, or commit hash
>>> )
```
Push your model from Python
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Push your model from Python
Preparation
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
### Preparation
The first step is to make sure your credentials to the hub are stored somewhere. This can be done in two ways. If you
have access to a terminal, you cam just run the following command in the virtual environment where you installed 🤗
Transformers:
.. code-block:: bash
```bash
huggingface-cli login
```
transformers-cli login
It will store your access token in the Hugging Face cache folder (by default :obj:`~/.cache/`).
It will store your access token in the Hugging Face cache folder (by default `~/.cache/`).
If you don't have an easy access to a terminal (for instance in a Colab session), you can find a token linked to your
account by going on `huggingface.co <https://huggingface.co/>`, click on your avatar on the top left corner, then on
`Edit profile` on the left, just beneath your profile picture. In the submenu `API Tokens`, you will find your API
account by going on [huggingface.co](https://huggingface.co/), click on your avatar on the top left corner, then on
*Edit profile* on the left, just beneath your profile picture. In the submenu *API Tokens*, you will find your API
token that you can just copy.
Directly push your model to the hub
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. raw:: html
### Directly push your model to the hub
<iframe width="560" height="315" src="https://www.youtube.com/embed/Z1-XMy-GNLQ" title="YouTube video player"
frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
picture-in-picture" allowfullscreen></iframe>
<Youtube id="Z1-XMy-GNLQ"/>
Once you have an API token (either stored in the cache or copied and pasted in your notebook), you can directly push a
finetuned model you saved in :obj:`save_directory` by calling:
finetuned model you saved in `save_directory` by calling:
.. code-block:: python
```python
finetuned_model.push_to_hub("my-awesome-model")
```
finetuned_model.push_to_hub("my-awesome-model")
If you have your API token not stored in the cache, you will need to pass it with :obj:`use_auth_token=your_token`.
If you have your API token not stored in the cache, you will need to pass it with `use_auth_token=your_token`.
This is also be the case for all the examples below, so we won't mention it again.
This will create a repository in your namespace name :obj:`my-awesome-model`, so anyone can now run:
.. code-block:: python
from transformers import AutoModel
This will create a repository in your namespace name `my-awesome-model`, so anyone can now run:
model = AutoModel.from_pretrained("your_username/my-awesome-model")
```python
from transformers import AutoModel
Even better, you can combine this push to the hub with the call to :obj:`save_pretrained`:
model = AutoModel.from_pretrained("your_username/my-awesome-model")
```
.. code-block:: python
Even better, you can combine this push to the hub with the call to `save_pretrained`:
finetuned_model.save_pretrained(save_directory, push_to_hub=True, repo_name="my-awesome-model")
```python
finetuned_model.save_pretrained(save_directory, push_to_hub=True, repo_name="my-awesome-model")
```
If you are a premium user and want your model to be private, just add :obj:`private=True` to this call.
If you are a premium user and want your model to be private, just add `private=True` to this call.
If you are a member of an organization and want to push it inside the namespace of the organization instead of yours,
just add :obj:`organization=my_amazing_org`.
just add `organization=my_amazing_org`.
Add new files to your model repo
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
### Add new files to your model repo
Once you have pushed your model to the hub, you might want to add the tokenizer, or a version of your model for another
framework (TensorFlow, PyTorch, Flax). This is super easy to do! Let's begin with the tokenizer. You can add it to the
repo you created before like this
.. code-block:: python
tokenizer.push_to_hub("my-awesome-model")
If you know its URL (it should be :obj:`https://huggingface.co/username/repo_name`), you can also do:
```python
tokenizer.push_to_hub("my-awesome-model")
```
.. code-block:: python
If you know its URL (it should be `https://huggingface.co/username/repo_name`), you can also do:
tokenizer.push_to_hub(repo_url=my_repo_url)
```python
tokenizer.push_to_hub(repo_url=my_repo_url)
```
And that's all there is to it! It's also a very easy way to fix a mistake if one of the files online had a bug.
......@@ -141,121 +129,111 @@ To add a model for another backend, it's also super easy. Let's say you have fin
add the pytorch model files to your model repo, so that anyone in the community can use it. The following allows you to
directly create a PyTorch version of your TensorFlow model:
.. code-block:: python
```python
from transformers import AutoModel
from transformers import AutoModel
model = AutoModel.from_pretrained(save_directory, from_tf=True)
```
model = AutoModel.from_pretrained(save_directory, from_tf=True)
You can also replace :obj:`save_directory` by the identifier of your model (:obj:`username/repo_name`) if you don't
You can also replace `save_directory` by the identifier of your model (`username/repo_name`) if you don't
have a local save of it anymore. Then, just do the same as before:
.. code-block:: python
model.push_to_hub("my-awesome-model")
```python
model.push_to_hub("my-awesome-model")
```
or
.. code-block:: python
model.push_to_hub(repo_url=my_repo_url)
Use your terminal and git
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
```python
model.push_to_hub(repo_url=my_repo_url)
```
.. raw:: html
## Use your terminal and git
<iframe width="560" height="315" src="https://www.youtube.com/embed/rkCly_cbMBk" title="YouTube video player"
frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
picture-in-picture" allowfullscreen></iframe>
<Youtube id="rkCly_cbMBk"/>
Basic steps
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
### Basic steps
In order to upload a model, you'll need to first create a git repo. This repo will live on the model hub, allowing
users to clone it and you (and your organization members) to push to it.
You can create a model repo directly from `the /new page on the website <https://huggingface.co/new>`__.
You can create a model repo directly from [the /new page on the website](https://huggingface.co/new).
Alternatively, you can use the ``transformers-cli``. The next steps describe that process:
Alternatively, you can use the `transformers-cli`. The next steps describe that process:
Go to a terminal and run the following command. It should be in the virtual environment where you installed 🤗
Transformers, since that command :obj:`transformers-cli` comes from the library.
.. code-block:: bash
transformers-cli login
Transformers, since that command `transformers-cli` comes from the library.
```bash
transformers-cli login
```
Once you are logged in with your model hub credentials, you can start building your repositories. To create a repo:
.. code-block:: bash
transformers-cli repo create your-model-name
If you want to create a repo under a specific organization, you should add a `--organization` flag:
```bash
transformers-cli repo create your-model-name
```
.. code-block:: bash
If you want to create a repo under a specific organization, you should add a *--organization* flag:
transformers-cli repo create your-model-name --organization your-org-name
```bash
transformers-cli repo create your-model-name --organization your-org-name
```
This creates a repo on the model hub, which can be cloned.
.. code-block:: bash
```bash
# Make sure you have git-lfs installed
# (https://git-lfs.github.com/)
git lfs install
# Make sure you have git-lfs installed
# (https://git-lfs.github.com/)
git lfs install
git clone https://huggingface.co/username/your-model-name
git clone https://huggingface.co/username/your-model-name
```
When you have your local clone of your repo and lfs installed, you can then add/remove from that clone as you would
with any other git repo.
.. code-block:: bash
# Commit as usual
cd your-model-name
echo "hello" >> README.md
git add . && git commit -m "Update from $USER"
```bash
# Commit as usual
cd your-model-name
echo "hello" >> README.md
git add . && git commit -m "Update from $USER"
```
We are intentionally not wrapping git too much, so that you can go on with the workflow you're used to and the tools
you already know.
The only learning curve you might have compared to regular git is the one for git-lfs. The documentation at
`git-lfs.github.com <https://git-lfs.github.com/>`__ is decent, but we'll work on a tutorial with some tips and tricks
[git-lfs.github.com](https://git-lfs.github.com/) is decent, but we'll work on a tutorial with some tips and tricks
in the coming weeks!
Additionally, if you want to change multiple repos at once, the `change_config.py script
<https://github.com/huggingface/efficient_scripts/blob/main/change_config.py>`__ can probably save you some time.
Additionally, if you want to change multiple repos at once, the [change_config.py script](https://github.com/huggingface/efficient_scripts/blob/main/change_config.py) can probably save you some time.
Make your model work on all frameworks
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
### Make your model work on all frameworks
..
TODO Sylvain: make this automatic during the upload
<!--TODO Sylvain: make this automatic during the upload
-->
You probably have your favorite framework, but so will other users! That's why it's best to upload your model with both
PyTorch `and` TensorFlow checkpoints to make it easier to use (if you skip this step, users will still be able to load
PyTorch *and* TensorFlow checkpoints to make it easier to use (if you skip this step, users will still be able to load
your model in another framework, but it will be slower, as it will have to be converted on the fly). Don't worry, it's
super easy to do (and in a future version, it might all be automatic). You will need to install both PyTorch and
TensorFlow for this step, but you don't need to worry about the GPU, so it should be very easy. Check the `TensorFlow
installation page <https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available>`__ and/or the `PyTorch
installation page <https://pytorch.org/get-started/locally/#start-locally>`__ to see how.
TensorFlow for this step, but you don't need to worry about the GPU, so it should be very easy. Check the [TensorFlow
installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available) and/or the [PyTorch
installation page](https://pytorch.org/get-started/locally/#start-locally) to see how.
First check that your model class exists in the other framework, that is try to import the same model by either adding
or removing TF. For instance, if you trained a :class:`~transformers.DistilBertForSequenceClassification`, try to type
.. code-block::
or removing TF. For instance, if you trained a [`DistilBertForSequenceClassification`], try to type
>>> from transformers import TFDistilBertForSequenceClassification
```python
>>> from transformers import TFDistilBertForSequenceClassification
```
and if you trained a :class:`~transformers.TFDistilBertForSequenceClassification`, try to type
and if you trained a [`TFDistilBertForSequenceClassification`], try to type
.. code-block::
>>> from transformers import DistilBertForSequenceClassification
```python
>>> from transformers import DistilBertForSequenceClassification
```
This will give back an error if your model does not exist in the other framework (something that should be pretty rare
since we're aiming for full parity between the two frameworks). In this case, skip this and go to the next step.
......@@ -263,161 +241,155 @@ since we're aiming for full parity between the two frameworks). In this case, sk
Now, if you trained your model in PyTorch and have to create a TensorFlow version, adapt the following code to your
model class:
.. code-block::
>>> tf_model = TFDistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_pt=True)
>>> tf_model.save_pretrained("path/to/awesome-name-you-picked")
```python
>>> tf_model = TFDistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_pt=True)
>>> tf_model.save_pretrained("path/to/awesome-name-you-picked")
```
and if you trained your model in TensorFlow and have to create a PyTorch version, adapt the following code to your
model class:
.. code-block::
>>> pt_model = DistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_tf=True)
>>> pt_model.save_pretrained("path/to/awesome-name-you-picked")
```python
>>> pt_model = DistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_tf=True)
>>> pt_model.save_pretrained("path/to/awesome-name-you-picked")
```
That's all there is to it!
Check the directory before pushing to the model hub.
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
### Check the directory before pushing to the model hub.
Make sure there are no garbage files in the directory you'll upload. It should only have:
- a `config.json` file, which saves the :doc:`configuration <main_classes/configuration>` of your model ;
- a `pytorch_model.bin` file, which is the PyTorch checkpoint (unless you can't have it for some reason) ;
- a `tf_model.h5` file, which is the TensorFlow checkpoint (unless you can't have it for some reason) ;
- a `special_tokens_map.json`, which is part of your :doc:`tokenizer <main_classes/tokenizer>` save;
- a `tokenizer_config.json`, which is part of your :doc:`tokenizer <main_classes/tokenizer>` save;
- files named `vocab.json`, `vocab.txt`, `merges.txt`, or similar, which contain the vocabulary of your tokenizer, part
of your :doc:`tokenizer <main_classes/tokenizer>` save;
- maybe a `added_tokens.json`, which is part of your :doc:`tokenizer <main_classes/tokenizer>` save.
- a *config.json* file, which saves the [configuration](main_classes/configuration) of your model ;
- a *pytorch_model.bin* file, which is the PyTorch checkpoint (unless you can't have it for some reason) ;
- a *tf_model.h5* file, which is the TensorFlow checkpoint (unless you can't have it for some reason) ;
- a *special_tokens_map.json*, which is part of your [tokenizer](main_classes/tokenizer) save;
- a *tokenizer_config.json*, which is part of your [tokenizer](main_classes/tokenizer) save;
- files named *vocab.json*, *vocab.txt*, *merges.txt*, or similar, which contain the vocabulary of your tokenizer, part
of your [tokenizer](main_classes/tokenizer) save;
- maybe a *added_tokens.json*, which is part of your [tokenizer](main_classes/tokenizer) save.
Other files can safely be deleted.
Uploading your files
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Uploading your files
Once the repo is cloned, you can add the model, configuration and tokenizer files. For instance, saving the model and
tokenizer files:
.. code-block::
>>> model.save_pretrained("path/to/repo/clone/your-model-name")
>>> tokenizer.save_pretrained("path/to/repo/clone/your-model-name")
```python
>>> model.save_pretrained("path/to/repo/clone/your-model-name")
>>> tokenizer.save_pretrained("path/to/repo/clone/your-model-name")
```
Or, if you're using the Trainer API
.. code-block::
>>> trainer.save_model("path/to/awesome-name-you-picked")
>>> tokenizer.save_pretrained("path/to/repo/clone/your-model-name")
```python
>>> trainer.save_model("path/to/awesome-name-you-picked")
>>> tokenizer.save_pretrained("path/to/repo/clone/your-model-name")
```
You can then add these files to the staging environment and verify that they have been correctly staged with the ``git
status`` command:
You can then add these files to the staging environment and verify that they have been correctly staged with the `git status` command:
.. code-block:: bash
git add --all
git status
```bash
git add --all
git status
```
Finally, the files should be committed:
.. code-block:: bash
git commit -m "First version of the your-model-name model and tokenizer."
```bash
git commit -m "First version of the your-model-name model and tokenizer."
```
And pushed to the remote:
.. code-block:: bash
git push
```bash
git push
```
This will upload the folder containing the weights, tokenizer and configuration we have just prepared.
Add a model card
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
### Add a model card
To make sure everyone knows what your model can do, what its limitations, potential bias or ethical considerations are,
please add a README.md model card to your model repo. You can just create it, or there's also a convenient button
titled "Add a README.md" on your model page. A model card documentation can be found `here
<https://huggingface.co/docs/hub/model-repos>`__ (meta-suggestions are welcome). model card template (meta-suggestions
titled "Add a README.md" on your model page. A model card documentation can be found [here](https://huggingface.co/docs/hub/model-repos) (meta-suggestions are welcome). model card template (meta-suggestions
are welcome).
.. note::
<Tip>
Model cards used to live in the 🤗 Transformers repo under *model_cards/*, but for consistency and scalability we
migrated every model card from the repo to its corresponding huggingface.co model repo.
Model cards used to live in the 🤗 Transformers repo under `model_cards/`, but for consistency and scalability we
migrated every model card from the repo to its corresponding huggingface.co model repo.
</Tip>
If your model is fine-tuned from another model coming from the model hub (all 🤗 Transformers pretrained models do),
don't forget to link to its model card so that people can fully trace how your model was built.
Using your model
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
### Using your model
Your model now has a page on huggingface.co/models 🔥
Anyone can load it from code:
.. code-block::
>>> tokenizer = AutoTokenizer.from_pretrained("namespace/awesome-name-you-picked")
>>> model = AutoModel.from_pretrained("namespace/awesome-name-you-picked")
You may specify a revision by using the ``revision`` flag in the ``from_pretrained`` method:
```python
>>> tokenizer = AutoTokenizer.from_pretrained("namespace/awesome-name-you-picked")
>>> model = AutoModel.from_pretrained("namespace/awesome-name-you-picked")
```
.. code-block::
You may specify a revision by using the `revision` flag in the `from_pretrained` method:
>>> tokenizer = AutoTokenizer.from_pretrained(
>>> "julien-c/EsperBERTo-small",
>>> revision="v2.0.1" # tag name, or branch name, or commit hash
>>> )
```python
>>> tokenizer = AutoTokenizer.from_pretrained(
>>> "julien-c/EsperBERTo-small",
>>> revision="v2.0.1" # tag name, or branch name, or commit hash
>>> )
```
Workflow in a Colab notebook
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Workflow in a Colab notebook
If you're in a Colab notebook (or similar) with no direct access to a terminal, here is the workflow you can use to
upload your model. You can execute each one of them in a cell by adding a ! at the beginning.
First you need to install `git-lfs` in the environment used by the notebook:
First you need to install *git-lfs* in the environment used by the notebook:
.. code-block:: bash
```bash
sudo apt-get install git-lfs
```
sudo apt-get install git-lfs
Then you can use either create a repo directly from [huggingface.co](https://huggingface.co/) , or use the
`transformers-cli` to create it:
Then you can use either create a repo directly from `huggingface.co <https://huggingface.co/>`__ , or use the
:obj:`transformers-cli` to create it:
.. code-block:: bash
transformers-cli login
transformers-cli repo create your-model-name
```bash
transformers-cli login
transformers-cli repo create your-model-name
```
Once it's created, you can clone it and configure it (replace username by your username on huggingface.co):
.. code-block:: bash
```bash
git lfs install
git lfs install
git clone https://username:password@huggingface.co/username/your-model-name
# Alternatively if you have a token,
# you can use it instead of your password
git clone https://username:token@huggingface.co/username/your-model-name
git clone https://username:password@huggingface.co/username/your-model-name
# Alternatively if you have a token,
# you can use it instead of your password
git clone https://username:token@huggingface.co/username/your-model-name
cd your-model-name
git config --global user.email "email@example.com"
# Tip: using the same email than for your huggingface.co account will link your commits to your profile
git config --global user.name "Your name"
cd your-model-name
git config --global user.email "email@example.com"
# Tip: using the same email than for your huggingface.co account will link your commits to your profile
git config --global user.name "Your name"
```
Once you've saved your model inside, and your clone is setup with the right remote URL, you can add it and push it with
usual git commands.
.. code-block:: bash
git add .
git commit -m "Initial commit"
git push
```bash
git add .
git commit -m "Initial commit"
git push
```
docs/source/model_summary.rst → docs/source/model_summary.mdx
View file @ 207594be
..
Copyright 2020 The HuggingFace Team. All rights reserved.
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
Summary of the models
=======================================================================================================================
# Summary of the models
This is a summary of the models available in 🤗 Transformers. It assumes you’re familiar with the original `transformer
model <https://arxiv.org/abs/1706.03762>`_. For a gentle introduction check the `annotated transformer
<http://nlp.seas.harvard.edu/2018/04/03/attention.html>`_. Here we focus on the high-level differences between the
models. You can check them more in detail in their respective documentation. Also check out the :doc:`pretrained model
page </pretrained_models>` to see the checkpoints available for each type of model and all `the community models
<https://huggingface.co/models>`_.
This is a summary of the models available in 🤗 Transformers. It assumes you’re familiar with the original [transformer
model](https://arxiv.org/abs/1706.03762). For a gentle introduction check the [annotated transformer](http://nlp.seas.harvard.edu/2018/04/03/attention.html). Here we focus on the high-level differences between the
models. You can check them more in detail in their respective documentation. Also check out [the Model Hub](https://huggingface.co/models) where you can filter the checkpoints by model architecture.
Each one of the models in the library falls into one of the following categories:
* :ref:`autoregressive-models`
* :ref:`autoencoding-models`
* :ref:`seq-to-seq-models`
* :ref:`multimodal-models`
* :ref:`retrieval-based-models`
- [autoregressive-models](#autoregressive-models)
- [autoencoding-models](#autoencoding-models)
- [seq-to-seq-models](#seq-to-seq-models)
- [multimodal-models](#multimodal-models)
- [retrieval-based-models](#retrieval-based-models)
.. raw:: html
<iframe width="560" height="315" src="https://www.youtube.com/embed/H39Z_720T5s" title="YouTube video player"
frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
picture-in-picture" allowfullscreen></iframe>
<iframe width="560" height="315" src="https://www.youtube.com/embed/H39Z_720T5s" title="YouTube video player"
frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
picture-in-picture" allowfullscreen></iframe>
Autoregressive models are pretrained on the classic language modeling task: guess the next token having read all the
previous ones. They correspond to the decoder of the original transformer model, and a mask is used on top of the full
......@@ -58,54 +52,41 @@ example of such a model (only for translation), T5 is an example that can be fin
Multimodal models mix text inputs with other kinds (e.g. images) and are more specific to a given task.
.. _autoregressive-models:
<a id='autoregressive-models'></a>
Decoders or autoregressive models
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
## Decoders or autoregressive models
As mentioned before, these models rely on the decoder part of the original transformer and use an attention mask so
that at each position, the model can only look at the tokens before the attention heads.
.. raw:: html
<iframe width="560" height="315" src="https://www.youtube.com/embed/d_ixlCubqQw" title="YouTube video player"
frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
picture-in-picture" allowfullscreen></iframe>
Original GPT
-----------------------------------------------------------------------------------------------------------------------
<Youtube id="d_ixlCubqQw"/>
.. raw:: html
### Original GPT
<a href="https://huggingface.co/models?filter=openai-gpt">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-openai--gpt-blueviolet">
</a>
<a href="model_doc/gpt">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-openai--gpt-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=openai-gpt">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-openai--gpt-blueviolet">
</a>
<a href="model_doc/gpt">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-openai--gpt-blueviolet">
</a>
`Improving Language Understanding by Generative Pre-Training
<https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf>`_, Alec Radford et al.
[Improving Language Understanding by Generative Pre-Training](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf), Alec Radford et al.
The first autoregressive model based on the transformer architecture, pretrained on the Book Corpus dataset.
The library provides versions of the model for language modeling and multitask language modeling/multiple choice
classification.
GPT-2
-----------------------------------------------------------------------------------------------------------------------
### GPT-2
.. raw:: html
<a href="https://huggingface.co/models?filter=gpt2">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-gpt2-blueviolet">
</a>
<a href="model_doc/gpt2">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-gpt2-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=gpt2">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-gpt2-blueviolet">
</a>
<a href="model_doc/gpt2">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-gpt2-blueviolet">
</a>
`Language Models are Unsupervised Multitask Learners
<https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf>`_,
[Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf),
Alec Radford et al.
A bigger and better version of GPT, pretrained on WebText (web pages from outgoing links in Reddit with 3 karmas or
......@@ -114,19 +95,16 @@ more).
The library provides versions of the model for language modeling and multitask language modeling/multiple choice
classification.
CTRL
-----------------------------------------------------------------------------------------------------------------------
.. raw:: html
### CTRL
<a href="https://huggingface.co/models?filter=ctrl">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-ctrl-blueviolet">
</a>
<a href="model_doc/ctrl">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-ctrl-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=ctrl">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-ctrl-blueviolet">
</a>
<a href="model_doc/ctrl">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-ctrl-blueviolet">
</a>
`CTRL: A Conditional Transformer Language Model for Controllable Generation <https://arxiv.org/abs/1909.05858>`_,
[CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858),
Nitish Shirish Keskar et al.
Same as the GPT model but adds the idea of control codes. Text is generated from a prompt (can be empty) and one (or
......@@ -135,19 +113,16 @@ wikipedia article, a book or a movie review.
The library provides a version of the model for language modeling only.
Transformer-XL
-----------------------------------------------------------------------------------------------------------------------
### Transformer-XL
.. raw:: html
<a href="https://huggingface.co/models?filter=transfo-xl">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-transfo--xl-blueviolet">
</a>
<a href="model_doc/transformerxl">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-transfo--xl-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=transfo-xl">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-transfo--xl-blueviolet">
</a>
<a href="model_doc/transformerxl">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-transfo--xl-blueviolet">
</a>
`Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`_, Zihang
[Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860), Zihang
Dai et al.
Same as a regular GPT model, but introduces a recurrence mechanism for two consecutive segments (similar to a regular
......@@ -164,55 +139,53 @@ adjustments in the way attention scores are computed.
The library provides a version of the model for language modeling only.
.. _reformer:
Reformer
-----------------------------------------------------------------------------------------------------------------------
<a id='reformer'></a>
.. raw:: html
### Reformer
<a href="https://huggingface.co/models?filter=reformer">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-reformer-blueviolet">
</a>
<a href="model_doc/reformer">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-reformer-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=reformer">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-reformer-blueviolet">
</a>
<a href="model_doc/reformer">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-reformer-blueviolet">
</a>
`Reformer: The Efficient Transformer <https://arxiv.org/abs/2001.04451>`_, Nikita Kitaev et al .
[Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451), Nikita Kitaev et al .
An autoregressive transformer model with lots of tricks to reduce memory footprint and compute time. Those tricks
include:
* Use :ref:`Axial position encoding <axial-pos-encoding>` (see below for more details). It’s a mechanism to avoid
having a huge positional encoding matrix (when the sequence length is very big) by factorizing it into smaller
matrices.
* Replace traditional attention by :ref:`LSH (local-sensitive hashing) attention <lsh-attention>` (see below for more
details). It's a technique to avoid computing the full product query-key in the attention layers.
* Avoid storing the intermediate results of each layer by using reversible transformer layers to obtain them during
the backward pass (subtracting the residuals from the input of the next layer gives them back) or recomputing them
for results inside a given layer (less efficient than storing them but saves memory).
* Compute the feedforward operations by chunks and not on the whole batch.
- Use [Axial position encoding](#axial-pos-encoding) (see below for more details). It’s a mechanism to avoid
having a huge positional encoding matrix (when the sequence length is very big) by factorizing it into smaller
matrices.
- Replace traditional attention by [LSH (local-sensitive hashing) attention](#lsh-attention) (see below for more
details). It's a technique to avoid computing the full product query-key in the attention layers.
- Avoid storing the intermediate results of each layer by using reversible transformer layers to obtain them during
the backward pass (subtracting the residuals from the input of the next layer gives them back) or recomputing them
for results inside a given layer (less efficient than storing them but saves memory).
- Compute the feedforward operations by chunks and not on the whole batch.
With those tricks, the model can be fed much larger sentences than traditional transformer autoregressive models.
**Note:** This model could be very well be used in an autoencoding setting, there is no checkpoint for such a
<Tip>
This model could be very well be used in an autoencoding setting, there is no checkpoint for such a
pretraining yet, though.
The library provides a version of the model for language modeling only.
</Tip>
XLNet
-----------------------------------------------------------------------------------------------------------------------
The library provides a version of the model for language modeling only.
.. raw:: html
### XLNet
<a href="https://huggingface.co/models?filter=xlnet">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-xlnet-blueviolet">
</a>
<a href="model_doc/xlnet">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-xlnet-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=xlnet">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-xlnet-blueviolet">
</a>
<a href="model_doc/xlnet">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-xlnet-blueviolet">
</a>
`XLNet: Generalized Autoregressive Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`_, Zhilin
[XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237), Zhilin
Yang et al.
XLNet is not a traditional autoregressive model but uses a training strategy that builds on that. It permutes the
......@@ -225,42 +198,34 @@ XLNet also uses the same recurrence mechanism as Transformer-XL to build long-te
The library provides a version of the model for language modeling, token classification, sentence classification,
multiple choice classification and question answering.
.. _autoencoding-models:
<a id='autoencoding-models'></a>
Encoders or autoencoding models
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
## Encoders or autoencoding models
As mentioned before, these models rely on the encoder part of the original transformer and use no mask so the model can
look at all the tokens in the attention heads. For pretraining, targets are the original sentences and inputs are their
corrupted versions.
.. raw:: html
<iframe width="560" height="315" src="https://www.youtube.com/embed/MUqNwgPjJvQ" title="YouTube video player"
frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
picture-in-picture" allowfullscreen></iframe>
BERT
-----------------------------------------------------------------------------------------------------------------------
<Youtube id="MUqNwgPjJvQ"/>
.. raw:: html
### BERT
<a href="https://huggingface.co/models?filter=bert">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-bert-blueviolet">
</a>
<a href="model_doc/bert">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-bert-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=bert">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-bert-blueviolet">
</a>
<a href="model_doc/bert">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-bert-blueviolet">
</a>
`BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`_,
[BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805),
Jacob Devlin et al.
Corrupts the inputs by using random masking, more precisely, during pretraining, a given percentage of tokens (usually
15%) is masked by:
* a special mask token with probability 0.8
* a random token different from the one masked with probability 0.1
* the same token with probability 0.1
- a special mask token with probability 0.8
- a random token different from the one masked with probability 0.1
- the same token with probability 0.1
The model must predict the original sentence, but has a second objective: inputs are two sentences A and B (with a
separation token in between). With probability 50%, the sentences are consecutive in the corpus, in the remaining 50%
......@@ -269,98 +234,86 @@ they are not related. The model has to predict if the sentences are consecutive
The library provides a version of the model for language modeling (traditional or masked), next sentence prediction,
token classification, sentence classification, multiple choice classification and question answering.
ALBERT
-----------------------------------------------------------------------------------------------------------------------
### ALBERT
.. raw:: html
<a href="https://huggingface.co/models?filter=albert">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-albert-blueviolet">
</a>
<a href="model_doc/albert">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-albert-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=albert">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-albert-blueviolet">
</a>
<a href="model_doc/albert">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-albert-blueviolet">
</a>
`ALBERT: A Lite BERT for Self-supervised Learning of Language Representations <https://arxiv.org/abs/1909.11942>`_,
[ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942),
Zhenzhong Lan et al.
Same as BERT but with a few tweaks:
* Embedding size E is different from hidden size H justified because the embeddings are context independent (one
embedding vector represents one token), whereas hidden states are context dependent (one hidden state represents a
sequence of tokens) so it's more logical to have H >> E. Also, the embedding matrix is large since it's V x E (V
being the vocab size). If E < H, it has less parameters.
* Layers are split in groups that share parameters (to save memory).
* Next sentence prediction is replaced by a sentence ordering prediction: in the inputs, we have two sentences A and
B (that are consecutive) and we either feed A followed by B or B followed by A. The model must predict if they have
been swapped or not.
- Embedding size E is different from hidden size H justified because the embeddings are context independent (one
embedding vector represents one token), whereas hidden states are context dependent (one hidden state represents a
sequence of tokens) so it's more logical to have H >> E. Also, the embedding matrix is large since it's V x E (V
being the vocab size). If E < H, it has less parameters.
- Layers are split in groups that share parameters (to save memory).
- Next sentence prediction is replaced by a sentence ordering prediction: in the inputs, we have two sentences A and
B (that are consecutive) and we either feed A followed by B or B followed by A. The model must predict if they have
been swapped or not.
The library provides a version of the model for masked language modeling, token classification, sentence
classification, multiple choice classification and question answering.
RoBERTa
-----------------------------------------------------------------------------------------------------------------------
.. raw:: html
### RoBERTa
<a href="https://huggingface.co/models?filter=roberta">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-roberta-blueviolet">
</a>
<a href="model_doc/roberta">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-roberta-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=roberta">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-roberta-blueviolet">
</a>
<a href="model_doc/roberta">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-roberta-blueviolet">
</a>
`RoBERTa: A Robustly Optimized BERT Pretraining Approach <https://arxiv.org/abs/1907.11692>`_, Yinhan Liu et al.
[RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692), Yinhan Liu et al.
Same as BERT with better pretraining tricks:
* dynamic masking: tokens are masked differently at each epoch, whereas BERT does it once and for all
* no NSP (next sentence prediction) loss and instead of putting just two sentences together, put a chunk of
contiguous texts together to reach 512 tokens (so the sentences are in an order than may span several documents)
* train with larger batches
* use BPE with bytes as a subunit and not characters (because of unicode characters)
- dynamic masking: tokens are masked differently at each epoch, whereas BERT does it once and for all
- no NSP (next sentence prediction) loss and instead of putting just two sentences together, put a chunk of
contiguous texts together to reach 512 tokens (so the sentences are in an order than may span several documents)
- train with larger batches
- use BPE with bytes as a subunit and not characters (because of unicode characters)
The library provides a version of the model for masked language modeling, token classification, sentence
classification, multiple choice classification and question answering.
DistilBERT
-----------------------------------------------------------------------------------------------------------------------
### DistilBERT
.. raw:: html
<a href="https://huggingface.co/models?filter=distilbert">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-distilbert-blueviolet">
</a>
<a href="model_doc/distilbert">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-distilbert-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=distilbert">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-distilbert-blueviolet">
</a>
<a href="model_doc/distilbert">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-distilbert-blueviolet">
</a>
`DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter <https://arxiv.org/abs/1910.01108>`_,
[DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108),
Victor Sanh et al.
Same as BERT but smaller. Trained by distillation of the pretrained BERT model, meaning it's been trained to predict
the same probabilities as the larger model. The actual objective is a combination of:
* finding the same probabilities as the teacher model
* predicting the masked tokens correctly (but no next-sentence objective)
* a cosine similarity between the hidden states of the student and the teacher model
- finding the same probabilities as the teacher model
- predicting the masked tokens correctly (but no next-sentence objective)
- a cosine similarity between the hidden states of the student and the teacher model
The library provides a version of the model for masked language modeling, token classification, sentence classification
and question answering.
ConvBERT
-----------------------------------------------------------------------------------------------------------------------
.. raw:: html
### ConvBERT
<a href="https://huggingface.co/models?filter=convbert">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-convbert-blueviolet">
</a>
<a href="model_doc/convbert">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-convbert-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=convbert">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-convbert-blueviolet">
</a>
<a href="model_doc/convbert">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-convbert-blueviolet">
</a>
`ConvBERT: Improving BERT with Span-based Dynamic Convolution <https://arxiv.org/abs/2008.02496>`_, Zihang Jiang,
[ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496), Zihang Jiang,
Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
Pre-trained language models like BERT and its variants have recently achieved impressive performance in various natural
......@@ -378,53 +331,47 @@ using less than 1/4 training cost.
The library provides a version of the model for masked language modeling, token classification, sentence classification
and question answering.
XLM
-----------------------------------------------------------------------------------------------------------------------
### XLM
.. raw:: html
<a href="https://huggingface.co/models?filter=xlm">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-xlm-blueviolet">
</a>
<a href="model_doc/xlm">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-xlm-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=xlm">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-xlm-blueviolet">
</a>
<a href="model_doc/xlm">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-xlm-blueviolet">
</a>
`Cross-lingual Language Model Pretraining <https://arxiv.org/abs/1901.07291>`_, Guillaume Lample and Alexis Conneau
[Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291), Guillaume Lample and Alexis Conneau
A transformer model trained on several languages. There are three different type of training for this model and the
library provides checkpoints for all of them:
* Causal language modeling (CLM) which is the traditional autoregressive training (so this model could be in the
previous section as well). One of the languages is selected for each training sample, and the model input is a
sentence of 256 tokens, that may span over several documents in one of those languages.
* Masked language modeling (MLM) which is like RoBERTa. One of the languages is selected for each training sample,
and the model input is a sentence of 256 tokens, that may span over several documents in one of those languages,
with dynamic masking of the tokens.
* A combination of MLM and translation language modeling (TLM). This consists of concatenating a sentence in two
different languages, with random masking. To predict one of the masked tokens, the model can use both, the
surrounding context in language 1 and the context given by language 2.
Checkpoints refer to which method was used for pretraining by having `clm`, `mlm` or `mlm-tlm` in their names. On top
- Causal language modeling (CLM) which is the traditional autoregressive training (so this model could be in the
previous section as well). One of the languages is selected for each training sample, and the model input is a
sentence of 256 tokens, that may span over several documents in one of those languages.
- Masked language modeling (MLM) which is like RoBERTa. One of the languages is selected for each training sample,
and the model input is a sentence of 256 tokens, that may span over several documents in one of those languages,
with dynamic masking of the tokens.
- A combination of MLM and translation language modeling (TLM). This consists of concatenating a sentence in two
different languages, with random masking. To predict one of the masked tokens, the model can use both, the
surrounding context in language 1 and the context given by language 2.
Checkpoints refer to which method was used for pretraining by having *clm*, *mlm* or *mlm-tlm* in their names. On top
of positional embeddings, the model has language embeddings. When training using MLM/CLM, this gives the model an
indication of the language used, and when training using MLM+TLM, an indication of the language used for each part.
The library provides a version of the model for language modeling, token classification, sentence classification and
question answering.
XLM-RoBERTa
-----------------------------------------------------------------------------------------------------------------------
.. raw:: html
### XLM-RoBERTa
<a href="https://huggingface.co/models?filter=xlm-roberta">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-xlm--roberta-blueviolet">
</a>
<a href="model_doc/xlmroberta">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-xlm--roberta-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=xlm-roberta">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-xlm--roberta-blueviolet">
</a>
<a href="model_doc/xlmroberta">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-xlm--roberta-blueviolet">
</a>
`Unsupervised Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`_, Alexis Conneau et
[Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116), Alexis Conneau et
al.
Uses RoBERTa tricks on the XLM approach, but does not use the translation language modeling objective. It only uses
......@@ -434,37 +381,31 @@ masked language modeling on sentences coming from one language. However, the mod
The library provides a version of the model for masked language modeling, token classification, sentence
classification, multiple choice classification and question answering.
FlauBERT
-----------------------------------------------------------------------------------------------------------------------
### FlauBERT
.. raw:: html
<a href="https://huggingface.co/models?filter=flaubert">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-flaubert-blueviolet">
</a>
<a href="model_doc/flaubert">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-flaubert-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=flaubert">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-flaubert-blueviolet">
</a>
<a href="model_doc/flaubert">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-flaubert-blueviolet">
</a>
`FlauBERT: Unsupervised Language Model Pre-training for French <https://arxiv.org/abs/1912.05372>`_, Hang Le et al.
[FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372), Hang Le et al.
Like RoBERTa, without the sentence ordering prediction (so just trained on the MLM objective).
The library provides a version of the model for language modeling and sentence classification.
ELECTRA
-----------------------------------------------------------------------------------------------------------------------
.. raw:: html
### ELECTRA
<a href="https://huggingface.co/models?filter=electra">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-electra-blueviolet">
</a>
<a href="model_doc/electra">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-electra-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=electra">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-electra-blueviolet">
</a>
<a href="model_doc/electra">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-electra-blueviolet">
</a>
`ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators <https://arxiv.org/abs/2003.10555>`_,
[ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://arxiv.org/abs/2003.10555),
Kevin Clark et al.
ELECTRA is a transformer model pretrained with the use of another (small) masked language model. The inputs are
......@@ -476,20 +417,16 @@ traditional GAN setting) then the ELECTRA model is trained for a few steps.
The library provides a version of the model for masked language modeling, token classification and sentence
classification.
Funnel Transformer
-----------------------------------------------------------------------------------------------------------------------
.. raw:: html
### Funnel Transformer
<a href="https://huggingface.co/models?filter=funnel">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-funnel-blueviolet">
</a>
<a href="model_doc/funnel">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-funnel-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=funnel">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-funnel-blueviolet">
</a>
<a href="model_doc/funnel">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-funnel-blueviolet">
</a>
`Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
<https://arxiv.org/abs/2006.03236>`_, Zihang Dai et al.
[Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://arxiv.org/abs/2006.03236), Zihang Dai et al.
Funnel Transformer is a transformer model using pooling, a bit like a ResNet model: layers are grouped in blocks, and
at the beginning of each block (except the first one), the hidden states are pooled among the sequence dimension. This
......@@ -508,98 +445,86 @@ The pretrained models available use the same pretraining objective as ELECTRA.
The library provides a version of the model for masked language modeling, token classification, sentence
classification, multiple choice classification and question answering.
.. _longformer:
<a id='longformer'></a>
Longformer
-----------------------------------------------------------------------------------------------------------------------
### Longformer
.. raw:: html
<a href="https://huggingface.co/models?filter=longformer">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-longformer-blueviolet">
</a>
<a href="model_doc/longformer">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-longformer-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=longformer">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-longformer-blueviolet">
</a>
<a href="model_doc/longformer">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-longformer-blueviolet">
</a>
`Longformer: The Long-Document Transformer <https://arxiv.org/abs/2004.05150>`_, Iz Beltagy et al.
[Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150), Iz Beltagy et al.
A transformer model replacing the attention matrices by sparse matrices to go faster. Often, the local context (e.g.,
what are the two tokens left and right?) is enough to take action for a given token. Some preselected input tokens are
still given global attention, but the attention matrix has way less parameters, resulting in a speed-up. See the
:ref:`local attention section <local-attention>` for more information.
[local attention section](#local-attention) for more information.
It is pretrained the same way a RoBERTa otherwise.
**Note:** This model could be very well be used in an autoregressive setting, there is no checkpoint for such a
<Tip>
This model could be very well be used in an autoregressive setting, there is no checkpoint for such a
pretraining yet, though.
</Tip>
The library provides a version of the model for masked language modeling, token classification, sentence
classification, multiple choice classification and question answering.
.. _seq-to-seq-models:
<a id='seq-to-seq-models'></a>
Sequence-to-sequence models
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
## Sequence-to-sequence models
As mentioned before, these models keep both the encoder and the decoder of the original transformer.
.. raw:: html
<iframe width="560" height="315" src="https://www.youtube.com/embed/0_4KEb08xrE" title="YouTube video player"
frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
picture-in-picture" allowfullscreen></iframe>
<Youtube id="0_4KEb08xrE"/>
BART
-----------------------------------------------------------------------------------------------------------------------
### BART
.. raw:: html
<a href="https://huggingface.co/models?filter=bart">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-bart-blueviolet">
</a>
<a href="model_doc/bart">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-bart-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=bart">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-bart-blueviolet">
</a>
<a href="model_doc/bart">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-bart-blueviolet">
</a>
`BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
<https://arxiv.org/abs/1910.13461>`_, Mike Lewis et al.
[BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461), Mike Lewis et al.
Sequence-to-sequence model with an encoder and a decoder. Encoder is fed a corrupted version of the tokens, decoder is
fed the original tokens (but has a mask to hide the future words like a regular transformers decoder). A composition of
the following transformations are applied on the pretraining tasks for the encoder:
* mask random tokens (like in BERT)
* delete random tokens
* mask a span of k tokens with a single mask token (a span of 0 tokens is an insertion of a mask token)
* permute sentences
* rotate the document to make it start at a specific token
- mask random tokens (like in BERT)
- delete random tokens
- mask a span of k tokens with a single mask token (a span of 0 tokens is an insertion of a mask token)
- permute sentences
- rotate the document to make it start at a specific token
The library provides a version of this model for conditional generation and sequence classification.
Pegasus
-----------------------------------------------------------------------------------------------------------------------
.. raw:: html
### Pegasus
<a href="https://huggingface.co/models?filter=pegasus">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-pegasus-blueviolet">
</a>
<a href="model_doc/pegasus">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-pegasus-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=pegasus">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-pegasus-blueviolet">
</a>
<a href="model_doc/pegasus">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-pegasus-blueviolet">
</a>
`PEGASUS: Pre-training with Extracted Gap-sentences forAbstractive Summarization
<https://arxiv.org/pdf/1912.08777.pdf>`_, Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019.
[PEGASUS: Pre-training with Extracted Gap-sentences forAbstractive Summarization](https://arxiv.org/pdf/1912.08777.pdf), Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019.
Sequence-to-sequence model with the same encoder-decoder model architecture as BART. Pegasus is pre-trained jointly on
two self-supervised objective functions: Masked Language Modeling (MLM) and a novel summarization specific pretraining
objective, called Gap Sentence Generation (GSG).
* MLM: encoder input tokens are randomly replaced by a mask tokens and have to be predicted by the encoder (like in
BERT)
* GSG: whole encoder input sentences are replaced by a second mask token and fed to the decoder, but which has a
causal mask to hide the future words like a regular auto-regressive transformer decoder.
- MLM: encoder input tokens are randomly replaced by a mask tokens and have to be predicted by the encoder (like in
BERT)
- GSG: whole encoder input sentences are replaced by a second mask token and fed to the decoder, but which has a
causal mask to hide the future words like a regular auto-regressive transformer decoder.
In contrast to BART, Pegasus' pretraining task is intentionally similar to summarization: important sentences are
masked and are generated together as one output sequence from the remaining sentences, similar to an extractive
......@@ -608,39 +533,32 @@ summary.
The library provides a version of this model for conditional generation, which should be used for summarization.
MarianMT
-----------------------------------------------------------------------------------------------------------------------
### MarianMT
.. raw:: html
<a href="https://huggingface.co/models?filter=marian">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-marian-blueviolet">
</a>
<a href="model_doc/marian">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-marian-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=marian">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-marian-blueviolet">
</a>
<a href="model_doc/marian">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-marian-blueviolet">
</a>
`Marian: Fast Neural Machine Translation in C++ <https://arxiv.org/abs/1804.00344>`_, Marcin Junczys-Dowmunt et al.
[Marian: Fast Neural Machine Translation in C++](https://arxiv.org/abs/1804.00344), Marcin Junczys-Dowmunt et al.
A framework for translation models, using the same models as BART
The library provides a version of this model for conditional generation.
T5
-----------------------------------------------------------------------------------------------------------------------
.. raw:: html
### T5
<a href="https://huggingface.co/models?filter=t5">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-t5-blueviolet">
</a>
<a href="model_doc/t5">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-t5-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=t5">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-t5-blueviolet">
</a>
<a href="model_doc/t5">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-t5-blueviolet">
</a>
`Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
<https://arxiv.org/abs/1910.10683>`_, Colin Raffel et al.
[Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683), Colin Raffel et al.
Uses the traditional transformer model (with a slight change in the positional embeddings, which are learned at each
layer). To be able to operate on all NLP tasks, it transforms them into text-to-text problems by using specific
......@@ -660,19 +578,16 @@ For instance, if we have the sentence “My dog is very cute .”, and we decide
The library provides a version of this model for conditional generation.
MT5
-----------------------------------------------------------------------------------------------------------------------
### MT5
.. raw:: html
<a href="https://huggingface.co/models?filter=mt5">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-mt5-blueviolet">
</a>
<a href="model_doc/mt5">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-mt5-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=mt5">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-mt5-blueviolet">
</a>
<a href="model_doc/mt5">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-mt5-blueviolet">
</a>
`mT5: A massively multilingual pre-trained text-to-text transformer <https://arxiv.org/abs/2010.11934>`_, Linting Xue
[mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934), Linting Xue
et al.
The model architecture is same as T5. mT5's pretraining objective includes T5's self-supervised training, but not T5's
......@@ -681,19 +596,16 @@ supervised training. mT5 is trained on 101 languages.
The library provides a version of this model for conditional generation.
MBart
-----------------------------------------------------------------------------------------------------------------------
.. raw:: html
### MBart
<a href="https://huggingface.co/models?filter=mbart">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-mbart-blueviolet">
</a>
<a href="model_doc/mbart">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-mbart-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=mbart">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-mbart-blueviolet">
</a>
<a href="model_doc/mbart">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-mbart-blueviolet">
</a>
`Multilingual Denoising Pre-training for Neural Machine Translation <https://arxiv.org/abs/2001.08210>`_ by Yinhan Liu,
[Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu,
Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
The model architecture and pretraining objective is same as BART, but MBart is trained on 25 languages and is intended
......@@ -702,27 +614,24 @@ sequence-to-sequence model by denoising full texts in multiple languages,
The library provides a version of this model for conditional generation.
The `mbart-large-en-ro checkpoint <https://huggingface.co/facebook/mbart-large-en-ro>`_ can be used for english ->
The [mbart-large-en-ro checkpoint](https://huggingface.co/facebook/mbart-large-en-ro) can be used for english ->
romanian translation.
The `mbart-large-cc25 <https://huggingface.co/facebook/mbart-large-cc25>`_ checkpoint can be finetuned for other
The [mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) checkpoint can be finetuned for other
translation and summarization tasks, using code in ```examples/pytorch/translation/``` , but is not very useful without
finetuning.
ProphetNet
-----------------------------------------------------------------------------------------------------------------------
### ProphetNet
.. raw:: html
<a href="https://huggingface.co/models?filter=prophetnet">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-prophetnet-blueviolet">
</a>
<a href="model_doc/prophetnet">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-prophetnet-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=prophetnet">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-prophetnet-blueviolet">
</a>
<a href="model_doc/prophetnet">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-prophetnet-blueviolet">
</a>
`ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training, <https://arxiv.org/abs/2001.04063>`__ by
[ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training,](https://arxiv.org/abs/2001.04063) by
Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, Ming Zhou.
ProphetNet introduces a novel *sequence-to-sequence* pretraining objective, called *future n-gram prediction*. In
......@@ -735,39 +644,34 @@ self-attention mechanism and a self and n-stream (predict) self-attention mechan
The library provides a pre-trained version of this model for conditional generation and a fine-tuned version for
summarization.
XLM-ProphetNet
-----------------------------------------------------------------------------------------------------------------------
.. raw:: html
### XLM-ProphetNet
<a href="https://huggingface.co/models?filter=xprophetnet">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-xprophetnet-blueviolet">
</a>
<a href="model_doc/xlmprophetnet">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-xprophetnet-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=xprophetnet">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-xprophetnet-blueviolet">
</a>
<a href="model_doc/xlmprophetnet">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-xprophetnet-blueviolet">
</a>
`ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training, <https://arxiv.org/abs/2001.04063>`__ by
[ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training,](https://arxiv.org/abs/2001.04063) by
Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, Ming Zhou.
XLM-ProphetNet's model architecture and pretraining objective is same as ProphetNet, but XLM-ProphetNet was pre-trained
on the cross-lingual dataset `XGLUE <https://arxiv.org/abs/2004.01401>`__.
on the cross-lingual dataset [XGLUE](https://arxiv.org/abs/2004.01401).
The library provides a pre-trained version of this model for multi-lingual conditional generation and fine-tuned
versions for headline generation and question generation, respectively.
.. _multimodal-models:
<a id='multimodal-models'></a>
Multimodal models
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
## Multimodal models
There is one multimodal model in the library which has not been pretrained in the self-supervised fashion like the
others.
MMBT
-----------------------------------------------------------------------------------------------------------------------
### MMBT
`Supervised Multimodal Bitransformers for Classifying Images and Text <https://arxiv.org/abs/1909.02950>`_, Douwe Kiela
[Supervised Multimodal Bitransformers for Classifying Images and Text](https://arxiv.org/abs/1909.02950), Douwe Kiela
et al.
A transformers model used in multimodal settings, combining a text and an image to make predictions. The transformer
......@@ -780,30 +684,26 @@ model know which part of the input vector corresponds to the text and which to t
The pretrained model only works for classification.
..
More information in this :doc:`model documentation <model_doc/mmbt>`. TODO: write this page
<!--More information in this [model documentation](model_doc/mmbt). TODO: write this page
-->
.. _retrieval-based-models:
<a id='retrieval-based-models'></a>
Retrieval-based models
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
## Retrieval-based models
Some models use documents retrieval during (pre)training and inference for open-domain question answering, for example.
DPR
-----------------------------------------------------------------------------------------------------------------------
### DPR
.. raw:: html
<a href="https://huggingface.co/models?filter=dpr">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-dpr-blueviolet">
</a>
<a href="model_doc/dpr">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-dpr-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=dpr">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-dpr-blueviolet">
</a>
<a href="model_doc/dpr">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-dpr-blueviolet">
</a>
`Dense Passage Retrieval for Open-Domain Question Answering <https://arxiv.org/abs/2004.04906>`_, Vladimir Karpukhin et
[Dense Passage Retrieval for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906), Vladimir Karpukhin et
al.
Dense Passage Retrieval (DPR) - is a set of tools and models for state-of-the-art open-domain question-answering
......@@ -812,27 +712,24 @@ research.
DPR consists in three models:
* Question encoder: encode questions as vectors
* Context encoder: encode contexts as vectors
* Reader: extract the answer of the questions inside retrieved contexts, along with a relevance score (high if the
inferred span actually answers the question).
- Question encoder: encode questions as vectors
- Context encoder: encode contexts as vectors
- Reader: extract the answer of the questions inside retrieved contexts, along with a relevance score (high if the
inferred span actually answers the question).
DPR's pipeline (not implemented yet) uses a retrieval step to find the top k contexts given a certain question, and
then it calls the reader with the question and the retrieved documents to get the answer.
RAG
-----------------------------------------------------------------------------------------------------------------------
.. raw:: html
### RAG
<a href="https://huggingface.co/models?filter=rag">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-rag-blueviolet">
</a>
<a href="model_doc/rag">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-rag-blueviolet">
</a>
<a href="https://huggingface.co/models?filter=rag">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-rag-blueviolet">
</a>
<a href="model_doc/rag">
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-rag-blueviolet">
</a>
`Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks <https://arxiv.org/abs/2005.11401>`_, Patrick Lewis,
[Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401), Patrick Lewis,
Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau
Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela
......@@ -843,32 +740,30 @@ to adapt to downstream tasks.
The two models RAG-Token and RAG-Sequence are available for generation.
More technical aspects
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
## More technical aspects
Full vs sparse attention
-----------------------------------------------------------------------------------------------------------------------
### Full vs sparse attention
Most transformer models use full attention in the sense that the attention matrix is square. It can be a big
computational bottleneck when you have long texts. Longformer and reformer are models that try to be more efficient and
use a sparse version of the attention matrix to speed up training.
.. _lsh-attention:
<a id='lsh-attention'></a>
**LSH attention**
:ref:`Reformer <reformer>` uses LSH attention. In the softmax(QK^t), only the biggest elements (in the softmax
[Reformer](#reformer) uses LSH attention. In the softmax(QK^t), only the biggest elements (in the softmax
dimension) of the matrix QK^t are going to give useful contributions. So for each query q in Q, we can consider only
the keys k in K that are close to q. A hash function is used to determine if q and k are close. The attention mask is
modified to mask the current token (except at the first position), because it will give a query and a key equal (so
very similar to each other). Since the hash can be a bit random, several hash functions are used in practice
(determined by a n_rounds parameter) and then are averaged together.
.. _local-attention:
<a id='local-attention'></a>
**Local attention**
:ref:`Longformer <longformer>` uses local attention: often, the local context (e.g., what are the two tokens to the
[Longformer](#longformer) uses local attention: often, the local context (e.g., what are the two tokens to the
left and right?) is enough to take action for a given token. Also, by stacking attention layers that have a small
window, the last layer will have a receptive field of more than just the tokens in the window, allowing them to build a
representation of the whole sentence.
......@@ -877,25 +772,22 @@ Some preselected input tokens are also given global attention: for those few tok
all tokens and this process is symmetric: all other tokens have access to those specific tokens (on top of the ones in
their local window). This is shown in Figure 2d of the paper, see below for a sample attention mask:
.. image:: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/local_attention_mask.png
:scale: 50 %
:align: center
<img scale="50 %" align="center" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/local_attention_mask.png"/>
Using those attention matrices with less parameters then allows the model to have inputs having a bigger sequence
length.
Other tricks
-----------------------------------------------------------------------------------------------------------------------
### Other tricks
.. _axial-pos-encoding:
<a id='axial-pos-encoding'></a>
**Axial positional encodings**
:ref:`Reformer <reformer>` uses axial positional encodings: in traditional transformer models, the positional encoding
E is a matrix of size :math:`l` by :math:`d`, :math:`l` being the sequence length and :math:`d` the dimension of the
[Reformer](#reformer) uses axial positional encodings: in traditional transformer models, the positional encoding
E is a matrix of size \\(l\\) by \\(d\\), \\(l\\) being the sequence length and \\(d\\) the dimension of the
hidden state. If you have very long texts, this matrix can be huge and take way too much space on the GPU. To alleviate
that, axial positional encodings consist of factorizing that big matrix E in two smaller matrices E1 and E2, with
dimensions :math:`l_{1} \times d_{1}` and :math:`l_{2} \times d_{2}`, such that :math:`l_{1} \times l_{2} = l` and
:math:`d_{1} + d_{2} = d` (with the product for the lengths, this ends up being way smaller). The embedding for time
step :math:`j` in E is obtained by concatenating the embeddings for timestep :math:`j \% l1` in E1 and :math:`j // l1`
dimensions \\(l_{1} \times d_{1}\\) and \\(l_{2} \times d_{2}\\), such that \\(l_{1} \times l_{2} = l\\) and
\\(d_{1} + d_{2} = d\\) (with the product for the lengths, this ends up being way smaller). The embedding for time
step \\(j\\) in E is obtained by concatenating the embeddings for timestep \\(j \% l1\\) in E1 and \\(j // l1\\)
in E2.
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# Philosophy
🤗 Transformers is an opinionated library built for:
- NLP researchers and educators seeking to use/study/extend large-scale transformers models
- hands-on practitioners who want to fine-tune those models and/or serve them in production
- engineers who just want to download a pretrained model and use it to solve a given NLP task.
The library was designed with two strong goals in mind:
- Be as easy and fast to use as possible:
- We strongly limited the number of user-facing abstractions to learn, in fact, there are almost no abstractions,
just three standard classes required to use each model: [configuration](main_classes/configuration),
[models](main_classes/model) and [tokenizer](main_classes/tokenizer).
- All of these classes can be initialized in a simple and unified way from pretrained instances by using a common
`from_pretrained()` instantiation method which will take care of downloading (if needed), caching and
loading the related class instance and associated data (configurations' hyper-parameters, tokenizers' vocabulary,
and models' weights) from a pretrained checkpoint provided on [Hugging Face Hub](https://huggingface.co/models) or your own saved checkpoint.
- On top of those three base classes, the library provides two APIs: [`pipeline`] for quickly
using a model (plus its associated tokenizer and configuration) on a given task and
[`Trainer`]/`Keras.fit` to quickly train or fine-tune a given model.
- As a consequence, this library is NOT a modular toolbox of building blocks for neural nets. If you want to
extend/build-upon the library, just use regular Python/PyTorch/TensorFlow/Keras modules and inherit from the base
classes of the library to reuse functionalities like model loading/saving.
- Provide state-of-the-art models with performances as close as possible to the original models:
- We provide at least one example for each architecture which reproduces a result provided by the official authors
of said architecture.
- The code is usually as close to the original code base as possible which means some PyTorch code may be not as
*pytorchic* as it could be as a result of being converted TensorFlow code and vice versa.
A few other goals:
- Expose the models' internals as consistently as possible:
- We give access, using a single API, to the full hidden-states and attention weights.
- Tokenizer and base model's API are standardized to easily switch between models.
- Incorporate a subjective selection of promising tools for fine-tuning/investigating these models:
- A simple/consistent way to add new tokens to the vocabulary and embeddings for fine-tuning.
- Simple ways to mask and prune transformer heads.
- Switch easily between PyTorch and TensorFlow 2.0, allowing training using one framework and inference using another.
## Main concepts
The library is built around three types of classes for each model:
- **Model classes** such as [`BertModel`], which are 30+ PyTorch models ([torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)) or Keras models ([tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model)) that work with the pretrained weights provided in the
library.
- **Configuration classes** such as [`BertConfig`], which store all the parameters required to build
a model. You don't always need to instantiate these yourself. In particular, if you are using a pretrained model
without any modification, creating the model will automatically take care of instantiating the configuration (which
is part of the model).
- **Tokenizer classes** such as [`BertTokenizer`], which store the vocabulary for each model and
provide methods for encoding/decoding strings in a list of token embeddings indices to be fed to a model.
All these classes can be instantiated from pretrained instances and saved locally using two methods:
- `from_pretrained()` lets you instantiate a model/configuration/tokenizer from a pretrained version either
provided by the library itself (the supported models can be found on the [Model Hub](https://huggingface.co/models)) or
stored locally (or on a server) by the user,
- `save_pretrained()` lets you save a model/configuration/tokenizer locally so that it can be reloaded using
`from_pretrained()`.
docs/source/philosophy.rst deleted 100644 → 0
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..
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
Philosophy
=======================================================================================================================
🤗 Transformers is an opinionated library built for:
- NLP researchers and educators seeking to use/study/extend large-scale transformers models
- hands-on practitioners who want to fine-tune those models and/or serve them in production
- engineers who just want to download a pretrained model and use it to solve a given NLP task.
The library was designed with two strong goals in mind:
- Be as easy and fast to use as possible:
- We strongly limited the number of user-facing abstractions to learn, in fact, there are almost no abstractions,
just three standard classes required to use each model: :doc:`configuration <main_classes/configuration>`,
:doc:`models <main_classes/model>` and :doc:`tokenizer <main_classes/tokenizer>`.
- All of these classes can be initialized in a simple and unified way from pretrained instances by using a common
:obj:`from_pretrained()` instantiation method which will take care of downloading (if needed), caching and
loading the related class instance and associated data (configurations' hyper-parameters, tokenizers' vocabulary,
and models' weights) from a pretrained checkpoint provided on `Hugging Face Hub
<https://huggingface.co/models>`__ or your own saved checkpoint.
- On top of those three base classes, the library provides two APIs: :func:`~transformers.pipeline` for quickly
using a model (plus its associated tokenizer and configuration) on a given task and
:func:`~transformers.Trainer`/:func:`~transformers.TFTrainer` to quickly train or fine-tune a given model.
- As a consequence, this library is NOT a modular toolbox of building blocks for neural nets. If you want to
extend/build-upon the library, just use regular Python/PyTorch/TensorFlow/Keras modules and inherit from the base
classes of the library to reuse functionalities like model loading/saving.
- Provide state-of-the-art models with performances as close as possible to the original models:
- We provide at least one example for each architecture which reproduces a result provided by the official authors
of said architecture.
- The code is usually as close to the original code base as possible which means some PyTorch code may be not as
*pytorchic* as it could be as a result of being converted TensorFlow code and vice versa.
A few other goals:
- Expose the models' internals as consistently as possible:
- We give access, using a single API, to the full hidden-states and attention weights.
- Tokenizer and base model's API are standardized to easily switch between models.
- Incorporate a subjective selection of promising tools for fine-tuning/investigating these models:
- A simple/consistent way to add new tokens to the vocabulary and embeddings for fine-tuning.
- Simple ways to mask and prune transformer heads.
- Switch easily between PyTorch and TensorFlow 2.0, allowing training using one framework and inference using another.
Main concepts
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The library is built around three types of classes for each model:
- **Model classes** such as :class:`~transformers.BertModel`, which are 30+ PyTorch models (`torch.nn.Module
<https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__) or Keras models (`tf.keras.Model
<https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__) that work with the pretrained weights provided in the
library.
- **Configuration classes** such as :class:`~transformers.BertConfig`, which store all the parameters required to build
a model. You don't always need to instantiate these yourself. In particular, if you are using a pretrained model
without any modification, creating the model will automatically take care of instantiating the configuration (which
is part of the model).
- **Tokenizer classes** such as :class:`~transformers.BertTokenizer`, which store the vocabulary for each model and
provide methods for encoding/decoding strings in a list of token embeddings indices to be fed to a model.
All these classes can be instantiated from pretrained instances and saved locally using two methods:
- :obj:`from_pretrained()` lets you instantiate a model/configuration/tokenizer from a pretrained version either
provided by the library itself (the supported models are provided in the list :doc:`here <pretrained_models>`) or
stored locally (or on a server) by the user,
- :obj:`save_pretrained()` lets you save a model/configuration/tokenizer locally so that it can be reloaded using
:obj:`from_pretrained()`.
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