Convert rst to mdx bert (#14806)
* BERT to mdx mdx :) c * Update docs/source/model_doc/bert.mdx Co-authored-by: Julien Chaumond <julien@huggingface.co> * Remove all Co-authored-by: sgugger <sylvain.gugger@gmail.com> Co-authored-by: Julien Chaumond <julien@huggingface.co>
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<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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-->
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# BERT
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## Overview
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The BERT model was proposed in [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. It's a
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bidirectional transformer pretrained using a combination of masked language modeling objective and next sentence
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prediction on a large corpus comprising the Toronto Book Corpus and Wikipedia.
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The abstract from the paper is the following:
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*We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations
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from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional
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representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result,
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the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models
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for a wide range of tasks, such as question answering and language inference, without substantial task-specific
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architecture modifications.*
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*BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural
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language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI
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accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute
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improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).*
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Tips:
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- BERT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
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the left.
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- BERT was trained with the masked language modeling (MLM) and next sentence prediction (NSP) objectives. It is
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efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation.
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|
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This model was contributed by [thomwolf](https://huggingface.co/thomwolf). The original code can be found [here](https://github.com/google-research/bert).
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## BertConfig
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[[autodoc]] BertConfig
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- all
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## BertTokenizer
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[[autodoc]] BertTokenizer
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- build_inputs_with_special_tokens
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- get_special_tokens_mask
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- create_token_type_ids_from_sequences
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- save_vocabulary
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## BertTokenizerFast
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[[autodoc]] BertTokenizerFast
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## Bert specific outputs
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[[autodoc]] models.bert.modeling_bert.BertForPreTrainingOutput
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[[autodoc]] models.bert.modeling_tf_bert.TFBertForPreTrainingOutput
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[[autodoc]] models.bert.modeling_flax_bert.FlaxBertForPreTrainingOutput
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## BertModel
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[[autodoc]] BertModel
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- forward
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## BertForPreTraining
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[[autodoc]] BertForPreTraining
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- forward
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## BertLMHeadModel
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[[autodoc]] BertLMHeadModel
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- forward
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## BertForMaskedLM
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[[autodoc]] BertForMaskedLM
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- forward
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## BertForNextSentencePrediction
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[[autodoc]] BertForNextSentencePrediction
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- forward
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## BertForSequenceClassification
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[[autodoc]] BertForSequenceClassification
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- forward
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## BertForMultipleChoice
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[[autodoc]] BertForMultipleChoice
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- forward
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## BertForTokenClassification
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[[autodoc]] BertForTokenClassification
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- forward
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## BertForQuestionAnswering
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[[autodoc]] BertForQuestionAnswering
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- forward
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## TFBertModel
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[[autodoc]] TFBertModel
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- call
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## TFBertForPreTraining
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[[autodoc]] TFBertForPreTraining
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- call
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## TFBertModelLMHeadModel
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[[autodoc]] TFBertLMHeadModel
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- call
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## TFBertForMaskedLM
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[[autodoc]] TFBertForMaskedLM
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- call
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## TFBertForNextSentencePrediction
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[[autodoc]] TFBertForNextSentencePrediction
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- call
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## TFBertForSequenceClassification
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[[autodoc]] TFBertForSequenceClassification
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- call
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## TFBertForMultipleChoice
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[[autodoc]] TFBertForMultipleChoice
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- call
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## TFBertForTokenClassification
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[[autodoc]] TFBertForTokenClassification
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- call
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## TFBertForQuestionAnswering
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[[autodoc]] TFBertForQuestionAnswering
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- call
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## FlaxBertModel
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[[autodoc]] FlaxBertModel
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- __call__
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## FlaxBertForPreTraining
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[[autodoc]] FlaxBertForPreTraining
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- __call__
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## FlaxBertForMaskedLM
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[[autodoc]] FlaxBertForMaskedLM
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- __call__
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## FlaxBertForNextSentencePrediction
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[[autodoc]] FlaxBertForNextSentencePrediction
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- __call__
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## FlaxBertForSequenceClassification
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[[autodoc]] FlaxBertForSequenceClassification
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- __call__
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## FlaxBertForMultipleChoice
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[[autodoc]] FlaxBertForMultipleChoice
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- __call__
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## FlaxBertForTokenClassification
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[[autodoc]] FlaxBertForTokenClassification
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- __call__
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## FlaxBertForQuestionAnswering
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[[autodoc]] FlaxBertForQuestionAnswering
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- __call__
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@@ -1,262 +0,0 @@
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..
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Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
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the License. You may obtain a copy of the License at
|
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|
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
|
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BERT
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-----------------------------------------------------------------------------------------------------------------------
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The BERT model was proposed in `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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<https://arxiv.org/abs/1810.04805>`__ by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. It's a
|
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bidirectional transformer pretrained using a combination of masked language modeling objective and next sentence
|
|
||||||
prediction on a large corpus comprising the Toronto Book Corpus and Wikipedia.
|
|
||||||
|
|
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The abstract from the paper is the following:
|
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|
|
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*We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations
|
|
||||||
from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional
|
|
||||||
representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result,
|
|
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the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models
|
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||||||
for a wide range of tasks, such as question answering and language inference, without substantial task-specific
|
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architecture modifications.*
|
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|
|
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*BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural
|
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language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI
|
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||||||
accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute
|
|
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improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).*
|
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Tips:
|
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||||||
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|
||||||
- BERT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
|
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the left.
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- BERT was trained with the masked language modeling (MLM) and next sentence prediction (NSP) objectives. It is
|
|
||||||
efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation.
|
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|
|
||||||
This model was contributed by `thomwolf <https://huggingface.co/thomwolf>`__. The original code can be found `here
|
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<https://github.com/google-research/bert>`__.
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BertConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.BertConfig
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:members:
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BertTokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.BertTokenizer
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:members: build_inputs_with_special_tokens, get_special_tokens_mask,
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create_token_type_ids_from_sequences, save_vocabulary
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BertTokenizerFast
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.BertTokenizerFast
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:members:
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Bert specific outputs
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.models.bert.modeling_bert.BertForPreTrainingOutput
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:members:
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.. autoclass:: transformers.models.bert.modeling_tf_bert.TFBertForPreTrainingOutput
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:members:
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.. autoclass:: transformers.models.bert.modeling_flax_bert.FlaxBertForPreTrainingOutput
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:members:
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BertModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.BertModel
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:members: forward
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BertForPreTraining
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.BertForPreTraining
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:members: forward
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BertLMHeadModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.BertLMHeadModel
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:members: forward
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BertForMaskedLM
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.BertForMaskedLM
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:members: forward
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||||||
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BertForNextSentencePrediction
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|
||||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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||||||
.. autoclass:: transformers.BertForNextSentencePrediction
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:members: forward
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||||||
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||||||
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||||||
BertForSequenceClassification
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||||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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||||||
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||||||
.. autoclass:: transformers.BertForSequenceClassification
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|
||||||
:members: forward
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||||||
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||||||
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BertForMultipleChoice
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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||||||
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.. autoclass:: transformers.BertForMultipleChoice
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||||||
:members: forward
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||||||
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||||||
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||||||
BertForTokenClassification
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|
||||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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||||||
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||||||
.. autoclass:: transformers.BertForTokenClassification
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|
||||||
:members: forward
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||||||
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||||||
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||||||
BertForQuestionAnswering
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||||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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||||||
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|
||||||
.. autoclass:: transformers.BertForQuestionAnswering
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|
||||||
:members: forward
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||||||
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|
||||||
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|
||||||
TFBertModel
|
|
||||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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|
||||||
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|
||||||
.. autoclass:: transformers.TFBertModel
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|
||||||
:members: call
|
|
||||||
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|
||||||
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|
||||||
TFBertForPreTraining
|
|
||||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
||||||
|
|
||||||
.. autoclass:: transformers.TFBertForPreTraining
|
|
||||||
:members: call
|
|
||||||
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|
||||||
|
|
||||||
TFBertModelLMHeadModel
|
|
||||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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|
||||||
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|
||||||
.. autoclass:: transformers.TFBertLMHeadModel
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|
||||||
:members: call
|
|
||||||
|
|
||||||
|
|
||||||
TFBertForMaskedLM
|
|
||||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
||||||
|
|
||||||
.. autoclass:: transformers.TFBertForMaskedLM
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|
||||||
:members: call
|
|
||||||
|
|
||||||
|
|
||||||
TFBertForNextSentencePrediction
|
|
||||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
||||||
|
|
||||||
.. autoclass:: transformers.TFBertForNextSentencePrediction
|
|
||||||
:members: call
|
|
||||||
|
|
||||||
|
|
||||||
TFBertForSequenceClassification
|
|
||||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
||||||
|
|
||||||
.. autoclass:: transformers.TFBertForSequenceClassification
|
|
||||||
:members: call
|
|
||||||
|
|
||||||
|
|
||||||
TFBertForMultipleChoice
|
|
||||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
||||||
|
|
||||||
.. autoclass:: transformers.TFBertForMultipleChoice
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|
||||||
:members: call
|
|
||||||
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|
||||||
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|
||||||
TFBertForTokenClassification
|
|
||||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
||||||
|
|
||||||
.. autoclass:: transformers.TFBertForTokenClassification
|
|
||||||
:members: call
|
|
||||||
|
|
||||||
|
|
||||||
TFBertForQuestionAnswering
|
|
||||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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|
||||||
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|
||||||
.. autoclass:: transformers.TFBertForQuestionAnswering
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|
||||||
:members: call
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|
||||||
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|
||||||
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|
||||||
FlaxBertModel
|
|
||||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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|
||||||
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|
||||||
.. autoclass:: transformers.FlaxBertModel
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|
||||||
:members: __call__
|
|
||||||
|
|
||||||
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|
||||||
FlaxBertForPreTraining
|
|
||||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
||||||
|
|
||||||
.. autoclass:: transformers.FlaxBertForPreTraining
|
|
||||||
:members: __call__
|
|
||||||
|
|
||||||
|
|
||||||
FlaxBertForMaskedLM
|
|
||||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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|
||||||
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|
||||||
.. autoclass:: transformers.FlaxBertForMaskedLM
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|
||||||
:members: __call__
|
|
||||||
|
|
||||||
|
|
||||||
FlaxBertForNextSentencePrediction
|
|
||||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
||||||
|
|
||||||
.. autoclass:: transformers.FlaxBertForNextSentencePrediction
|
|
||||||
:members: __call__
|
|
||||||
|
|
||||||
|
|
||||||
FlaxBertForSequenceClassification
|
|
||||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
||||||
|
|
||||||
.. autoclass:: transformers.FlaxBertForSequenceClassification
|
|
||||||
:members: __call__
|
|
||||||
|
|
||||||
|
|
||||||
FlaxBertForMultipleChoice
|
|
||||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
||||||
|
|
||||||
.. autoclass:: transformers.FlaxBertForMultipleChoice
|
|
||||||
:members: __call__
|
|
||||||
|
|
||||||
|
|
||||||
FlaxBertForTokenClassification
|
|
||||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
||||||
|
|
||||||
.. autoclass:: transformers.FlaxBertForTokenClassification
|
|
||||||
:members: __call__
|
|
||||||
|
|
||||||
|
|
||||||
FlaxBertForQuestionAnswering
|
|
||||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
||||||
|
|
||||||
.. autoclass:: transformers.FlaxBertForQuestionAnswering
|
|
||||||
:members: __call__
|
|
||||||
Reference in New Issue
Block a user