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>

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>
77d6c826 · Lysandre Debut · GitHub · 0b4ea79a · 77d6c826 · 0b4ea79a
Unverified Commit 77d6c826 authored Dec 17, 2021 by Lysandre Debut Committed by GitHub Dec 17, 2021
Hide whitespace changes
Inline Side-by-side

Showing with 197 additions and 262 deletions

docs/source/model_doc/bert.mdx docs/source/model_doc/bert.mdx +197 -0

docs/source/model_doc/bert.rst docs/source/model_doc/bert.rst +0 -262

No files found.
--- a/docs/source/model_doc/bert.mdx
+++ b/docs/source/model_doc/bert.mdx
+<!--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.
+-->
+# BERT
+## Overview
+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
+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.
+The abstract from the paper is the following:
+*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,
+the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models
+for a wide range of tasks, such as question answering and language inference, without substantial task-specific
+architecture modifications.*
+*BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural
+language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI
+accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute
+improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).*
+Tips:
+- BERT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
+  the left.
+- 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.
+This model was contributed by [thomwolf](https://huggingface.co/thomwolf). The original code can be found [here](https://github.com/google-research/bert).
+## BertConfig
+[[autodoc]] BertConfig
+    - all
+## BertTokenizer
+[[autodoc]] BertTokenizer
+    - build_inputs_with_special_tokens
+    - get_special_tokens_mask
+    - create_token_type_ids_from_sequences
+    - save_vocabulary
+## BertTokenizerFast
+[[autodoc]] BertTokenizerFast
+## Bert specific outputs
+[[autodoc]] models.bert.modeling_bert.BertForPreTrainingOutput
+[[autodoc]] models.bert.modeling_tf_bert.TFBertForPreTrainingOutput
+[[autodoc]] models.bert.modeling_flax_bert.FlaxBertForPreTrainingOutput
+## BertModel
+[[autodoc]] BertModel
+    - forward
+## BertForPreTraining
+[[autodoc]] BertForPreTraining
+    - forward
+## BertLMHeadModel
+[[autodoc]] BertLMHeadModel
+    - forward
+## BertForMaskedLM
+[[autodoc]] BertForMaskedLM
+    - forward
+## BertForNextSentencePrediction
+[[autodoc]] BertForNextSentencePrediction
+    - forward
+## BertForSequenceClassification
+[[autodoc]] BertForSequenceClassification
+    - forward
+## BertForMultipleChoice
+[[autodoc]] BertForMultipleChoice
+    - forward
+## BertForTokenClassification
+[[autodoc]] BertForTokenClassification
+    - forward
+## BertForQuestionAnswering
+[[autodoc]] BertForQuestionAnswering
+    - forward
+## TFBertModel
+[[autodoc]] TFBertModel
+    - call
+## TFBertForPreTraining
+[[autodoc]] TFBertForPreTraining
+    - call
+## TFBertModelLMHeadModel
+[[autodoc]] TFBertLMHeadModel
+    - call
+## TFBertForMaskedLM
+[[autodoc]] TFBertForMaskedLM
+    - call
+## TFBertForNextSentencePrediction
+[[autodoc]] TFBertForNextSentencePrediction
+    - call
+## TFBertForSequenceClassification
+[[autodoc]] TFBertForSequenceClassification
+    - call
+## TFBertForMultipleChoice
+[[autodoc]] TFBertForMultipleChoice
+    - call
+## TFBertForTokenClassification
+[[autodoc]] TFBertForTokenClassification
+    - call
+## TFBertForQuestionAnswering
+[[autodoc]] TFBertForQuestionAnswering
+    - call
+## FlaxBertModel
+[[autodoc]] FlaxBertModel
+    - __call__
+## FlaxBertForPreTraining
+[[autodoc]] FlaxBertForPreTraining
+    - __call__
+## FlaxBertForMaskedLM
+[[autodoc]] FlaxBertForMaskedLM
+    - __call__
+## FlaxBertForNextSentencePrediction
+[[autodoc]] FlaxBertForNextSentencePrediction
+    - __call__
+## FlaxBertForSequenceClassification
+[[autodoc]] FlaxBertForSequenceClassification
+    - __call__
+## FlaxBertForMultipleChoice
+[[autodoc]] FlaxBertForMultipleChoice
+    - __call__
+## FlaxBertForTokenClassification
+[[autodoc]] FlaxBertForTokenClassification
+    - __call__
+## FlaxBertForQuestionAnswering
+[[autodoc]] FlaxBertForQuestionAnswering
+    - __call__
--- a/docs/source/model_doc/bert.rst
+++ b/docs/source/model_doc/bert.rst
-.. 
-    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.
-BERT
-----------------------------------------------------------------------------------------------------------------------
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-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
-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.
-The abstract from the paper is the following:
-*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,
-the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models
-for a wide range of tasks, such as question answering and language inference, without substantial task-specific
-architecture modifications.*
-*BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural
-language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI
-accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute
-improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).*
-Tips:
- BERT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
-  the left.
- 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.
-This model was contributed by `thomwolf <https://huggingface.co/thomwolf>`__. The original code can be found `here
-<https://github.com/google-research/bert>`__.
-BertConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.BertConfig
-    :members:
-BertTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.BertTokenizer
-    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, save_vocabulary
-BertTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.BertTokenizerFast
-    :members:
-Bert specific outputs
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.models.bert.modeling_bert.BertForPreTrainingOutput
-    :members:
-.. autoclass:: transformers.models.bert.modeling_tf_bert.TFBertForPreTrainingOutput
-    :members:
-.. autoclass:: transformers.models.bert.modeling_flax_bert.FlaxBertForPreTrainingOutput
-    :members:
-BertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.BertModel
-    :members: forward
-BertForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.BertForPreTraining
-    :members: forward
-BertLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.BertLMHeadModel
-    :members: forward
-BertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.BertForMaskedLM
-    :members: forward
-BertForNextSentencePrediction
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.BertForNextSentencePrediction
-    :members: forward
-BertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.BertForSequenceClassification
-    :members: forward
-BertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.BertForMultipleChoice
-    :members: forward
-BertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.BertForTokenClassification
-    :members: forward
-BertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.BertForQuestionAnswering
-    :members: forward
-TFBertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.TFBertModel
-    :members: call
-TFBertForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.TFBertForPreTraining
-    :members: call
-TFBertModelLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.TFBertLMHeadModel
-    :members: call
-TFBertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.TFBertForMaskedLM
-    :members: call
-TFBertForNextSentencePrediction
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.TFBertForNextSentencePrediction
-    :members: call
-TFBertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.TFBertForSequenceClassification
-    :members: call
-TFBertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.TFBertForMultipleChoice
-    :members: call
-TFBertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.TFBertForTokenClassification
-    :members: call
-TFBertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.TFBertForQuestionAnswering
-    :members: call
-FlaxBertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.FlaxBertModel
-    :members: __call__
-FlaxBertForPreTraining
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.FlaxBertForPreTraining
-    :members: __call__
-FlaxBertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.FlaxBertForMaskedLM
-    :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__