Convert model files from rst to mdx (#14865)

* First pass

* Apply suggestions from code review

* Apply suggestions from code review

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

docs/source/model_doc/electra.mdx

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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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+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.
+-->
+
+# ELECTRA
+
+## Overview
+
+The ELECTRA model was proposed in the paper [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than
+Generators](https://openreview.net/pdf?id=r1xMH1BtvB). ELECTRA is a new pretraining approach which trains two
+transformer models: the generator and the discriminator. The generator's role is to replace tokens in a sequence, and
+is therefore trained as a masked language model. The discriminator, which is the model we're interested in, tries to
+identify which tokens were replaced by the generator in the sequence.
+
+The abstract from the paper is the following:
+
+*Masked language modeling (MLM) pretraining methods such as BERT corrupt the input by replacing some tokens with [MASK]
+and then train a model to reconstruct the original tokens. While they produce good results when transferred to
+downstream NLP tasks, they generally require large amounts of compute to be effective. As an alternative, we propose a
+more sample-efficient pretraining task called replaced token detection. Instead of masking the input, our approach
+corrupts it by replacing some tokens with plausible alternatives sampled from a small generator network. Then, instead
+of training a model that predicts the original identities of the corrupted tokens, we train a discriminative model that
+predicts whether each token in the corrupted input was replaced by a generator sample or not. Thorough experiments
+demonstrate this new pretraining task is more efficient than MLM because the task is defined over all input tokens
+rather than just the small subset that was masked out. As a result, the contextual representations learned by our
+approach substantially outperform the ones learned by BERT given the same model size, data, and compute. The gains are
+particularly strong for small models; for example, we train a model on one GPU for 4 days that outperforms GPT (trained
+using 30x more compute) on the GLUE natural language understanding benchmark. Our approach also works well at scale,
+where it performs comparably to RoBERTa and XLNet while using less than 1/4 of their compute and outperforms them when
+using the same amount of compute.*
+
+Tips:
+
+- ELECTRA is the pretraining approach, therefore there is nearly no changes done to the underlying model: BERT. The
+  only change is the separation of the embedding size and the hidden size: the embedding size is generally smaller,
+  while the hidden size is larger. An additional projection layer (linear) is used to project the embeddings from their
+  embedding size to the hidden size. In the case where the embedding size is the same as the hidden size, no projection
+  layer is used.
+- The ELECTRA checkpoints saved using [Google Research's implementation](https://github.com/google-research/electra)
+  contain both the generator and discriminator. The conversion script requires the user to name which model to export
+  into the correct architecture. Once converted to the HuggingFace format, these checkpoints may be loaded into all
+  available ELECTRA models, however. This means that the discriminator may be loaded in the
+  [`ElectraForMaskedLM`] model, and the generator may be loaded in the
+  [`ElectraForPreTraining`] model (the classification head will be randomly initialized as it
+  doesn't exist in the generator).
+
+This model was contributed by [lysandre](https://huggingface.co/lysandre). The original code can be found [here](https://github.com/google-research/electra).
+
+
+## ElectraConfig
+
+[[autodoc]] ElectraConfig
+
+## ElectraTokenizer
+
+[[autodoc]] ElectraTokenizer
+
+## ElectraTokenizerFast
+
+[[autodoc]] ElectraTokenizerFast
+
+## Electra specific outputs
+
+[[autodoc]] models.electra.modeling_electra.ElectraForPreTrainingOutput
+
+[[autodoc]] models.electra.modeling_tf_electra.TFElectraForPreTrainingOutput
+
+## ElectraModel
+
+[[autodoc]] ElectraModel
+    - forward
+
+## ElectraForPreTraining
+
+[[autodoc]] ElectraForPreTraining
+    - forward
+
+## ElectraForMaskedLM
+
+[[autodoc]] ElectraForMaskedLM
+    - forward
+
+## ElectraForSequenceClassification
+
+[[autodoc]] ElectraForSequenceClassification
+    - forward
+
+## ElectraForMultipleChoice
+
+[[autodoc]] ElectraForMultipleChoice
+    - forward
+
+## ElectraForTokenClassification
+
+[[autodoc]] ElectraForTokenClassification
+    - forward
+
+## ElectraForQuestionAnswering
+
+[[autodoc]] ElectraForQuestionAnswering
+    - forward
+
+## TFElectraModel
+
+[[autodoc]] TFElectraModel
+    - call
+
+## TFElectraForPreTraining
+
+[[autodoc]] TFElectraForPreTraining
+    - call
+
+## TFElectraForMaskedLM
+
+[[autodoc]] TFElectraForMaskedLM
+    - call
+
+## TFElectraForSequenceClassification
+
+[[autodoc]] TFElectraForSequenceClassification
+    - call
+
+## TFElectraForMultipleChoice
+
+[[autodoc]] TFElectraForMultipleChoice
+    - call
+
+## TFElectraForTokenClassification
+
+[[autodoc]] TFElectraForTokenClassification
+    - call
+
+## TFElectraForQuestionAnswering
+
+[[autodoc]] TFElectraForQuestionAnswering
+    - call
+
+## FlaxElectraModel
+
+[[autodoc]] FlaxElectraModel
+    - __call__
+
+## FlaxElectraForPreTraining
+
+[[autodoc]] FlaxElectraForPreTraining
+    - __call__
+
+## FlaxElectraForMaskedLM
+
+[[autodoc]] FlaxElectraForMaskedLM
+    - __call__
+
+## FlaxElectraForSequenceClassification
+
+[[autodoc]] FlaxElectraForSequenceClassification
+    - __call__
+
+## FlaxElectraForMultipleChoice
+
+[[autodoc]] FlaxElectraForMultipleChoice
+    - __call__
+
+## FlaxElectraForTokenClassification
+
+[[autodoc]] FlaxElectraForTokenClassification
+    - __call__
+
+## FlaxElectraForQuestionAnswering
+
+[[autodoc]] FlaxElectraForQuestionAnswering
+    - __call__