Migrate doc files to Markdown. (#24376)

* Rename index.mdx to index.md

* With saved modifs

* Address review comment

* Treat all files

* .mdx -> .md

* Remove special char

* Update utils/tests_fetcher.py

Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>

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Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>

docs/source/en/model_doc/table-transformer.md

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+<!--Copyright 2022 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
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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
+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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Table Transformer
+
+## Overview
+
+The Table Transformer model was proposed in [PubTables-1M: Towards comprehensive table extraction from unstructured documents](https://arxiv.org/abs/2110.00061) by
+Brandon Smock, Rohith Pesala, Robin Abraham. The authors introduce a new dataset, PubTables-1M, to benchmark progress in table extraction from unstructured documents,
+as well as table structure recognition and functional analysis. The authors train 2 [DETR](detr) models, one for table detection and one for table structure recognition, dubbed Table Transformers.
+
+The abstract from the paper is the following:
+
+*Recently, significant progress has been made applying machine learning to the problem of table structure inference and extraction from unstructured documents.
+However, one of the greatest challenges remains the creation of datasets with complete, unambiguous ground truth at scale. To address this, we develop a new, more
+comprehensive dataset for table extraction, called PubTables-1M. PubTables-1M contains nearly one million tables from scientific articles, supports multiple input
+modalities, and contains detailed header and location information for table structures, making it useful for a wide variety of modeling approaches. It also addresses a significant
+source of ground truth inconsistency observed in prior datasets called oversegmentation, using a novel canonicalization procedure. We demonstrate that these improvements lead to a
+significant increase in training performance and a more reliable estimate of model performance at evaluation for table structure recognition. Further, we show that transformer-based
+object detection models trained on PubTables-1M produce excellent results for all three tasks of detection, structure recognition, and functional analysis without the need for any
+special customization for these tasks.*
+
+Tips:
+
+- The authors released 2 models, one for [table detection](https://huggingface.co/microsoft/table-transformer-detection) in documents, one for [table structure recognition](https://huggingface.co/microsoft/table-transformer-structure-recognition) (the task of recognizing the individual rows, columns etc. in a table).
+- One can use the [`AutoImageProcessor`] API to prepare images and optional targets for the model. This will load a [`DetrImageProcessor`] behind the scenes.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/table_transformer_architecture.jpeg"
+alt="drawing" width="600"/>
+
+<small> Table detection and table structure recognition clarified. Taken from the <a href="https://arxiv.org/abs/2110.00061">original paper</a>. </small>
+
+This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be
+found [here](https://github.com/microsoft/table-transformer).
+
+## Resources
+
+<PipelineTag pipeline="object-detection"/>
+
+- A demo notebook for the Table Transformer can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Table%20Transformer).
+- It turns out padding of images is quite important for detection. An interesting Github thread with replies from the authors can be found [here](https://github.com/microsoft/table-transformer/issues/68).
+
+## TableTransformerConfig
+
+[[autodoc]] TableTransformerConfig
+
+## TableTransformerModel
+
+[[autodoc]] TableTransformerModel
+    - forward
+
+## TableTransformerForObjectDetection
+
+[[autodoc]] TableTransformerForObjectDetection
+    - forward