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/layoutlm.mdx

@@ -0,0 +1,124 @@
+<!--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.
+-->
+
+# LayoutLM
+
+<a id='Overview'></a>
+
+## Overview
+
+The LayoutLM model was proposed in the paper [LayoutLM: Pre-training of Text and Layout for Document Image
+Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and
+Ming Zhou. It's a simple but effective pretraining method of text and layout for document image understanding and
+information extraction tasks, such as form understanding and receipt understanding. It obtains state-of-the-art results
+on several downstream tasks:
+
+- form understanding: the [FUNSD](https://guillaumejaume.github.io/FUNSD/) dataset (a collection of 199 annotated
+  forms comprising more than 30,000 words).
+- receipt understanding: the [SROIE](https://rrc.cvc.uab.es/?ch=13) dataset (a collection of 626 receipts for
+  training and 347 receipts for testing).
+- document image classification: the [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/) dataset (a collection of
+  400,000 images belonging to one of 16 classes).
+
+The abstract from the paper is the following:
+
+*Pre-training techniques have been verified successfully in a variety of NLP tasks in recent years. Despite the
+widespread use of pretraining models for NLP applications, they almost exclusively focus on text-level manipulation,
+while neglecting layout and style information that is vital for document image understanding. In this paper, we propose
+the LayoutLM to jointly model interactions between text and layout information across scanned document images, which is
+beneficial for a great number of real-world document image understanding tasks such as information extraction from
+scanned documents. Furthermore, we also leverage image features to incorporate words' visual information into LayoutLM.
+To the best of our knowledge, this is the first time that text and layout are jointly learned in a single framework for
+document-level pretraining. It achieves new state-of-the-art results in several downstream tasks, including form
+understanding (from 70.72 to 79.27), receipt understanding (from 94.02 to 95.24) and document image classification
+(from 93.07 to 94.42).*
+
+Tips:
+
+- In addition to *input_ids*, [`~transformers.LayoutLMModel.forward`] also expects the input `bbox`, which are
+  the bounding boxes (i.e. 2D-positions) of the input tokens. These can be obtained using an external OCR engine such
+  as Google's [Tesseract](https://github.com/tesseract-ocr/tesseract) (there's a [Python wrapper](https://pypi.org/project/pytesseract/) available). Each bounding box should be in (x0, y0, x1, y1) format, where
+  (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1, y1) represents the
+  position of the lower right corner. Note that one first needs to normalize the bounding boxes to be on a 0-1000
+  scale. To normalize, you can use the following function:
+
+```python
+def normalize_bbox(bbox, width, height):
+     return [
+         int(1000 * (bbox[0] / width)),
+         int(1000 * (bbox[1] / height)),
+         int(1000 * (bbox[2] / width)),
+         int(1000 * (bbox[3] / height)),
+     ]
+```
+
+Here, `width` and `height` correspond to the width and height of the original document in which the token
+occurs. Those can be obtained using the Python Image Library (PIL) library for example, as follows:
+
+```python
+from PIL import Image
+
+image = Image.open("name_of_your_document - can be a png file, pdf, etc.")
+
+width, height = image.size
+```
+
+- For a demo which shows how to fine-tune [`LayoutLMForTokenClassification`] on the [FUNSD dataset](https://guillaumejaume.github.io/FUNSD/) (a collection of annotated forms), see [this notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Fine_tuning_LayoutLMForTokenClassification_on_FUNSD.ipynb).
+  It includes an inference part, which shows how to use Google's Tesseract on a new document.
+
+This model was contributed by [liminghao1630](https://huggingface.co/liminghao1630). The original code can be found
+[here](https://github.com/microsoft/unilm/tree/master/layoutlm).
+
+
+## LayoutLMConfig
+
+[[autodoc]] LayoutLMConfig
+
+## LayoutLMTokenizer
+
+[[autodoc]] LayoutLMTokenizer
+
+## LayoutLMTokenizerFast
+
+[[autodoc]] LayoutLMTokenizerFast
+
+## LayoutLMModel
+
+[[autodoc]] LayoutLMModel
+
+## LayoutLMForMaskedLM
+
+[[autodoc]] LayoutLMForMaskedLM
+
+## LayoutLMForSequenceClassification
+
+[[autodoc]] LayoutLMForSequenceClassification
+
+## LayoutLMForTokenClassification
+
+[[autodoc]] LayoutLMForTokenClassification
+
+## TFLayoutLMModel
+
+[[autodoc]] TFLayoutLMModel
+
+## TFLayoutLMForMaskedLM
+
+[[autodoc]] TFLayoutLMForMaskedLM
+
+## TFLayoutLMForSequenceClassification
+
+[[autodoc]] TFLayoutLMForSequenceClassification
+
+## TFLayoutLMForTokenClassification
+
+[[autodoc]] TFLayoutLMForTokenClassification