Migrate model card from transformers-repo

Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/amberoad/bert-multilingual-passage-reranking-msmarco/README.md

README.md

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+---
+language: multilingual
+thumbnail: "https://amberoad.de/images/logo_text.png"
+tags:
+- msmarco
+- multilingual
+- passage reranking
+license:  Apache-2.0
+datasets:
+- msmarco
+metrics:
+- MRR
+widget:
+- query: "What is a corporation?"
+  passage: "A company is incorporated in a specific nation, often within the bounds of a smaller subset of that nation, such as a state or province. The corporation is then governed by the laws of incorporation in that state. A corporation may issue stock, either private or public, or may be classified as a non-stock corporation. If stock is issued, the corporation will usually be governed by its shareholders, either directly or indirectly."
+---
+
+# Passage Reranking Multilingual BERT 🔃 🌍
+
+
+
+## Model description
+**Input:** Supports over 100 Languages. See [List of supported languages](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages) for all available.
+
+**Purpose:** This module takes a search query [1] and a passage [2] and calculates if the passage matches the query. 
+It can be used as an improvement for Elasticsearch Results and boosts the relevancy by up to 100%. 
+
+**Architecture:** On top of BERT there is a Densly Connected NN which takes the 768 Dimensional [CLS] Token as input and provides the output ([Arxiv](https://arxiv.org/abs/1901.04085)).
+
+**Output:** Just a single value between between -10 and 10. Better matching query,passage pairs tend to have a higher a score.
+
+
+
+## Intended uses & limitations
+Both query[1] and passage[2] have to fit in 512 Tokens.
+As you normally want to rerank the first dozens of search results keep in mind the inference time of approximately 300 ms/query.
+
+#### How to use
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("amberoad/bert-multilingual-passage-reranking-msmarco")
+
+model = AutoModelForSequenceClassification.from_pretrained("amberoad/bert-multilingual-passage-reranking-msmarco")
+```
+
+This Model can be used as a drop-in replacement in the [Nboost Library](https://github.com/koursaros-ai/nboost)
+Through this you can directly improve your Elasticsearch Results without any coding. 
+
+
+## Training data
+
+This model is trained using the [**Microsoft MS Marco Dataset**](https://microsoft.github.io/msmarco/ "Microsoft MS Marco"). This training dataset contains approximately 400M tuples of a query, relevant and non-relevant passages. All datasets used for training and evaluating are listed in this [table](https://github.com/microsoft/MSMARCO-Passage-Ranking#data-information-and-formating). The used dataset for training is called *Train Triples Large*, while the evaluation was made on *Top 1000 Dev*. There are 6,900 queries in total in the development dataset, where each query is mapped to top 1,000 passage retrieved using BM25 from MS MARCO corpus. 
+
+## Training procedure
+
+The training is performed the same way as stated in this [README](https://github.com/nyu-dl/dl4marco-bert "NYU Github"). See their excellent Paper on [Arxiv](https://arxiv.org/abs/1901.04085). 
+
+We changed the BERT Model from an English only to the default BERT Multilingual uncased Model from [Google](https://huggingface.co/bert-base-multilingual-uncased).
+
+Training was done 400 000 Steps. This equaled 12 hours an a TPU V3-8.
+
+
+## Eval results
+
+We see nearly similar performance than the English only Model in the English [Bing Queries Dataset](http://www.msmarco.org/). Although the training data is English only internal Tests on private data showed a far higher accurancy in German than all other available models.
+
+
+
+Fine-tuned Models                                                                   | Dependency                                                                   | Eval Set                                                           | Search Boost<a href='#benchmarks'> | Speed on GPU
+----------------------------------------------------------------------------------- | ---------------------------------------------------------------------------- | ------------------------------------------------------------------ | ----------------------------------------------------- | ----------------------------------
+**`amberoad/Multilingual-uncased-MSMARCO`**  (This Model)                                       | <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-blue"/>          |  <a href ='http://www.msmarco.org/'>bing queries</a>               | **+61%** <sub><sup>(0.29 vs 0.18)</sup></sub>         | ~300 ms/query <a href='#footnotes'>
+`nboost/pt-tinybert-msmarco`                                          | <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-red"/>          |  <a href ='http://www.msmarco.org/'>bing queries</a>               | **+45%** <sub><sup>(0.26 vs 0.18)</sup></sub>         | ~50ms/query <a href='#footnotes'>
+`nboost/pt-bert-base-uncased-msmarco`                                               | <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-red"/>          | <a href ='http://www.msmarco.org/'>bing queries</a>                | **+62%** <sub><sup>(0.29 vs 0.18)</sup></sub>         | ~300 ms/query<a href='#footnotes'>
+`nboost/pt-bert-large-msmarco`                                                      | <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-red"/>          | <a href ='http://www.msmarco.org/'>bing queries</a>                | **+77%** <sub><sup>(0.32 vs 0.18)</sup></sub>         | -
+`nboost/pt-biobert-base-msmarco`                                                    | <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-red"/>          | <a href ='https://github.com/naver/biobert-pretrained'>biomed</a>  | **+66%** <sub><sup>(0.17 vs 0.10)</sup></sub>         | ~300 ms/query<a href='#footnotes'>
+
+This table is taken from [nboost](https://github.com/koursaros-ai/nboost) and extended by the first line. 
+
+
+
+## Contact Infos
+
+![](https://amberoad.de/images/logo_text.png)
+
+Amberoad is a company focussing on Search and Business Intelligence. 
+We provide you: 
+* Advanced Internal Company Search Engines thorugh NLP
+* External Search Egnines: Find Competitors, Customers, Suppliers 
+
+**Get in Contact now to benefit from our Expertise:**
+
+The training and evaluation was performed by [**Philipp Reissel**](https://reissel.eu/) and [**Igli Manaj**](https://github.com/iglimanaj) 
+
+ [![Amberoad](https://i.stack.imgur.com/gVE0j.png) Linkedin](https://de.linkedin.com/company/amberoad) | <svg xmlns="http://www.w3.org/2000/svg" x="0px" y="0px"
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