Pushing the model to Huggingace hub

README.md

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+---
+language: 
+- multilingual
+- af
+- sq
+- ar
+- an
+- hy
+- ast
+- az
+- ba
+- eu
+- bar
+- be
+- bn
+- inc
+- bs
+- br
+- bg
+- my
+- ca
+- ceb
+- ce
+- zh
+- cv
+- hr
+- cs
+- da
+- nl
+- en
+- et
+- fi
+- fr
+- gl
+- ka
+- de
+- el
+- gu
+- ht
+- he
+- hi
+- hu
+- is
+- io
+- id
+- ga
+- it
+- ja
+- jv
+- kn
+- kk
+- ky
+- ko
+- la
+- lv
+- lt
+- roa
+- nds
+- lm
+- mk
+- mg
+- ms
+- ml
+- mr
+- min
+- ne
+- new
+- nb
+- nn
+- oc
+- fa
+- pms
+- pl
+- pt
+- pa
+- ro
+- ru
+- sco
+- sr
+- hr
+- scn
+- sk
+- sl
+- aze
+- es
+- su
+- sw
+- sv
+- tl
+- tg
+- ta
+- tt
+- te
+- tr
+- uk
+- ud
+- uz
+- vi
+- vo
+- war
+- cy
+- fry
+- pnb
+- yo
+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"
+width="32" height="32"
+viewBox="0 0 172 172"
+style=" fill:#000000;"><g fill="none" fill-rule="nonzero" stroke="none" stroke-width="1" stroke-linecap="butt" stroke-linejoin="miter" stroke-miterlimit="10" stroke-dasharray="" stroke-dashoffset="0" font-family="none" font-weight="none" font-size="none" text-anchor="none" style="mix-blend-mode: normal"><path d="M0,172v-172h172v172z" fill="none"></path><g fill="#e67e22"><path d="M37.625,21.5v86h96.75v-86h-5.375zM48.375,32.25h10.75v10.75h-10.75zM69.875,32.25h10.75v10.75h-10.75zM91.375,32.25h32.25v10.75h-32.25zM48.375,53.75h75.25v43h-75.25zM80.625,112.875v17.61572c-1.61558,0.93921 -2.94506,2.2687 -3.88428,3.88428h-49.86572v10.75h49.86572c1.8612,3.20153 5.28744,5.375 9.25928,5.375c3.97183,0 7.39808,-2.17347 9.25928,-5.375h49.86572v-10.75h-49.86572c-0.93921,-1.61558 -2.2687,-2.94506 -3.88428,-3.88428v-17.61572z"></path></g></g></svg>[Homepage](https://de.linkedin.com/company/amberoad) |  [Email](info@amberoad.de)
+
+
+
+

config.json

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+  ],
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+  "directionality": "bidi",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
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+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
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+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
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+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "type_vocab_size": 2,
+  "vocab_size": 105879
+}

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special_tokens_map.json

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tf_model.h5

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tokenizer_config.json

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