Update README.md

README.md

@@ -1,9 +1,205 @@
 ---
+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
+license: apache-2.0
+datasets:
+- wikipedia
 library_name: transformers.js
 base_model:
 - google-bert/bert-base-multilingual-uncased
 ---
 
-# bert-base-multilingual-uncased (ONNX)
+# BERT multilingual base model (uncased)
 
-This is an ONNX version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
+Pretrained model on the top 102 languages with the largest Wikipedia using a masked language modeling (MLM) objective.
+It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in
+[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
+between english and English.
+
+Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by
+the Hugging Face team.
+
+## Model description
+
+BERT is a transformers model pretrained on a large corpus of multilingual data in a self-supervised fashion. This means
+it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
+publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
+was pretrained with two objectives:
+
+- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
+  the entire masked sentence through the model and has to predict the masked words. This is different from traditional
+  recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
+  GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the
+  sentence.
+- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
+  they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
+  predict if the two sentences were following each other or not.
+
+This way, the model learns an inner representation of the languages in the training set that can then be used to
+extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a
+standard classifier using the features produced by the BERT model as inputs.
+
+## Intended uses & limitations
+
+You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
+be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for
+fine-tuned versions on a task that interests you.
+
+Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
+to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
+generation you should look at model like GPT2.
+
+### How to use
+
+If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
+```bash
+npm i @huggingface/transformers
+```
+
+You can then use this model directly with a pipeline for masked language modeling:
+
+```js
+import { pipeline } from "@huggingface/transformers";
+
+const unmasker = await pipeline("fill-mask", "onnx-community/bert-base-multilingual-uncased-ONNX");
+const result = await unmasker("The capital of France is [MASK].", { top_k: 3 });
+console.log(result);
+// [
+//   { score: 0.2571190595626831, token: 10718, token_str: 'paris', sequence: 'the capital of france is paris.' },
+//   { score: 0.15583284199237823, token: 18254, token_str: 'lyon', sequence: 'the capital of france is lyon.' },
+//   { score: 0.06897224485874176, token: 25091, token_str: 'bordeaux', sequence: 'the capital of france is bordeaux.' }
+// ]
+```
+
+### Limitations and bias
+
+Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
+predictions. This bias will also affect all fine-tuned versions of this model.
+
+## Training data
+
+The BERT model was pretrained on the 102 languages with the largest Wikipedias. You can find the complete list
+[here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages).
+
+### BibTeX entry and citation info
+
+```bibtex
+@article{DBLP:journals/corr/abs-1810-04805,
+  author    = {Jacob Devlin and
+               Ming{-}Wei Chang and
+               Kenton Lee and
+               Kristina Toutanova},
+  title     = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
+               Understanding},
+  journal   = {CoRR},
+  volume    = {abs/1810.04805},
+  year      = {2018},
+  url       = {http://arxiv.org/abs/1810.04805},
+  archivePrefix = {arXiv},
+  eprint    = {1810.04805},
+  timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```