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README.md

@@ -1,256 +0,0 @@
----
-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
-- mn
-- 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
-- th
-- ta
-- tt
-- te
-- tr
-- uk
-- ud
-- uz
-- vi
-- vo
-- war
-- cy
-- fry
-- pnb
-- yo
-license: apache-2.0
-datasets:
-- wikipedia
----
-
-# BERT multilingual base model (cased)
-
-Pretrained model on the top 104 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 case sensitive: it makes 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
-
-You can use this model directly with a pipeline for masked language modeling:
-
-```python
->>> from transformers import pipeline
->>> unmasker = pipeline('fill-mask', model='bert-base-multilingual-cased')
->>> unmasker("Hello I'm a [MASK] model.")
-
-[{'sequence': "[CLS] Hello I'm a model model. [SEP]",
-  'score': 0.10182085633277893,
-  'token': 13192,
-  'token_str': 'model'},
- {'sequence': "[CLS] Hello I'm a world model. [SEP]",
-  'score': 0.052126359194517136,
-  'token': 11356,
-  'token_str': 'world'},
- {'sequence': "[CLS] Hello I'm a data model. [SEP]",
-  'score': 0.048930276185274124,
-  'token': 11165,
-  'token_str': 'data'},
- {'sequence': "[CLS] Hello I'm a flight model. [SEP]",
-  'score': 0.02036019042134285,
-  'token': 23578,
-  'token_str': 'flight'},
- {'sequence': "[CLS] Hello I'm a business model. [SEP]",
-  'score': 0.020079681649804115,
-  'token': 14155,
-  'token_str': 'business'}]
-```
-
-Here is how to use this model to get the features of a given text in PyTorch:
-
-```python
-from transformers import BertTokenizer, BertModel
-tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased')
-model = BertModel.from_pretrained("bert-base-multilingual-cased")
-text = "Replace me by any text you'd like."
-encoded_input = tokenizer(text, return_tensors='pt')
-output = model(**encoded_input)
-```
-
-and in TensorFlow:
-
-```python
-from transformers import BertTokenizer, TFBertModel
-tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased')
-model = TFBertModel.from_pretrained("bert-base-multilingual-cased")
-text = "Replace me by any text you'd like."
-encoded_input = tokenizer(text, return_tensors='tf')
-output = model(encoded_input)
-```
-
-## Training data
-
-The BERT model was pretrained on the 104 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).
-
-## Training procedure
-
-### Preprocessing
-
-The texts are lowercased and tokenized using WordPiece and a shared vocabulary size of 110,000. The languages with a
-larger Wikipedia are under-sampled and the ones with lower resources are oversampled. For languages like Chinese,
-Japanese Kanji and Korean Hanja that don't have space, a CJK Unicode block is added around every character. 
-
-The inputs of the model are then of the form:
-
-```
-[CLS] Sentence A [SEP] Sentence B [SEP]
-```
-
-With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
-the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
-consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
-"sentences" has a combined length of less than 512 tokens.
-
-The details of the masking procedure for each sentence are the following:
-- 15% of the tokens are masked.
-- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
-- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
-- In the 10% remaining cases, the masked tokens are left as is.
-
-
-### 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}
-}
-```

modelcard.json

@@ -0,0 +1,11 @@
+{
+  "caveats_and_recommendations": {},
+  "ethical_considerations": {},
+  "evaluation_data": {},
+  "factors": {},
+  "intended_use": {},
+  "metrics": {},
+  "model_details": {},
+  "quantitative_analyses": {},
+  "training_data": {}
+}