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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
+license: apache-2.0
+datasets:
+- wikipedia
+---
+
+# BERT multilingual base model (uncased)
+
+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
+
+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-uncased')
+>>> unmasker("Hello I'm a [MASK] model.")
+
+[{'sequence': "[CLS] hello i'm a top model. [SEP]",
+  'score': 0.1507750153541565,
+  'token': 11397,
+  'token_str': 'top'},
+ {'sequence': "[CLS] hello i'm a fashion model. [SEP]",
+  'score': 0.13075384497642517,
+  'token': 23589,
+  'token_str': 'fashion'},
+ {'sequence': "[CLS] hello i'm a good model. [SEP]",
+  'score': 0.036272723227739334,
+  'token': 12050,
+  'token_str': 'good'},
+ {'sequence': "[CLS] hello i'm a new model. [SEP]",
+  'score': 0.035954564809799194,
+  'token': 10246,
+  'token_str': 'new'},
+ {'sequence': "[CLS] hello i'm a great model. [SEP]",
+  'score': 0.028643041849136353,
+  'token': 11838,
+  'token_str': 'great'}]
+```
+
+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-uncased')
+model = BertModel.from_pretrained("bert-base-multilingual-uncased")
+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-uncased')
+model = TFBertModel.from_pretrained("bert-base-multilingual-uncased")
+text = "Replace me by any text you'd like."
+encoded_input = tokenizer(text, return_tensors='tf')
+output = model(encoded_input)
+```
+
+### Limitations and bias
+
+Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
+predictions:
+
+```python
+>>> from transformers import pipeline
+>>> unmasker = pipeline('fill-mask', model='bert-base-multilingual-uncased')
+>>> unmasker("The man worked as a [MASK].")
+
+[{'sequence': '[CLS] the man worked as a teacher. [SEP]',
+  'score': 0.07943806052207947,
+  'token': 21733,
+  'token_str': 'teacher'},
+ {'sequence': '[CLS] the man worked as a lawyer. [SEP]',
+  'score': 0.0629938617348671,
+  'token': 34249,
+  'token_str': 'lawyer'},
+ {'sequence': '[CLS] the man worked as a farmer. [SEP]',
+  'score': 0.03367974981665611,
+  'token': 36799,
+  'token_str': 'farmer'},
+ {'sequence': '[CLS] the man worked as a journalist. [SEP]',
+  'score': 0.03172805905342102,
+  'token': 19477,
+  'token_str': 'journalist'},
+ {'sequence': '[CLS] the man worked as a carpenter. [SEP]',
+  'score': 0.031021825969219208,
+  'token': 33241,
+  'token_str': 'carpenter'}]
+
+>>> unmasker("The Black woman worked as a [MASK].")
+
+[{'sequence': '[CLS] the black woman worked as a nurse. [SEP]',
+  'score': 0.07045423984527588,
+  'token': 52428,
+  'token_str': 'nurse'},
+ {'sequence': '[CLS] the black woman worked as a teacher. [SEP]',
+  'score': 0.05178029090166092,
+  'token': 21733,
+  'token_str': 'teacher'},
+ {'sequence': '[CLS] the black woman worked as a lawyer. [SEP]',
+  'score': 0.032601192593574524,
+  'token': 34249,
+  'token_str': 'lawyer'},
+ {'sequence': '[CLS] the black woman worked as a slave. [SEP]',
+  'score': 0.030507225543260574,
+  'token': 31173,
+  'token_str': 'slave'},
+ {'sequence': '[CLS] the black woman worked as a woman. [SEP]',
+  'score': 0.027691684663295746,
+  'token': 14050,
+  'token_str': 'woman'}]
+```
+
+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).
+
+## 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}
+}
+```

config.json

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+{
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "type_vocab_size": 2,
+  "vocab_size": 105879
+}

gitattributes

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+*.ot filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+model.safetensors filter=lfs diff=lfs merge=lfs -text

pytorch_model.bin

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tokenizer_config.json

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+{"do_lower_case": true, "model_max_length": 512}