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README.md
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README.md
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# bert-base-multilingual-uncased_a13593100094533632962194
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---
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language:
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- multilingual
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- af
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- sq
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- ar
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- an
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- hy
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- ast
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- az
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- ba
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- eu
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- bar
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- be
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- bn
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- inc
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- bs
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- br
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- bg
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- my
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- ca
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- ceb
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- ce
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- zh
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- cv
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- hr
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- cs
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- da
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- nl
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- en
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- et
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- fi
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- fr
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- gl
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- ka
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- de
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- el
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- gu
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- ht
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- he
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- hi
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- hu
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- is
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- io
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- id
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- ga
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- it
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- ja
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- jv
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- kn
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- kk
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- ky
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- ko
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- la
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- lv
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- lt
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- roa
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- nds
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- lm
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- mk
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- mg
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- ms
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- ml
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- mr
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- min
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- ne
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- new
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- nb
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- nn
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- oc
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- fa
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- pms
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- pl
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- pt
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- pa
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- ro
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- ru
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- sco
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- sr
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- hr
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- scn
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- sk
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- sl
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- aze
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- es
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- su
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- sw
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- sv
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- tl
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- tg
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- ta
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- tt
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- te
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- tr
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- uk
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- ud
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- uz
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- vi
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- vo
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- war
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- cy
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- fry
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- pnb
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- yo
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license: apache-2.0
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datasets:
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- wikipedia
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---
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bert-base-multilingual-uncased
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# BERT multilingual base model (uncased)
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Pretrained model on the top 102 languages with the largest Wikipedia using a masked language modeling (MLM) objective.
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It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in
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[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
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between english and English.
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Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by
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the Hugging Face team.
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## Model description
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BERT is a transformers model pretrained on a large corpus of multilingual data in a self-supervised fashion. This means
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it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
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publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
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was pretrained with two objectives:
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- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
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the entire masked sentence through the model and has to predict the masked words. This is different from traditional
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recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
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GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the
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sentence.
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- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
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they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
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predict if the two sentences were following each other or not.
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This way, the model learns an inner representation of the languages in the training set that can then be used to
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extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a
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standard classifier using the features produced by the BERT model as inputs.
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## Intended uses & limitations
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You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
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be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for
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fine-tuned versions on a task that interests you.
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Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
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to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
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generation you should look at model like GPT2.
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### How to use
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You can use this model directly with a pipeline for masked language modeling:
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```python
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>>> from transformers import pipeline
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>>> unmasker = pipeline('fill-mask', model='bert-base-multilingual-uncased')
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>>> unmasker("Hello I'm a [MASK] model.")
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[{'sequence': "[CLS] hello i'm a top model. [SEP]",
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'score': 0.1507750153541565,
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'token': 11397,
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'token_str': 'top'},
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{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
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'score': 0.13075384497642517,
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'token': 23589,
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'token_str': 'fashion'},
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{'sequence': "[CLS] hello i'm a good model. [SEP]",
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'score': 0.036272723227739334,
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'token': 12050,
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'token_str': 'good'},
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{'sequence': "[CLS] hello i'm a new model. [SEP]",
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'score': 0.035954564809799194,
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'token': 10246,
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'token_str': 'new'},
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{'sequence': "[CLS] hello i'm a great model. [SEP]",
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'score': 0.028643041849136353,
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'token': 11838,
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'token_str': 'great'}]
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```
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Here is how to use this model to get the features of a given text in PyTorch:
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```python
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from transformers import BertTokenizer, BertModel
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tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-uncased')
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model = BertModel.from_pretrained("bert-base-multilingual-uncased")
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text = "Replace me by any text you'd like."
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encoded_input = tokenizer(text, return_tensors='pt')
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output = model(**encoded_input)
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```
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and in TensorFlow:
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```python
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from transformers import BertTokenizer, TFBertModel
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tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-uncased')
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model = TFBertModel.from_pretrained("bert-base-multilingual-uncased")
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text = "Replace me by any text you'd like."
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encoded_input = tokenizer(text, return_tensors='tf')
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output = model(encoded_input)
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```
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### Limitations and bias
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Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
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predictions:
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```python
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>>> from transformers import pipeline
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>>> unmasker = pipeline('fill-mask', model='bert-base-multilingual-uncased')
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>>> unmasker("The man worked as a [MASK].")
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[{'sequence': '[CLS] the man worked as a teacher. [SEP]',
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'score': 0.07943806052207947,
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'token': 21733,
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'token_str': 'teacher'},
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{'sequence': '[CLS] the man worked as a lawyer. [SEP]',
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'score': 0.0629938617348671,
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'token': 34249,
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'token_str': 'lawyer'},
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{'sequence': '[CLS] the man worked as a farmer. [SEP]',
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'score': 0.03367974981665611,
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'token': 36799,
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'token_str': 'farmer'},
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{'sequence': '[CLS] the man worked as a journalist. [SEP]',
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'score': 0.03172805905342102,
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'token': 19477,
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'token_str': 'journalist'},
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{'sequence': '[CLS] the man worked as a carpenter. [SEP]',
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'score': 0.031021825969219208,
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'token': 33241,
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'token_str': 'carpenter'}]
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>>> unmasker("The Black woman worked as a [MASK].")
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[{'sequence': '[CLS] the black woman worked as a nurse. [SEP]',
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'score': 0.07045423984527588,
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'token': 52428,
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'token_str': 'nurse'},
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{'sequence': '[CLS] the black woman worked as a teacher. [SEP]',
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'score': 0.05178029090166092,
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'token': 21733,
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'token_str': 'teacher'},
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{'sequence': '[CLS] the black woman worked as a lawyer. [SEP]',
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'score': 0.032601192593574524,
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'token': 34249,
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'token_str': 'lawyer'},
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{'sequence': '[CLS] the black woman worked as a slave. [SEP]',
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'score': 0.030507225543260574,
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'token': 31173,
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'token_str': 'slave'},
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{'sequence': '[CLS] the black woman worked as a woman. [SEP]',
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'score': 0.027691684663295746,
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'token': 14050,
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'token_str': 'woman'}]
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```
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This bias will also affect all fine-tuned versions of this model.
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## Training data
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The BERT model was pretrained on the 102 languages with the largest Wikipedias. You can find the complete list
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[here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages).
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## Training procedure
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### Preprocessing
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The texts are lowercased and tokenized using WordPiece and a shared vocabulary size of 110,000. The languages with a
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larger Wikipedia are under-sampled and the ones with lower resources are oversampled. For languages like Chinese,
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Japanese Kanji and Korean Hanja that don't have space, a CJK Unicode block is added around every character.
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The inputs of the model are then of the form:
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```
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[CLS] Sentence A [SEP] Sentence B [SEP]
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```
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With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
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the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
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consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
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"sentences" has a combined length of less than 512 tokens.
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The details of the masking procedure for each sentence are the following:
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- 15% of the tokens are masked.
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- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
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- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
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- In the 10% remaining cases, the masked tokens are left as is.
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### BibTeX entry and citation info
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```bibtex
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@article{DBLP:journals/corr/abs-1810-04805,
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author = {Jacob Devlin and
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Ming{-}Wei Chang and
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Kenton Lee and
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Kristina Toutanova},
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title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
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Understanding},
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journal = {CoRR},
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volume = {abs/1810.04805},
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year = {2018},
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url = {http://arxiv.org/abs/1810.04805},
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archivePrefix = {arXiv},
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eprint = {1810.04805},
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timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
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biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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```
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