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# 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} | |
} | |
``` |