From da6d483f61953103d8d59abffc4f16adf3d20a8d Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Fri, 11 Dec 2020 22:23:33 +0100 Subject: [PATCH] Migrate model card from transformers-repo Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755 Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/bert-base-multilingual-cased-README.md --- README.md | 152 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 152 insertions(+) create mode 100644 README.md diff --git a/README.md b/README.md new file mode 100644 index 0000000..8a6ed9b --- /dev/null +++ b/README.md @@ -0,0 +1,152 @@ +--- +language: multilingual +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} +} +```