From 695e7a39c02730106a8dcc0f3bb9c1c9ba11e408 Mon Sep 17 00:00:00 2001 From: xxl <505279206@qq.com> Date: Fri, 15 Nov 2024 14:57:22 +0800 Subject: [PATCH] first commit --- README.md | 312 +++++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 310 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index a987241..0ffe67b 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,311 @@ -# bert-base-multilingual-uncased_a13593100094533632962194 +--- +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-base-multilingual-uncased \ No newline at end of file +# 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} +} +```