diff --git a/README.md b/README.md new file mode 100644 index 0000000..52f4ab6 --- /dev/null +++ b/README.md @@ -0,0 +1,231 @@ +--- +language: en +tags: +- exbert +license: apache-2.0 +datasets: +- bookcorpus +- wikipedia +--- + +# BERT base model (uncased) + +Pretrained model on English language 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 English 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 English language 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-uncased') +>>> unmasker("Hello I'm a [MASK] model.") + +[{'sequence': "[CLS] hello i'm a fashion model. [SEP]", + 'score': 0.1073106899857521, + 'token': 4827, + 'token_str': 'fashion'}, + {'sequence': "[CLS] hello i'm a role model. [SEP]", + 'score': 0.08774490654468536, + 'token': 2535, + 'token_str': 'role'}, + {'sequence': "[CLS] hello i'm a new model. [SEP]", + 'score': 0.05338378623127937, + 'token': 2047, + 'token_str': 'new'}, + {'sequence': "[CLS] hello i'm a super model. [SEP]", + 'score': 0.04667217284440994, + 'token': 3565, + 'token_str': 'super'}, + {'sequence': "[CLS] hello i'm a fine model. [SEP]", + 'score': 0.027095865458250046, + 'token': 2986, + 'token_str': 'fine'}] +``` + +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-uncased') +model = BertModel.from_pretrained("bert-base-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-uncased') +model = TFBertModel.from_pretrained("bert-base-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-uncased') +>>> unmasker("The man worked as a [MASK].") + +[{'sequence': '[CLS] the man worked as a carpenter. [SEP]', + 'score': 0.09747550636529922, + 'token': 10533, + 'token_str': 'carpenter'}, + {'sequence': '[CLS] the man worked as a waiter. [SEP]', + 'score': 0.0523831807076931, + 'token': 15610, + 'token_str': 'waiter'}, + {'sequence': '[CLS] the man worked as a barber. [SEP]', + 'score': 0.04962705448269844, + 'token': 13362, + 'token_str': 'barber'}, + {'sequence': '[CLS] the man worked as a mechanic. [SEP]', + 'score': 0.03788609802722931, + 'token': 15893, + 'token_str': 'mechanic'}, + {'sequence': '[CLS] the man worked as a salesman. [SEP]', + 'score': 0.037680890411138535, + 'token': 18968, + 'token_str': 'salesman'}] + +>>> unmasker("The woman worked as a [MASK].") + +[{'sequence': '[CLS] the woman worked as a nurse. [SEP]', + 'score': 0.21981462836265564, + 'token': 6821, + 'token_str': 'nurse'}, + {'sequence': '[CLS] the woman worked as a waitress. [SEP]', + 'score': 0.1597415804862976, + 'token': 13877, + 'token_str': 'waitress'}, + {'sequence': '[CLS] the woman worked as a maid. [SEP]', + 'score': 0.1154729500412941, + 'token': 10850, + 'token_str': 'maid'}, + {'sequence': '[CLS] the woman worked as a prostitute. [SEP]', + 'score': 0.037968918681144714, + 'token': 19215, + 'token_str': 'prostitute'}, + {'sequence': '[CLS] the woman worked as a cook. [SEP]', + 'score': 0.03042375110089779, + 'token': 5660, + 'token_str': 'cook'}] +``` + +This bias will also affect all fine-tuned versions of this model. + +## Training data + +The BERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 +unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and +headers). + +## Training procedure + +### Preprocessing + +The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. 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. + +### Pretraining + +The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size +of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer +used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, +learning rate warmup for 10,000 steps and linear decay of the learning rate after. + +## Evaluation results + +When fine-tuned on downstream tasks, this model achieves the following results: + +Glue test results: + +| Task | MNLI-(m/mm) | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | Average | +|:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:| +| | 84.6/83.4 | 71.2 | 90.5 | 93.5 | 52.1 | 85.8 | 88.9 | 66.4 | 79.6 | + + +### 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} +} +``` + + + +