| | --- |
| | language: en |
| | tags: |
| | - exbert |
| | license: apache-2.0 |
| | datasets: |
| | - bookcorpus |
| | - wikipedia |
| | base_model: google-bert/bert-base-uncased |
| | --- |
| | |
| | # BERT base model (cased) |
| |
|
| | 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 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 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-cased') |
| | >>> unmasker("Hello I'm a [MASK] model.") |
| | |
| | [{'sequence': "[CLS] Hello I'm a fashion model. [SEP]", |
| | 'score': 0.09019174426794052, |
| | 'token': 4633, |
| | 'token_str': 'fashion'}, |
| | {'sequence': "[CLS] Hello I'm a new model. [SEP]", |
| | 'score': 0.06349995732307434, |
| | 'token': 1207, |
| | 'token_str': 'new'}, |
| | {'sequence': "[CLS] Hello I'm a male model. [SEP]", |
| | 'score': 0.06228214129805565, |
| | 'token': 2581, |
| | 'token_str': 'male'}, |
| | {'sequence': "[CLS] Hello I'm a professional model. [SEP]", |
| | 'score': 0.0441727414727211, |
| | 'token': 1848, |
| | 'token_str': 'professional'}, |
| | {'sequence': "[CLS] Hello I'm a super model. [SEP]", |
| | 'score': 0.03326151892542839, |
| | 'token': 7688, |
| | 'token_str': 'super'}] |
| | ``` |
| |
|
| | 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-cased') |
| | model = BertModel.from_pretrained("bert-base-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-cased') |
| | model = TFBertModel.from_pretrained("bert-base-cased") |
| | 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-cased') |
| | >>> unmasker("The man worked as a [MASK].") |
| | |
| | [{'sequence': '[CLS] The man worked as a lawyer. [SEP]', |
| | 'score': 0.04804691672325134, |
| | 'token': 4545, |
| | 'token_str': 'lawyer'}, |
| | {'sequence': '[CLS] The man worked as a waiter. [SEP]', |
| | 'score': 0.037494491785764694, |
| | 'token': 17989, |
| | 'token_str': 'waiter'}, |
| | {'sequence': '[CLS] The man worked as a cop. [SEP]', |
| | 'score': 0.035512614995241165, |
| | 'token': 9947, |
| | 'token_str': 'cop'}, |
| | {'sequence': '[CLS] The man worked as a detective. [SEP]', |
| | 'score': 0.031271643936634064, |
| | 'token': 9140, |
| | 'token_str': 'detective'}, |
| | {'sequence': '[CLS] The man worked as a doctor. [SEP]', |
| | 'score': 0.027423162013292313, |
| | 'token': 3995, |
| | 'token_str': 'doctor'}] |
| | |
| | >>> unmasker("The woman worked as a [MASK].") |
| | |
| | [{'sequence': '[CLS] The woman worked as a nurse. [SEP]', |
| | 'score': 0.16927455365657806, |
| | 'token': 7439, |
| | 'token_str': 'nurse'}, |
| | {'sequence': '[CLS] The woman worked as a waitress. [SEP]', |
| | 'score': 0.1501094549894333, |
| | 'token': 15098, |
| | 'token_str': 'waitress'}, |
| | {'sequence': '[CLS] The woman worked as a maid. [SEP]', |
| | 'score': 0.05600163713097572, |
| | 'token': 13487, |
| | 'token_str': 'maid'}, |
| | {'sequence': '[CLS] The woman worked as a housekeeper. [SEP]', |
| | 'score': 0.04838843643665314, |
| | 'token': 26458, |
| | 'token_str': 'housekeeper'}, |
| | {'sequence': '[CLS] The woman worked as a cook. [SEP]', |
| | 'score': 0.029980547726154327, |
| | 'token': 9834, |
| | '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 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} |
| | } |
| | ``` |
| |
|
| | <a href="https://huggingface.co/exbert/?model=bert-base-cased"> |
| | <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> |
| | </a> |
