From 92671532e7d9d46e35d15308b76e488cf4e20343 Mon Sep 17 00:00:00 2001 From: sgugger Date: Fri, 26 Jun 2020 10:07:46 -0400 Subject: [PATCH] More model cards --- model_cards/bert-base-cased-README.md | 222 +++++++++++++++++- model_cards/bert-base-uncased-README.md | 4 +- model_cards/distilbert-base-uncased-README.md | 210 ++++++++++++++++- 3 files changed, 432 insertions(+), 4 deletions(-) diff --git a/model_cards/bert-base-cased-README.md b/model_cards/bert-base-cased-README.md index 0b6d067c05..39cfe35ae4 100644 --- a/model_cards/bert-base-cased-README.md +++ b/model_cards/bert-base-cased-README.md @@ -1,10 +1,230 @@ --- +language: english tags: - exbert - license: apache-2.0 +datasets: +- bookcorpus +- wikipedia --- +# 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, 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='pt') +output = model(**encoded_input) +``` + +and in TensorFlow: + +```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='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} +} +``` + diff --git a/model_cards/bert-base-uncased-README.md b/model_cards/bert-base-uncased-README.md index 9de947236b..2a7f1e92fa 100644 --- a/model_cards/bert-base-uncased-README.md +++ b/model_cards/bert-base-uncased-README.md @@ -93,9 +93,9 @@ output = model(**encoded_input) and in TensorFlow: ```python -from transformers import BertTokenizer, BertModel +from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') -model = BertModel.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) diff --git a/model_cards/distilbert-base-uncased-README.md b/model_cards/distilbert-base-uncased-README.md index 3ad023fa18..88977e252a 100644 --- a/model_cards/distilbert-base-uncased-README.md +++ b/model_cards/distilbert-base-uncased-README.md @@ -1,10 +1,218 @@ --- +language: english tags: - exbert - license: apache-2.0 +datasets: +- bookcorpus +- wikipedia --- +# DistilBERT base model (uncased) + +This model is a distilled version of the [BERT base mode](https://huggingface.co/distilbert-base-uncased). It was +introduced in [this paper](https://arxiv.org/abs/1910.01108). The code for the distillation process can be found +[here](https://github.com/huggingface/transformers/tree/master/examples/distillation). This model is uncased: it does +not make a difference between english and English. + +## Model description + +DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a +self-supervised fashion, using the BERT base model as a teacher. 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 using the BERT base model. More precisely, it was pretrained +with three objectives: + +- Distillation loss: the model was trained to return the same probabilities as the BERT base model. +- Masked language modeling (MLM): this is part of the original training loss of the BERT base model. When 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. +- Cosine embedding loss: the model was also trained to generate hidden states as close as possible as the BERT base + model. + +This way, the model learns the same inner representation of the English language than its teacher model, while being +faster for inference or downstream tasks. + +## 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=distilbert) 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='distilbert-base-uncased') +>>> unmasker("Hello I'm a [MASK] model.") + +[{'sequence': "[CLS] hello i'm a role model. [SEP]", + 'score': 0.05292855575680733, + 'token': 2535, + 'token_str': 'role'}, + {'sequence': "[CLS] hello i'm a fashion model. [SEP]", + 'score': 0.03968575969338417, + 'token': 4827, + 'token_str': 'fashion'}, + {'sequence': "[CLS] hello i'm a business model. [SEP]", + 'score': 0.034743521362543106, + 'token': 2449, + 'token_str': 'business'}, + {'sequence': "[CLS] hello i'm a model model. [SEP]", + 'score': 0.03462274372577667, + 'token': 2944, + 'token_str': 'model'}, + {'sequence': "[CLS] hello i'm a modeling model. [SEP]", + 'score': 0.018145186826586723, + 'token': 11643, + 'token_str': 'modeling'}] +``` + +Here is how to use this model to get the features of a given text in PyTorch: + +```python +from transformers import DistilBertTokenizer, DistilBertModel +tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') +model = DistilBertModel.from_pretrained("distilbert-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 DistilBertTokenizer, TFDistilBertModel +tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') +model = TFDistilBertModel.from_pretrained("distilbert-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. It also inherits some of +[the bias of its teacher model](https://huggingface.co/bert-base-uncased#limitations-and-bias). + +```python +>>> from transformers import pipeline +>>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased') +>>> unmasker("The White man worked as a [MASK].") + +[{'sequence': '[CLS] the white man worked as a blacksmith. [SEP]', + 'score': 0.1235365942120552, + 'token': 20987, + 'token_str': 'blacksmith'}, + {'sequence': '[CLS] the white man worked as a carpenter. [SEP]', + 'score': 0.10142576694488525, + 'token': 10533, + 'token_str': 'carpenter'}, + {'sequence': '[CLS] the white man worked as a farmer. [SEP]', + 'score': 0.04985016956925392, + 'token': 7500, + 'token_str': 'farmer'}, + {'sequence': '[CLS] the white man worked as a miner. [SEP]', + 'score': 0.03932540491223335, + 'token': 18594, + 'token_str': 'miner'}, + {'sequence': '[CLS] the white man worked as a butcher. [SEP]', + 'score': 0.03351764753460884, + 'token': 14998, + 'token_str': 'butcher'}] + +>>> unmasker("The Black woman worked as a [MASK].") + +[{'sequence': '[CLS] the black woman worked as a waitress. [SEP]', + 'score': 0.13283951580524445, + 'token': 13877, + 'token_str': 'waitress'}, + {'sequence': '[CLS] the black woman worked as a nurse. [SEP]', + 'score': 0.12586183845996857, + 'token': 6821, + 'token_str': 'nurse'}, + {'sequence': '[CLS] the black woman worked as a maid. [SEP]', + 'score': 0.11708822101354599, + 'token': 10850, + 'token_str': 'maid'}, + {'sequence': '[CLS] the black woman worked as a prostitute. [SEP]', + 'score': 0.11499975621700287, + 'token': 19215, + 'token_str': 'prostitute'}, + {'sequence': '[CLS] the black woman worked as a housekeeper. [SEP]', + 'score': 0.04722772538661957, + 'token': 22583, + 'token_str': 'housekeeper'}] +``` + +This bias will also affect all fine-tuned versions of this model. + +## Training data + +DistilBERT pretrained on the same data as BERT, which is [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 8 16 GB V100 for 90 hours. See the +[training code](https://github.com/huggingface/transformers/tree/master/examples/distillation) for all hyperparameters +details. + +## Evaluation results + +When fine-tuned on downstream tasks, this model achieves the following results: + +Glue test results: + +| Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | Average | +|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:| +| | 82.2 | 88.5 | 89.2 | 91.3 | 51.3 | 85.8 | 87.5 | 59.9 | 77.0 | + + +### BibTeX entry and citation info + +```bibtex +@article{Sanh2019DistilBERTAD, + title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, + author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf}, + journal={ArXiv}, + year={2019}, + volume={abs/1910.01108} +} +``` +