model cards for roberta and bert-multilingual (#5324)
* More model cards (cc @myleott) * Apply suggestions from code review Co-authored-by: Julien Chaumond <chaumond@gmail.com>
This commit is contained in:
Sylvain Gugger
@@ -1,5 +1,152 @@
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---
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language: multilingual
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license: apache-2.0
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datasets:
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- wikipedia
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---
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# BERT multilingual base model (uncased)
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Pretrained model on the top 104 languages with the largest Wikipedia using a masked language modeling (MLM) objective.
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It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in
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[this repository](https://github.com/google-research/bert). This model is case sensitive: it makes a difference
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between english and English.
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Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by
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the Hugging Face team.
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## Model description
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BERT is a transformers model pretrained on a large corpus of multilingual data in a self-supervised fashion. This means
|
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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
|
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was pretrained with two objectives:
|
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|
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- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
|
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the entire masked sentence through the model and has to predict the masked words. This is different from traditional
|
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recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
|
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GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the
|
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sentence.
|
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- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
|
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they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
|
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predict if the two sentences were following each other or not.
|
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|
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This way, the model learns an inner representation of the languages in the training set that can then be used to
|
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extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a
|
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standard classifier using the features produced by the BERT model as inputs.
|
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|
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## Intended uses & limitations
|
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|
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You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
|
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be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for
|
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fine-tuned versions on a task that interests you.
|
||||
|
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Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
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to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
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generation you should look at model like GPT2.
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### How to use
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You can use this model directly with a pipeline for masked language modeling:
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```python
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>>> from transformers import pipeline
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>>> unmasker = pipeline('fill-mask', model='bert-base-multilingual-cased')
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>>> unmasker("Hello I'm a [MASK] model.")
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[{'sequence': "[CLS] Hello I'm a model model. [SEP]",
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'score': 0.10182085633277893,
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'token': 13192,
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'token_str': 'model'},
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{'sequence': "[CLS] Hello I'm a world model. [SEP]",
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'score': 0.052126359194517136,
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'token': 11356,
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'token_str': 'world'},
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{'sequence': "[CLS] Hello I'm a data model. [SEP]",
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'score': 0.048930276185274124,
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'token': 11165,
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'token_str': 'data'},
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{'sequence': "[CLS] Hello I'm a flight model. [SEP]",
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'score': 0.02036019042134285,
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'token': 23578,
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'token_str': 'flight'},
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{'sequence': "[CLS] Hello I'm a business model. [SEP]",
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'score': 0.020079681649804115,
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'token': 14155,
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'token_str': 'business'}]
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```
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Here is how to use this model to get the features of a given text in PyTorch:
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```python
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from transformers import BertTokenizer, BertModel
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tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased')
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model = BertModel.from_pretrained("bert-base-multilingual-cased")
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text = "Replace me by any text you'd like."
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encoded_input = tokenizer(text, return_tensors='pt')
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output = model(**encoded_input)
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```
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and in TensorFlow:
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```python
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from transformers import BertTokenizer, TFBertModel
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tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased')
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model = TFBertModel.from_pretrained("bert-base-multilingual-cased")
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text = "Replace me by any text you'd like."
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encoded_input = tokenizer(text, return_tensors='tf')
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output = model(encoded_input)
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```
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## Training data
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The BERT model was pretrained on the 104 languages with the largest Wikipedias. You can find the complete list
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[here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages).
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## Training procedure
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### Preprocessing
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The texts are lowercased and tokenized using WordPiece and a shared vocabulary size of 110,000. The languages with a
|
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larger Wikipedia are under-sampled and the ones with lower resources are oversampled. For languages like Chinese,
|
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Japanese Kanji and Korean Hanja that don't have space, a CJK Unicode block is added around every character.
|
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|
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The inputs of the model are then of the form:
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|
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```
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[CLS] Sentence A [SEP] Sentence B [SEP]
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```
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With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
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the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
|
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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.
|
||||
|
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The details of the masking procedure for each sentence are the following:
|
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- 15% of the tokens are masked.
|
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- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
|
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- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
|
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- In the 10% remaining cases, the masked tokens are left as is.
|
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### BibTeX entry and citation info
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```bibtex
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@article{DBLP:journals/corr/abs-1810-04805,
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author = {Jacob Devlin and
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Ming{-}Wei Chang and
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Kenton Lee and
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Kristina Toutanova},
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title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
|
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Understanding},
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journal = {CoRR},
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volume = {abs/1810.04805},
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year = {2018},
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url = {http://arxiv.org/abs/1810.04805},
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archivePrefix = {arXiv},
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eprint = {1810.04805},
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timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
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biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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```
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@@ -1,5 +1,209 @@
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---
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language: multilingual
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language: english
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license: apache-2.0
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datasets:
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- wikipedia
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---
|
||||
|
||||
# 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
|
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>>> unmasker = pipeline('fill-mask', model='bert-base-multilingual-uncased')
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>>> unmasker("Hello I'm a [MASK] model.")
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[{'sequence': "[CLS] hello i'm a top model. [SEP]",
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'score': 0.1507750153541565,
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'token': 11397,
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'token_str': 'top'},
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{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
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'score': 0.13075384497642517,
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'token': 23589,
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'token_str': 'fashion'},
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{'sequence': "[CLS] hello i'm a good model. [SEP]",
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'score': 0.036272723227739334,
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'token': 12050,
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'token_str': 'good'},
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{'sequence': "[CLS] hello i'm a new model. [SEP]",
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'score': 0.035954564809799194,
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'token': 10246,
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'token_str': 'new'},
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{'sequence': "[CLS] hello i'm a great model. [SEP]",
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'score': 0.028643041849136353,
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'token': 11838,
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'token_str': 'great'}]
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```
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Here is how to use this model to get the features of a given text in PyTorch:
|
||||
|
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```python
|
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from transformers import BertTokenizer, BertModel
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tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-uncased')
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model = BertModel.from_pretrained("bert-base-multilingual-uncased")
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text = "Replace me by any text you'd like."
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encoded_input = tokenizer(text, return_tensors='pt')
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output = model(**encoded_input)
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```
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and in TensorFlow:
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|
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```python
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from transformers import BertTokenizer, TFBertModel
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tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-uncased')
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model = TFBertModel.from_pretrained("bert-base-multilingual-uncased")
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text = "Replace me by any text you'd like."
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encoded_input = tokenizer(text, return_tensors='tf')
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output = model(encoded_input)
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```
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### Limitations and bias
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Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
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predictions:
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```python
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>>> from transformers import pipeline
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>>> unmasker = pipeline('fill-mask', model='bert-base-multilingual-uncased')
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>>> unmasker("The man worked as a [MASK].")
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[{'sequence': '[CLS] the man worked as a teacher. [SEP]',
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'score': 0.07943806052207947,
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'token': 21733,
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'token_str': 'teacher'},
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{'sequence': '[CLS] the man worked as a lawyer. [SEP]',
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'score': 0.0629938617348671,
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'token': 34249,
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'token_str': 'lawyer'},
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{'sequence': '[CLS] the man worked as a farmer. [SEP]',
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'score': 0.03367974981665611,
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'token': 36799,
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'token_str': 'farmer'},
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{'sequence': '[CLS] the man worked as a journalist. [SEP]',
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'score': 0.03172805905342102,
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'token': 19477,
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'token_str': 'journalist'},
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{'sequence': '[CLS] the man worked as a carpenter. [SEP]',
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'score': 0.031021825969219208,
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'token': 33241,
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'token_str': 'carpenter'}]
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>>> unmasker("The Black woman worked as a [MASK].")
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[{'sequence': '[CLS] the black woman worked as a nurse. [SEP]',
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'score': 0.07045423984527588,
|
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'token': 52428,
|
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'token_str': 'nurse'},
|
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{'sequence': '[CLS] the black woman worked as a teacher. [SEP]',
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'score': 0.05178029090166092,
|
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'token': 21733,
|
||||
'token_str': 'teacher'},
|
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{'sequence': '[CLS] the black woman worked as a lawyer. [SEP]',
|
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'score': 0.032601192593574524,
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'token': 34249,
|
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'token_str': 'lawyer'},
|
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{'sequence': '[CLS] the black woman worked as a slave. [SEP]',
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'score': 0.030507225543260574,
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'token': 31173,
|
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'token_str': 'slave'},
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{'sequence': '[CLS] the black woman worked as a woman. [SEP]',
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'score': 0.027691684663295746,
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'token': 14050,
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'token_str': 'woman'}]
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```
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|
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This bias will also affect all fine-tuned versions of this model.
|
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|
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## 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}
|
||||
}
|
||||
```
|
||||
|
||||
@@ -1,10 +1,234 @@
|
||||
---
|
||||
language: english
|
||||
tags:
|
||||
- exbert
|
||||
|
||||
license: mit
|
||||
datasets:
|
||||
- bookcorpus
|
||||
- wikipedia
|
||||
---
|
||||
|
||||
# RoBERTa base model
|
||||
|
||||
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
|
||||
[this paper](https://arxiv.org/abs/1907.11692) and first released in
|
||||
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). This model is case-sensitive: it
|
||||
makes a difference between english and English.
|
||||
|
||||
Disclaimer: The team releasing RoBERTa did not write a model card for this model so this model card has been written by
|
||||
the Hugging Face team.
|
||||
|
||||
## Model description
|
||||
|
||||
RoBERTa 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 the Masked language modeling (MLM) objective. 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.
|
||||
|
||||
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 masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
|
||||
See the [model hub](https://huggingface.co/models?filter=roberta) 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='roberta-base')
|
||||
>>> unmasker("Hello I'm a <mask> model.")
|
||||
|
||||
[{'sequence': "<s>Hello I'm a male model.</s>",
|
||||
'score': 0.3306540250778198,
|
||||
'token': 2943,
|
||||
'token_str': 'Ġmale'},
|
||||
{'sequence': "<s>Hello I'm a female model.</s>",
|
||||
'score': 0.04655390977859497,
|
||||
'token': 2182,
|
||||
'token_str': 'Ġfemale'},
|
||||
{'sequence': "<s>Hello I'm a professional model.</s>",
|
||||
'score': 0.04232972860336304,
|
||||
'token': 2038,
|
||||
'token_str': 'Ġprofessional'},
|
||||
{'sequence': "<s>Hello I'm a fashion model.</s>",
|
||||
'score': 0.037216778844594955,
|
||||
'token': 2734,
|
||||
'token_str': 'Ġfashion'},
|
||||
{'sequence': "<s>Hello I'm a Russian model.</s>",
|
||||
'score': 0.03253649175167084,
|
||||
'token': 1083,
|
||||
'token_str': 'ĠRussian'}]
|
||||
```
|
||||
|
||||
Here is how to use this model to get the features of a given text in PyTorch:
|
||||
|
||||
```python
|
||||
from transformers import RobertaTokenizer, RobertaModel
|
||||
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
|
||||
model = RobertaModel.from_pretrained('roberta-base')
|
||||
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 RobertaTokenizer, TFRobertaModel
|
||||
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
|
||||
model = TFRobertaModel.from_pretrained('roberta-base')
|
||||
text = "Replace me by any text you'd like."
|
||||
encoded_input = tokenizer(text, return_tensors='tf')
|
||||
output = model(encoded_input)
|
||||
```
|
||||
|
||||
### Limitations and bias
|
||||
|
||||
The training data used for this model contains a lot of unfiltered content from the internet, which is far from
|
||||
neutral. Therefore, the model can have biased predictions:
|
||||
|
||||
```python
|
||||
>>> from transformers import pipeline
|
||||
>>> unmasker = pipeline('fill-mask', model='roberta-base')
|
||||
>>> unmasker("The man worked as a <mask>.")
|
||||
|
||||
[{'sequence': '<s>The man worked as a mechanic.</s>',
|
||||
'score': 0.08702439814805984,
|
||||
'token': 25682,
|
||||
'token_str': 'Ġmechanic'},
|
||||
{'sequence': '<s>The man worked as a waiter.</s>',
|
||||
'score': 0.0819653645157814,
|
||||
'token': 38233,
|
||||
'token_str': 'Ġwaiter'},
|
||||
{'sequence': '<s>The man worked as a butcher.</s>',
|
||||
'score': 0.073323555290699,
|
||||
'token': 32364,
|
||||
'token_str': 'Ġbutcher'},
|
||||
{'sequence': '<s>The man worked as a miner.</s>',
|
||||
'score': 0.046322137117385864,
|
||||
'token': 18678,
|
||||
'token_str': 'Ġminer'},
|
||||
{'sequence': '<s>The man worked as a guard.</s>',
|
||||
'score': 0.040150221437215805,
|
||||
'token': 2510,
|
||||
'token_str': 'Ġguard'}]
|
||||
|
||||
>>> unmasker("The Black woman worked as a <mask>.")
|
||||
|
||||
[{'sequence': '<s>The Black woman worked as a waitress.</s>',
|
||||
'score': 0.22177888453006744,
|
||||
'token': 35698,
|
||||
'token_str': 'Ġwaitress'},
|
||||
{'sequence': '<s>The Black woman worked as a prostitute.</s>',
|
||||
'score': 0.19288744032382965,
|
||||
'token': 36289,
|
||||
'token_str': 'Ġprostitute'},
|
||||
{'sequence': '<s>The Black woman worked as a maid.</s>',
|
||||
'score': 0.06498628109693527,
|
||||
'token': 29754,
|
||||
'token_str': 'Ġmaid'},
|
||||
{'sequence': '<s>The Black woman worked as a secretary.</s>',
|
||||
'score': 0.05375480651855469,
|
||||
'token': 2971,
|
||||
'token_str': 'Ġsecretary'},
|
||||
{'sequence': '<s>The Black woman worked as a nurse.</s>',
|
||||
'score': 0.05245552211999893,
|
||||
'token': 9008,
|
||||
'token_str': 'Ġnurse'}]
|
||||
```
|
||||
|
||||
This bias will also affect all fine-tuned versions of this model.
|
||||
|
||||
## Training data
|
||||
|
||||
The RoBERTa model was pretrained on the reunion of five datasets:
|
||||
- [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books;
|
||||
- [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers) ;
|
||||
- [CC-News](https://commoncrawl.org/2016/10/news-dataset-available/), a dataset containing 63 millions English news
|
||||
articles crawled between September 2016 and February 2019.
|
||||
- [OpenWebText](https://github.com/jcpeterson/openwebtext), an opensource recreation of the WebText dataset used to
|
||||
train GPT-2,
|
||||
- [Stories](https://arxiv.org/abs/1806.02847) a dataset containing a subset of CommonCrawl data filtered to match the
|
||||
story-like style of Winograd schemas.
|
||||
|
||||
Together theses datasets weight 160GB of text.
|
||||
|
||||
## Training procedure
|
||||
|
||||
### Preprocessing
|
||||
|
||||
The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50,000. The inputs of
|
||||
the model take pieces of 512 contiguous token that may span over documents. The beginning of a new document is marked
|
||||
with `<s>` and the end of one by `</s>`
|
||||
|
||||
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.
|
||||
|
||||
Contrary to BERT, the masking is done dynamically during pretraining (e.g., it changes at each epoch and is not fixed).
|
||||
|
||||
### Pretraining
|
||||
|
||||
The model was trained on 1024 V100 GPUs for 500K steps with a batch size of 8K and a sequence length of 512. The
|
||||
optimizer used is Adam with a learning rate of 6e-4, \\(\beta_{1} = 0.9\\), \\(\beta_{2} = 0.98\\) and
|
||||
\\(\epsilon = 1e-6\\), a weight decay of 0.01, learning rate warmup for 24,000 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 | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE |
|
||||
|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|
|
||||
| | 87.6 | 91.9 | 92.8 | 94.8 | 63.6 | 91.2 | 90.2 | 78.7 |
|
||||
|
||||
|
||||
### BibTeX entry and citation info
|
||||
|
||||
```bibtex
|
||||
@article{DBLP:journals/corr/abs-1907-11692,
|
||||
author = {Yinhan Liu and
|
||||
Myle Ott and
|
||||
Naman Goyal and
|
||||
Jingfei Du and
|
||||
Mandar Joshi and
|
||||
Danqi Chen and
|
||||
Omer Levy and
|
||||
Mike Lewis and
|
||||
Luke Zettlemoyer and
|
||||
Veselin Stoyanov},
|
||||
title = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach},
|
||||
journal = {CoRR},
|
||||
volume = {abs/1907.11692},
|
||||
year = {2019},
|
||||
url = {http://arxiv.org/abs/1907.11692},
|
||||
archivePrefix = {arXiv},
|
||||
eprint = {1907.11692},
|
||||
timestamp = {Thu, 01 Aug 2019 08:59:33 +0200},
|
||||
biburl = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib},
|
||||
bibsource = {dblp computer science bibliography, https://dblp.org}
|
||||
}
|
||||
```
|
||||
|
||||
<a href="https://huggingface.co/exbert/?model=roberta-base">
|
||||
<img width="300px" src="https://hf-dinosaur.huggingface.co/exbert/button.png">
|
||||
</a>
|
||||
|
||||
235
model_cards/roberta-large-README.md
Normal file
235
model_cards/roberta-large-README.md
Normal file
@@ -0,0 +1,235 @@
|
||||
---
|
||||
language: english
|
||||
tags:
|
||||
- exbert
|
||||
license: mit
|
||||
datasets:
|
||||
- bookcorpus
|
||||
- wikipedia
|
||||
---
|
||||
|
||||
# RoBERTa large model
|
||||
|
||||
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
|
||||
[this paper](https://arxiv.org/abs/1907.11692) and first released in
|
||||
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). This model is case-sensitive: it
|
||||
makes a difference between english and English.
|
||||
|
||||
Disclaimer: The team releasing RoBERTa did not write a model card for this model so this model card has been written by
|
||||
the Hugging Face team.
|
||||
|
||||
## Model description
|
||||
|
||||
RoBERTa 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 the Masked language modeling (MLM) objective. 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.
|
||||
|
||||
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 masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
|
||||
See the [model hub](https://huggingface.co/models?filter=roberta) 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='roberta-large')
|
||||
>>> unmasker("Hello I'm a <mask> model.")
|
||||
|
||||
[{'sequence': "<s>Hello I'm a male model.</s>",
|
||||
'score': 0.3317350447177887,
|
||||
'token': 2943,
|
||||
'token_str': 'Ġmale'},
|
||||
{'sequence': "<s>Hello I'm a fashion model.</s>",
|
||||
'score': 0.14171843230724335,
|
||||
'token': 2734,
|
||||
'token_str': 'Ġfashion'},
|
||||
{'sequence': "<s>Hello I'm a professional model.</s>",
|
||||
'score': 0.04291723668575287,
|
||||
'token': 2038,
|
||||
'token_str': 'Ġprofessional'},
|
||||
{'sequence': "<s>Hello I'm a freelance model.</s>",
|
||||
'score': 0.02134818211197853,
|
||||
'token': 18150,
|
||||
'token_str': 'Ġfreelance'},
|
||||
{'sequence': "<s>Hello I'm a young model.</s>",
|
||||
'score': 0.021098261699080467,
|
||||
'token': 664,
|
||||
'token_str': 'Ġyoung'}]
|
||||
```
|
||||
|
||||
Here is how to use this model to get the features of a given text in PyTorch:
|
||||
|
||||
```python
|
||||
from transformers import RobertaTokenizer, RobertaModel
|
||||
tokenizer = RobertaTokenizer.from_pretrained('roberta-large')
|
||||
model = RobertaModel.from_pretrained('roberta-large')
|
||||
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 RobertaTokenizer, TFRobertaModel
|
||||
tokenizer = RobertaTokenizer.from_pretrained('roberta-large')
|
||||
model = TFRobertaModel.from_pretrained('roberta-large')
|
||||
text = "Replace me by any text you'd like."
|
||||
encoded_input = tokenizer(text, return_tensors='tf')
|
||||
output = model(encoded_input)
|
||||
```
|
||||
|
||||
### Limitations and bias
|
||||
|
||||
The training data used for this model contains a lot of unfiltered content from the internet, which is far from
|
||||
neutral. Therefore, the model can have biased predictions:
|
||||
|
||||
```python
|
||||
>>> from transformers import pipeline
|
||||
>>> unmasker = pipeline('fill-mask', model='roberta-large')
|
||||
>>> unmasker("The man worked as a <mask>.")
|
||||
|
||||
[{'sequence': '<s>The man worked as a mechanic.</s>',
|
||||
'score': 0.08260300755500793,
|
||||
'token': 25682,
|
||||
'token_str': 'Ġmechanic'},
|
||||
{'sequence': '<s>The man worked as a driver.</s>',
|
||||
'score': 0.05736079439520836,
|
||||
'token': 1393,
|
||||
'token_str': 'Ġdriver'},
|
||||
{'sequence': '<s>The man worked as a teacher.</s>',
|
||||
'score': 0.04709019884467125,
|
||||
'token': 3254,
|
||||
'token_str': 'Ġteacher'},
|
||||
{'sequence': '<s>The man worked as a bartender.</s>',
|
||||
'score': 0.04641604796051979,
|
||||
'token': 33080,
|
||||
'token_str': 'Ġbartender'},
|
||||
{'sequence': '<s>The man worked as a waiter.</s>',
|
||||
'score': 0.04239227622747421,
|
||||
'token': 38233,
|
||||
'token_str': 'Ġwaiter'}]
|
||||
|
||||
>>> unmasker("The woman worked as a <mask>.")
|
||||
|
||||
[{'sequence': '<s>The woman worked as a nurse.</s>',
|
||||
'score': 0.2667474150657654,
|
||||
'token': 9008,
|
||||
'token_str': 'Ġnurse'},
|
||||
{'sequence': '<s>The woman worked as a waitress.</s>',
|
||||
'score': 0.12280137836933136,
|
||||
'token': 35698,
|
||||
'token_str': 'Ġwaitress'},
|
||||
{'sequence': '<s>The woman worked as a teacher.</s>',
|
||||
'score': 0.09747499972581863,
|
||||
'token': 3254,
|
||||
'token_str': 'Ġteacher'},
|
||||
{'sequence': '<s>The woman worked as a secretary.</s>',
|
||||
'score': 0.05783602222800255,
|
||||
'token': 2971,
|
||||
'token_str': 'Ġsecretary'},
|
||||
{'sequence': '<s>The woman worked as a cleaner.</s>',
|
||||
'score': 0.05576248839497566,
|
||||
'token': 16126,
|
||||
'token_str': 'Ġcleaner'}]
|
||||
```
|
||||
|
||||
This bias will also affect all fine-tuned versions of this model.
|
||||
|
||||
## Training data
|
||||
|
||||
The RoBERTa model was pretrained on the reunion of five datasets:
|
||||
- [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books;
|
||||
- [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers) ;
|
||||
- [CC-News](https://commoncrawl.org/2016/10/news-dataset-available/), a dataset containing 63 millions English news
|
||||
articles crawled between September 2016 and February 2019.
|
||||
- [OpenWebText](https://github.com/jcpeterson/openwebtext), an opensource recreation of the WebText dataset used to
|
||||
train GPT-2,
|
||||
- [Stories](https://arxiv.org/abs/1806.02847) a dataset containing a subset of CommonCrawl data filtered to match the
|
||||
story-like style of Winograd schemas.
|
||||
|
||||
Together theses datasets weight 160GB of text.
|
||||
|
||||
## Training procedure
|
||||
|
||||
### Preprocessing
|
||||
|
||||
The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50,000. The inputs of
|
||||
the model take pieces of 512 contiguous token that may span over documents. The beginning of a new document is marked
|
||||
with `<s>` and the end of one by `</s>`
|
||||
|
||||
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.
|
||||
|
||||
Contrary to BERT, the masking is done dynamically during pretraining (e.g., it changes at each epoch and is not fixed).
|
||||
|
||||
### Pretraining
|
||||
|
||||
The model was trained on 1024 V100 GPUs for 500K steps with a batch size of 8K and a sequence length of 512. The
|
||||
optimizer used is Adam with a learning rate of 4e-4, \\(\beta_{1} = 0.9\\), \\(\beta_{2} = 0.98\\) and
|
||||
\\(\epsilon = 1e-6\\), a weight decay of 0.01, learning rate warmup for 30,000 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 | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE |
|
||||
|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|
|
||||
| | 90.2 | 92.2 | 94.7 | 96.4 | 68.0 | 96.4 | 90.9 | 86.6 |
|
||||
|
||||
|
||||
### BibTeX entry and citation info
|
||||
|
||||
```bibtex
|
||||
@article{DBLP:journals/corr/abs-1907-11692,
|
||||
author = {Yinhan Liu and
|
||||
Myle Ott and
|
||||
Naman Goyal and
|
||||
Jingfei Du and
|
||||
Mandar Joshi and
|
||||
Danqi Chen and
|
||||
Omer Levy and
|
||||
Mike Lewis and
|
||||
Luke Zettlemoyer and
|
||||
Veselin Stoyanov},
|
||||
title = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach},
|
||||
journal = {CoRR},
|
||||
volume = {abs/1907.11692},
|
||||
year = {2019},
|
||||
url = {http://arxiv.org/abs/1907.11692},
|
||||
archivePrefix = {arXiv},
|
||||
eprint = {1907.11692},
|
||||
timestamp = {Thu, 01 Aug 2019 08:59:33 +0200},
|
||||
biburl = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib},
|
||||
bibsource = {dblp computer science bibliography, https://dblp.org}
|
||||
}
|
||||
```
|
||||
|
||||
<a href="https://huggingface.co/exbert/?model=roberta-base">
|
||||
<img width="300px" src="https://hf-dinosaur.huggingface.co/exbert/button.png">
|
||||
</a>
|
||||
Reference in New Issue
Block a user