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README.md

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://arxiv.org/abs/1907.11692

Introduction

RoBERTa iterates on BERT's pretraining procedure, including training the model longer, with bigger batches over more data; removing the next sentence prediction objective; training on longer sequences; and dynamically changing the masking pattern applied to the training data. See the associated paper for more details.

What's New:

Pre-trained models

Model Description # params Download
roberta.base RoBERTa using the BERT-base architecture 125M roberta.base.tar.gz
roberta.large RoBERTa using the BERT-large architecture 355M roberta.large.tar.gz
roberta.large.mnli roberta.large finetuned on MNLI 355M roberta.large.mnli.tar.gz
roberta.large.wsc roberta.large finetuned on WSC 355M roberta.large.wsc.tar.gz

Results

GLUE (Wang et al., 2019) (dev set, single model, single-task finetuning)

Model MNLI QNLI QQP RTE SST-2 MRPC CoLA STS-B
roberta.base 87.6 92.8 91.9 78.7 94.8 90.2 63.6 91.2
roberta.large 90.2 94.7 92.2 86.6 96.4 90.9 68.0 92.4
roberta.large.mnli 90.2 - - - - - - -

SuperGLUE (Wang et al., 2019) (dev set, single model, single-task finetuning)

Model BoolQ CB COPA MultiRC RTE WiC WSC
roberta.large 86.9 98.2 94.0 85.7 89.5 75.6 -
roberta.large.wsc - - - - - - 91.3

SQuAD (Rajpurkar et al., 2018) (dev set, no additional data used)

Model SQuAD 1.1 EM/F1 SQuAD 2.0 EM/F1
roberta.large 88.9/94.6 86.5/89.4

RACE (Lai et al., 2017) (test set)

Model Accuracy Middle High
roberta.large 83.2 86.5 81.3

HellaSwag (Zellers et al., 2019) (test set)

Model Overall In-domain Zero-shot ActivityNet WikiHow
roberta.large 85.2 87.3 83.1 74.6 90.9

Commonsense QA (Talmor et al., 2019) (test set)

Model Accuracy
roberta.large (single model) 72.1
roberta.large (ensemble) 72.5

Winogrande (Sakaguchi et al., 2019) (test set)

Model Accuracy
roberta.large 78.1

XNLI (Conneau et al., 2018) (TRANSLATE-TEST)

Model en fr es de el bg ru tr ar vi th zh hi sw ur
roberta.large.mnli 91.3 82.91 84.27 81.24 81.74 83.13 78.28 76.79 76.64 74.17 74.05 77.5 70.9 66.65 66.81

Example usage

Load RoBERTa from torch.hub (PyTorch >= 1.1):
import torch
roberta = torch.hub.load('pytorch/fairseq', 'roberta.large')
roberta.eval()  # disable dropout (or leave in train mode to finetune)
Load RoBERTa (for PyTorch 1.0 or custom models):
# Download roberta.large model
wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
tar -xzvf roberta.large.tar.gz

# Load the model in fairseq
from fairseq.models.roberta import RobertaModel
roberta = RobertaModel.from_pretrained('/path/to/roberta.large', checkpoint_file='model.pt')
roberta.eval()  # disable dropout (or leave in train mode to finetune)
Apply Byte-Pair Encoding (BPE) to input text:
tokens = roberta.encode('Hello world!')
assert tokens.tolist() == [0, 31414, 232, 328, 2]
roberta.decode(tokens)  # 'Hello world!'
Extract features from RoBERTa:
# Extract the last layer's features
last_layer_features = roberta.extract_features(tokens)
assert last_layer_features.size() == torch.Size([1, 5, 1024])

# Extract all layer's features (layer 0 is the embedding layer)
all_layers = roberta.extract_features(tokens, return_all_hiddens=True)
assert len(all_layers) == 25
assert torch.all(all_layers[-1] == last_layer_features)
Use RoBERTa for sentence-pair classification tasks:
# Download RoBERTa already finetuned for MNLI
roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.mnli')
roberta.eval()  # disable dropout for evaluation

# Encode a pair of sentences and make a prediction
tokens = roberta.encode('Roberta is a heavily optimized version of BERT.', 'Roberta is not very optimized.')
roberta.predict('mnli', tokens).argmax()  # 0: contradiction

# Encode another pair of sentences
tokens = roberta.encode('Roberta is a heavily optimized version of BERT.', 'Roberta is based on BERT.')
roberta.predict('mnli', tokens).argmax()  # 2: entailment
Register a new (randomly initialized) classification head:
roberta.register_classification_head('new_task', num_classes=3)
logprobs = roberta.predict('new_task', tokens)  # tensor([[-1.1050, -1.0672, -1.1245]], grad_fn=<LogSoftmaxBackward>)
Batched prediction:
import torch
from fairseq.data.data_utils import collate_tokens

roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.mnli')
roberta.eval()

batch_of_pairs = [
    ['Roberta is a heavily optimized version of BERT.', 'Roberta is not very optimized.'],
    ['Roberta is a heavily optimized version of BERT.', 'Roberta is based on BERT.'],
    ['potatoes are awesome.', 'I like to run.'],
    ['Mars is very far from earth.', 'Mars is very close.'],
]

batch = collate_tokens(
    [roberta.encode(pair[0], pair[1]) for pair in batch_of_pairs], pad_idx=1
)

logprobs = roberta.predict('mnli', batch)
print(logprobs.argmax(dim=1))
# tensor([0, 2, 1, 0])
Using the GPU:
roberta.cuda()
roberta.predict('new_task', tokens)  # tensor([[-1.1050, -1.0672, -1.1245]], device='cuda:0', grad_fn=<LogSoftmaxBackward>)

Advanced usage

Filling masks:

RoBERTa can be used to fill <mask> tokens in the input. Some examples from the Natural Questions dataset:

roberta.fill_mask('The first Star wars movie came out in <mask>', topk=3)
# [('The first Star wars movie came out in 1977', 0.9504708051681519, ' 1977'), ('The first Star wars movie came out in 1978', 0.009986862540245056, ' 1978'), ('The first Star wars movie came out in 1979', 0.009574787691235542, ' 1979')]

roberta.fill_mask('Vikram samvat calender is official in <mask>', topk=3)
# [('Vikram samvat calender is official in India', 0.21878819167613983, ' India'), ('Vikram samvat calender is official in Delhi', 0.08547237515449524, ' Delhi'), ('Vikram samvat calender is official in Gujarat', 0.07556215673685074, ' Gujarat')]

roberta.fill_mask('<mask> is the common currency of the European Union', topk=3)
# [('Euro is the common currency of the European Union', 0.9456493854522705, 'Euro'), ('euro is the common currency of the European Union', 0.025748178362846375, 'euro'), ('€ is the common currency of the European Union', 0.011183084920048714, '€')]

Pronoun disambiguation (Winograd Schema Challenge):

RoBERTa can be used to disambiguate pronouns. First install spaCy and download the English-language model:

pip install spacy
python -m spacy download en_core_web_lg

Next load the roberta.large.wsc model and call the disambiguate_pronoun function. The pronoun should be surrounded by square brackets ([]) and the query referent surrounded by underscores (_), or left blank to return the predicted candidate text directly:

roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.wsc', user_dir='examples/roberta/wsc')
roberta.cuda()  # use the GPU (optional)

roberta.disambiguate_pronoun('The _trophy_ would not fit in the brown suitcase because [it] was too big.')
# True
roberta.disambiguate_pronoun('The trophy would not fit in the brown _suitcase_ because [it] was too big.')
# False

roberta.disambiguate_pronoun('The city councilmen refused the demonstrators a permit because [they] feared violence.')
# 'The city councilmen'
roberta.disambiguate_pronoun('The city councilmen refused the demonstrators a permit because [they] advocated violence.')
# 'demonstrators'

See the RoBERTA Winograd Schema Challenge (WSC) README for more details on how to train this model.

Extract features aligned to words:

By default RoBERTa outputs one feature vector per BPE token. You can instead realign the features to match spaCy's word-level tokenization with the extract_features_aligned_to_words method. This will compute a weighted average of the BPE-level features for each word and expose them in spaCy's Token.vector attribute:

doc = roberta.extract_features_aligned_to_words('I said, "hello RoBERTa."')
assert len(doc) == 10
for tok in doc:
    print('{:10}{} (...)'.format(str(tok), tok.vector[:5]))
# <s>       tensor([-0.1316, -0.0386, -0.0832, -0.0477,  0.1943], grad_fn=<SliceBackward>) (...)
# I         tensor([ 0.0559,  0.1541, -0.4832,  0.0880,  0.0120], grad_fn=<SliceBackward>) (...)
# said      tensor([-0.1565, -0.0069, -0.8915,  0.0501, -0.0647], grad_fn=<SliceBackward>) (...)
# ,         tensor([-0.1318, -0.0387, -0.0834, -0.0477,  0.1944], grad_fn=<SliceBackward>) (...)
# "         tensor([-0.0486,  0.1818, -0.3946, -0.0553,  0.0981], grad_fn=<SliceBackward>) (...)
# hello     tensor([ 0.0079,  0.1799, -0.6204, -0.0777, -0.0923], grad_fn=<SliceBackward>) (...)
# RoBERTa   tensor([-0.2339, -0.1184, -0.7343, -0.0492,  0.5829], grad_fn=<SliceBackward>) (...)
# .         tensor([-0.1341, -0.1203, -0.1012, -0.0621,  0.1892], grad_fn=<SliceBackward>) (...)
# "         tensor([-0.1341, -0.1203, -0.1012, -0.0621,  0.1892], grad_fn=<SliceBackward>) (...)
# </s>      tensor([-0.0930, -0.0392, -0.0821,  0.0158,  0.0649], grad_fn=<SliceBackward>) (...)

Evaluating the roberta.large.mnli model:

Example python code snippet to evaluate accuracy on the MNLI dev_matched set.

label_map = {0: 'contradiction', 1: 'neutral', 2: 'entailment'}
ncorrect, nsamples = 0, 0
roberta.cuda()
roberta.eval()
with open('glue_data/MNLI/dev_matched.tsv') as fin:
    fin.readline()
    for index, line in enumerate(fin):
        tokens = line.strip().split('\t')
        sent1, sent2, target = tokens[8], tokens[9], tokens[-1]
        tokens = roberta.encode(sent1, sent2)
        prediction = roberta.predict('mnli', tokens).argmax().item()
        prediction_label = label_map[prediction]
        ncorrect += int(prediction_label == target)
        nsamples += 1
print('| Accuracy: ', float(ncorrect)/float(nsamples))
# Expected output: 0.9060

Finetuning

Pretraining using your own data

See the tutorial for pretraining RoBERTa using your own data.

Citation

@article{liu2019roberta,
    title = {RoBERTa: A Robustly Optimized BERT Pretraining Approach},
    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},
    journal={arXiv preprint arXiv:1907.11692},
    year = {2019},
}