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.

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

Example usage (torch.hub)

Load RoBERTa:

>>> import torch
>>> roberta = torch.hub.load('pytorch/fairseq', 'roberta.large')
>>> roberta.eval()  # disable dropout (or leave in train mode to finetune)

Apply Byte-Pair Encoding (BPE) to input text:

>>> tokens = roberta.encode('Hello world!')
>>> tokens
tensor([    0, 31414,   232,   328,     2])

Extract features from RoBERTa:

>>> last_layer_features = roberta.extract_features(tokens)
>>> last_layer_features.size()
torch.Size([1, 5, 1024])

>>> all_layers = roberta.extract_features(tokens, return_all_hiddens=True)
>>> len(all_layers)
25

>>> torch.all(all_layers[-1] == last_layer_features)
tensor(1, dtype=torch.uint8)

Use RoBERTa for sentence-pair classification tasks:

>>> roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.mnli')  # already finetuned
>>> roberta.eval()  # disable dropout for evaluation

>>> tokens = roberta.encode(
...   'Roberta is a heavily optimized version of BERT.',
...   'Roberta is not very optimized.'
... )

>>> roberta.predict('mnli', tokens).argmax()
tensor(0)  # contradiction

>>> tokens = roberta.encode(
...   'Roberta is a heavily optimized version of BERT.',
...   'Roberta is based on BERT.'
... )

>>> roberta.predict('mnli', tokens).argmax()
tensor(2)  # entailment

Register a new (randomly initialized) classification head:

>>> roberta.register_classification_head('new_task', num_classes=3)
>>> roberta.predict('new_task', tokens)
tensor([[-1.1050, -1.0672, -1.1245]], grad_fn=<LogSoftmaxBackward>)

Using the GPU:

>>> roberta.cuda()
>>> roberta.predict('new_task', tokens)
tensor([[-1.1050, -1.0672, -1.1245]], device='cuda:0', grad_fn=<LogSoftmaxBackward>)

Results

Results on GLUE tasks (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	-	-	-	-	-	-	-

Results on SQuAD (dev set)

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

Results on Reading Comprehension (RACE, test set)

Model	Accuracy	Middle	High
roberta.large	83.2	86.5	81.3

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 on GLUE tasks

A more detailed tutorial is coming soon.

Pretraining using your own data

You can use the masked_lm task to pretrain RoBERTa from scratch, or to continue pretraining RoBERTa starting from one of the released checkpoints.

Data should be preprocessed following the language modeling example.

A more detailed tutorial is coming soon.

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},
}