BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

https://arxiv.org/abs/1910.13461

Introduction

BART is sequence-to-sequence model trained with denoising as pretraining objective. We show that this pretraining objective is more generic and show that we can match RoBERTa results on SQuAD and GLUE and gain state-of-the-art results on summarization (XSum, CNN dataset), long form generative question answering (ELI5) and dialog response genration (ConvAI2). See the associated paper for more details.

Pre-trained models

Model	Description	# params	Download
`bart.base`	BART model with 6 encoder and decoder layers	140M	bart.base.tar.gz
`bart.large`	BART model with 12 encoder and decoder layers	400M	bart.large.tar.gz
`bart.large.mnli`	`bart.large` finetuned on `MNLI`	400M	bart.large.mnli.tar.gz
`bart.large.cnn`	`bart.large` finetuned on `CNN-DM`	400M	bart.large.cnn.tar.gz
`bart.large.xsum`	`bart.large` finetuned on `Xsum`	400M	bart.large.xsum.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.large`	90.2	94.7	92.2	86.6	96.4	90.9	68.0	92.4
`bart.large`	89.9	94.9	92.5	87.0	96.6	90.4	62.8	91.2

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
`bart.large`	88.8/94.6	86.1/89.2

CNN/Daily Mail (test set, no additional data used)

Model	R1	R2	RL
`BERTSUMEXTABS`	42.13	19.60	39.18
`bart.large`	44.16	21.28	40.90

Example usage

Load BART from torch.hub (PyTorch >= 1.1):

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

Load BART (for PyTorch 1.0 or custom models):

# Download bart.large model
wget https://dl.fbaipublicfiles.com/fairseq/models/bart.large.tar.gz
tar -xzvf bart.large.tar.gz

# Load the model in fairseq
from fairseq.models.bart import BARTModel
bart = BARTModel.from_pretrained('/path/to/bart.large', checkpoint_file='model.pt')
bart.eval()  # disable dropout (or leave in train mode to finetune)

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

tokens = bart.encode('Hello world!')
assert tokens.tolist() == [0, 31414, 232, 328, 2]
bart.decode(tokens)  # 'Hello world!'

Extract features from BART:

# Extract the last layer's features
last_layer_features = bart.extract_features(tokens)
assert last_layer_features.size() == torch.Size([1, 5, 1024])

# Extract all layer's features from decoder (layer 0 is the embedding layer)
all_layers = bart.extract_features(tokens, return_all_hiddens=True)
assert len(all_layers) == 13
assert torch.all(all_layers[-1] == last_layer_features)

Use BART for sentence-pair classification tasks:

# Download BART already finetuned for MNLI
bart = torch.hub.load('pytorch/fairseq', 'bart.large.mnli')
bart.eval()  # disable dropout for evaluation

# Encode a pair of sentences and make a prediction
tokens = bart.encode('BART is a seq2seq model.', 'BART is not sequence to sequence.')
bart.predict('mnli', tokens).argmax()  # 0: contradiction

# Encode another pair of sentences
tokens = bart.encode('BART is denoising autoencoder.', 'BART is version of autoencoder.')
bart.predict('mnli', tokens).argmax()  # 2: entailment

Register a new (randomly initialized) classification head:

bart.register_classification_head('new_task', num_classes=3)
logprobs = bart.predict('new_task', tokens)

Batched prediction:

import torch
from fairseq.data.data_utils import collate_tokens

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

batch_of_pairs = [
    ['BART is a seq2seq model.', 'BART is not sequence to sequence.'],
    ['BART is denoising autoencoder.', 'BART is version of autoencoder.'],
]

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

logprobs = bart.predict('mnli', batch)
print(logprobs.argmax(dim=1))
# tensor([0, 2])

Using the GPU:

bart.cuda()
bart.predict('new_task', tokens)

Filling masks:

BART can be used to fill multiple <mask> tokens in the input.

bart = torch.hub.load('pytorch/fairseq', 'bart.base')
bart.eval()
bart.fill_mask(['The cat <mask> on the <mask>.'], topk=3, beam=10)
# [[('The cat was on the ground.', tensor(-0.6183)), ('The cat was on the floor.', tensor(-0.6798)), ('The cat sleeps on the couch.', tensor(-0.6830))]]

Note that by default we enforce the output length to match the input length. This can be disabled by setting match_source_len=False:

bart.fill_mask(['The cat <mask> on the <mask>.'], topk=3, beam=10, match_source_len=False)
# [[('The cat was on the ground.', tensor(-0.6185)), ('The cat was asleep on the couch.', tensor(-0.6276)), ('The cat was on the floor.', tensor(-0.6800))]]

Example code to fill masks for a batch of sentences using GPU

bart.cuda()
bart.fill_mask(['The cat <mask> on the <mask>.', 'The dog <mask> on the <mask>.'], topk=3, beam=10)
# [[('The cat was on the ground.', tensor(-0.6183)), ('The cat was on the floor.', tensor(-0.6798)), ('The cat sleeps on the couch.', tensor(-0.6830))], [('The dog was on the ground.', tensor(-0.6190)), ('The dog lay on the ground.', tensor(-0.6711)),
('The dog was asleep on the couch', tensor(-0.6796))]]

Evaluating the `bart.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
bart.cuda()
bart.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 = bart.encode(sent1, sent2)
        prediction = bart.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: 0.9010

Evaluating the `bart.large.cnn` model:

Follow instructions here to download and process into data-files such that test.source and test.target has one line for each non-tokenized sample.
For simpler preprocessing, you can also wget https://cdn-datasets.huggingface.co/summarization/cnn_dm_v2.tgz, although there is no guarantee of identical scores
huggingface/transformers has a simpler interface that supports single-gpu and multi-gpu beam search. In huggingface/transformers, the BART models' paths are facebook/bart-large-cnn and facebook/bart-large-xsum.

In fairseq, summaries can be generated using:

cp data-bin/cnn_dm/dict.source.txt  checkpoints/
python examples/bart/summarize.py \
  --model-dir pytorch/fairseq \
  --model-file bart.large.cnn \
  --src cnn_dm/test.source \
  --out cnn_dm/test.hypo

For calculating rouge, install files2rouge from here.

export CLASSPATH=/path/to/stanford-corenlp-full-2016-10-31/stanford-corenlp-3.7.0.jar

# Tokenize hypothesis and target files.
cat test.hypo | java edu.stanford.nlp.process.PTBTokenizer -ioFileList -preserveLines > test.hypo.tokenized
cat test.target | java edu.stanford.nlp.process.PTBTokenizer -ioFileList -preserveLines > test.hypo.target
files2rouge test.hypo.tokenized test.hypo.target
# Expected output: (ROUGE-2 Average_F: 0.21238)

Finetuning

Citation

@article{lewis2019bart,
    title = {BART: Denoising Sequence-to-Sequence Pre-training for Natural
Language Generation, Translation, and Comprehension},
    author = {Mike Lewis and Yinhan Liu and Naman Goyal and Marjan Ghazvininejad and
              Abdelrahman Mohamed and Omer Levy and Veselin Stoyanov
              and Luke Zettlemoyer },
    journal={arXiv preprint arXiv:1910.13461},
    year = {2019},
}