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

[https://arxiv.org/pdf/1910.13461.pdf]

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)

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.

bart = torch.hub.load('pytorch/fairseq', 'bart.large.cnn')
bart.cuda()
bart.eval()
bart.half()
count = 1
bsz = 32
with open('test.source') as source, open('test.hypo', 'w') as fout:
    sline = source.readline().strip()
    slines = [sline]
    for sline in source:
        if count % bsz == 0:
            with torch.no_grad():
                hypotheses_batch = bart.sample(slines, beam=4, lenpen=2.0, max_len_b=140, min_len=55, no_repeat_ngram_size=3)

            for hypothesis in hypotheses_batch:
                fout.write(hypothesis + '\n')
                fout.flush()
            slines = []

        slines.append(sline.strip())
        count += 1
    if slines != []:
        hypotheses_batch = bart.sample(slines, beam=4, lenpen=2.0, max_len_b=140, min_len=55, no_repeat_ngram_size=3)
        for hypothesis in hypotheses_batch:
            fout.write(hypothesis + '\n')
            fout.flush()

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},
}