wehub-resource-sync
122 lines
4.3 KiB
Markdown
122 lines
4.3 KiB
Markdown
# Fine-tuning BART on CNN-Dailymail summarization task
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### 1) Download the CNN and Daily Mail data and preprocess it into data files with non-tokenized cased samples.
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Follow the instructions [here](https://github.com/abisee/cnn-dailymail) to download the original CNN and Daily Mail datasets. To preprocess the data, refer to the pointers in [this issue](https://github.com/pytorch/fairseq/issues/1391) or check out the code [here](https://github.com/artmatsak/cnn-dailymail).
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Follow the instructions [here](https://github.com/EdinburghNLP/XSum) to download the original Extreme Summarization datasets, or check out the code [here](https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset), Please keep the raw dataset and make sure no tokenization nor BPE on the dataset.
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### 2) BPE preprocess:
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```bash
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wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json'
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wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe'
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wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt'
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TASK=cnn_dm
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for SPLIT in train val
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do
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for LANG in source target
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do
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python -m examples.roberta.multiprocessing_bpe_encoder \
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--encoder-json encoder.json \
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--vocab-bpe vocab.bpe \
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--inputs "$TASK/$SPLIT.$LANG" \
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--outputs "$TASK/$SPLIT.bpe.$LANG" \
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--workers 60 \
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--keep-empty;
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done
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done
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```
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### 3) Binarize dataset:
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```bash
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fairseq-preprocess \
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--source-lang "source" \
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--target-lang "target" \
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--trainpref "${TASK}/train.bpe" \
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--validpref "${TASK}/val.bpe" \
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--destdir "${TASK}-bin/" \
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--workers 60 \
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--srcdict dict.txt \
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--tgtdict dict.txt;
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```
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### 4) Fine-tuning on CNN-DM summarization task:
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Example fine-tuning CNN-DM
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```bash
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TOTAL_NUM_UPDATES=20000
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WARMUP_UPDATES=500
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LR=3e-05
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MAX_TOKENS=2048
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UPDATE_FREQ=4
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BART_PATH=/path/to/bart/model.pt
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CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 fairseq-train cnn_dm-bin \
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--restore-file $BART_PATH \
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--max-tokens $MAX_TOKENS \
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--task translation \
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--source-lang source --target-lang target \
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--truncate-source \
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--layernorm-embedding \
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--share-all-embeddings \
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--share-decoder-input-output-embed \
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--reset-optimizer --reset-dataloader --reset-meters \
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--required-batch-size-multiple 1 \
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--arch bart_large \
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--criterion label_smoothed_cross_entropy \
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--label-smoothing 0.1 \
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--dropout 0.1 --attention-dropout 0.1 \
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--weight-decay 0.01 --optimizer adam --adam-betas "(0.9, 0.999)" --adam-eps 1e-08 \
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--clip-norm 0.1 \
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--lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \
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--fp16 --update-freq $UPDATE_FREQ \
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--skip-invalid-size-inputs-valid-test \
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--find-unused-parameters;
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```
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Above is expected to run on `1` node with `8 32gb-V100`.
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Expected training time is about `5 hours`. Training time can be reduced with distributed training on `4` nodes and `--update-freq 1`.
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Use TOTAL_NUM_UPDATES=15000 UPDATE_FREQ=2 for Xsum task
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### Inference for CNN-DM test data using above trained checkpoint.
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After training the model as mentioned in previous step, you can perform inference with checkpoints in `checkpoints/` directory using following python code snippet:
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```python
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import torch
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from fairseq.models.bart import BARTModel
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bart = BARTModel.from_pretrained(
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'checkpoints/',
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checkpoint_file='checkpoint_best.pt',
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data_name_or_path='cnn_dm-bin'
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)
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bart.cuda()
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bart.eval()
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bart.half()
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count = 1
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bsz = 32
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with open('cnn_dm/test.source') as source, open('cnn_dm/test.hypo', 'w') as fout:
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sline = source.readline().strip()
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slines = [sline]
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for sline in source:
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if count % bsz == 0:
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with torch.no_grad():
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hypotheses_batch = bart.sample(slines, beam=4, lenpen=2.0, max_len_b=140, min_len=55, no_repeat_ngram_size=3)
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for hypothesis in hypotheses_batch:
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fout.write(hypothesis + '\n')
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fout.flush()
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slines = []
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slines.append(sline.strip())
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count += 1
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if slines != []:
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hypotheses_batch = bart.sample(slines, beam=4, lenpen=2.0, max_len_b=140, min_len=55, no_repeat_ngram_size=3)
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for hypothesis in hypotheses_batch:
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fout.write(hypothesis + '\n')
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fout.flush()
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```
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Use beam=6, lenpen=1.0, max_len_b=60, min_len=10 for Xsum Generation
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