HuggingFace_transformer/examples/summarization

Data

CNN/DailyMail data

cd examples/summarization
wget https://s3.amazonaws.com/datasets.huggingface.co/summarization/cnn_dm.tgz
tar -xzvf cnn_dm.tgz
export CNN_DIR=${PWD}/cnn_dm

this should make a directory called cnn_dm/ with files like test.source. To use your own data, copy that files format. Each article to be summarized is on its own line.

XSUM Data:

cd examples/summarization
wget https://s3.amazonaws.com/datasets.huggingface.co/summarization/xsum.tar.gz
tar -xzvf xsum.tar.gz
export XSUM_DIR=${PWD}/xsum

Evaluation

To create summaries for each article in dataset, run:

python run_eval.py <path_to_test.source> test_generations.txt <model-name>  --score_path rouge_scores.txt

The default batch size, 4, fits in 16GB GPU memory, but may need to be adjusted to fit your system.

Training

Run/modify finetune.sh

The following command should work on a 16GB GPU:

export me=`git config user.name`
./finetune.sh \
    --data_dir $XSUM_DIR \
    --train_batch_size=1 \
    --eval_batch_size=1 \
    --output_dir="$me"_xsum_results \
    --num_train_epochs 1

Tips:

• 1 epoch at batch size 1 for bart-large takes 24 hours, requires 13GB GPU RAM with fp16 on an NVIDIA-V100.
• try bart-base, --freeze_encoder or --freeze_embeds for faster training/larger batch size. (3hr/epoch with bs=8, see below)
• fp16_opt_level=O1 (the default works best).
• If you are finetuning on your own dataset, start from bart-large-cnn if you want long summaries and bart-large-xsum if you want short summaries. (It rarely makes sense to start from bart-large unless you are a researching finetuning methods).
• In addition to the pytorch-lightning .ckpt checkpoint, a transformers checkpoint will be saved. Load it with BartForConditionalGeneration.from_pretrained(f'{output_dir}/best_tfmr).
• At the moment, --do_predict does not work in a multi-gpu setting. You need to use evaluate_checkpoint or the run_eval.py code.
• If you want to run experiments on improving the summarization finetuning process, try the XSUM Shared Task (below). It's faster to train than CNNDM because the summaries are shorter.

XSUM Shared Task

Compare XSUM results with others by using --logger wandb_shared. This requires wandb registration. Here is an example command

export me=`git config user.name`
./finetune.sh \
    --data_dir $XSUM_DIR \
    --output_dir "$me"_xsum_frozen_embs \
    --logger wandb_shared \
    --train_batch_size 16 --eval_batch_size 16 --freeze_embeds --freeze_encoder \
    --num_train_epochs 6

Results can be viewed here