Implementation of the following paper:
Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data (https://arxiv.org/abs/2203.08773)
Shuohang Wang (shuowa at microsoft.com), Yichong Xu, Yuwei Fang, Yang Liu, Siqi Sun, Ruochen Xu, Chenguang Zhu, Michael Zeng
Accept to ACL2022 main conference
After cloning the repo, run the following code with docker to reproduce REINA on XSum dataset. REINA is interaged into the model trainig code. Please set model name to google/pegasus-large or facebook/bart-large or facebook/bart-base, etc. By default, the job is run on 8 GPUs. Please tuning "--gradient_accumulation_steps" if use less GPUs. More --reina_workers is prefered to speed up REINA process. 40 workers will task around 15 minutes.
docker run --gpus all -it --rm --shm-size 10g -w /home/reina/src -v ${PWD}/REINA:/home/reina shuohang/pytorch:reina /bin/bash -c "export HF_DATASETS_CACHE=/home/reina/data; export TRANSFORMERS_CACHE=/home/reina/cache; python -m torch.distributed.launch --nproc_per_node=8 run_summarization.py --report_to none --save_strategy epoch --model_name_or_path google/pegasus-large --dataset_name xsum --do_train --do_eval --do_predict --per_device_train_batch_size=2 --gradient_accumulation_steps 2 --per_device_eval_batch_size=4 --predict_with_generate --output_dir /home/reina/output --overwrite_output_dir --text_column document --summary_column summary --num_train_epochs 3 --logging_strategy epoch --evaluation_strategy epoch --load_best_model_at_end --max_target_length 64 --val_max_target_length 64 --learning_rate 0.00005 --reina --reina_workers 40"
In this section, the REINA and model training are splitted in two steps. The first step will save REINA data into files and then run seq2seq model for summarization.
docker run --gpus all -it --rm --shm-size 10g -w /home/reina/src -v ${PWD}/REINA:/home/reina shuohang/pytorch:reina /bin/bash -c "export HF_DATASETS_CACHE=/home/reina/data; python reina.py --dataname xsum --reina_workers 10 --key_column document --value_column summary"
docker run --gpus all -it --rm --shm-size 10g -w /home/reina/src -v ${PWD}/REINA:/home/reina shuohang/pytorch:reina /bin/bash -c "export HF_DATASETS_CACHE=/home/reina/data; export TRANSFORMERS_CACHE=/home/reina/cache; python -m torch.distributed.launch --nproc_per_node=8 run_summarization.py --report_to none --save_strategy epoch --model_name_or_path google/pegasus-large --do_train --do_eval --do_predict --per_device_train_batch_size=2 --gradient_accumulation_steps 2 --per_device_eval_batch_size=4 --predict_with_generate --output_dir /home/reina/output --overwrite_output_dir --text_column document --summary_column summary --num_train_epochs 3 --logging_strategy epoch --evaluation_strategy epoch --load_best_model_at_end --max_target_length 64 --val_max_target_length 64 --learning_rate 0.00005 --train_file /home/reina/data/reina/xsum/train.json --validation_file /home/reina/data/reina/xsum/validation.json --test_file /home/reina/data/reina/xsum/test.json"
REINA is integrated into the project of Human Parity on CommonsenseQA
https://github.com/microsoft/KEAR
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