jboru / reina

REINA

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

Usage 1

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"

Usage 2

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"

Related project

REINA is integrated into the project of Human Parity on CommonsenseQA

https://github.com/microsoft/KEAR

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Trademarks

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