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Data-Augmentation-for-Biomedical-Factoid-Question-Answering

This repository includes the code to train and evaluate all models mentioned in the paper "Data Augmentation for Biomedical Factoid Question Answering" presented in BIONLP 2022 workshop of ACL.

The data used in the paper can be found in the following webpage: http://nlp.cs.aueb.gr/publications.html

After downloading the data you should unzip the file and change the paths in the train.py file

How to train:

You could run python train.py --help to see all parameters.

An example training can be seen below

python train.py --train_path=/home/dpappas/bioasq_factoid/pubmed_factoid_extracted_data.p --dev_path=/home/dpappas/bioasq_factoid/pubmed_factoid_extracted_data_dev.p --keep_only=factoid_snippet --batch_size=16 --augment_with=w2v_embed --how_many_aug=10000 --augment_strategy=separate --prefix=w2v_embed_10k_albert

How to eval:

After training you could evaluate on dev set or test set using the trained model.

python3.6 eval.py --trained_model_path=some_model_path.pth.tar --data_path=pubmed_factoid_extracted_data_test.p --model_name=ktrapeznikov/albert-xlarge-v2-squad-v2 --transformer_size=2048

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