Generative Data Augmentation for Commonsense Reasoning ( G-DAUG^C)

This repository contains the code used for EMNLP 2020 Finding paper:

This code was originally forked from the HuggingFace library.

Usage: will update

Name		Name	Last commit message	Last commit date
Latest commit History 6 Commits
CODAH		CODAH
README.md		README.md
data_utils.py		data_utils.py
finetune_gd_swag.py		finetune_gd_swag.py
finetune_qa_csqa.py		finetune_qa_csqa.py
finetune_qa_swag.py		finetune_qa_swag.py
finetune_qa_winogrande.py		finetune_qa_winogrande.py
finetune_qd_csqa.py		finetune_qd_csqa.py
finetune_qg_csqa.py		finetune_qg_csqa.py
finetune_qg_swag.py		finetune_qg_swag.py
finetune_qg_winogrande.py		finetune_qg_winogrande.py
generate_question_swag.py		generate_question_swag.py
generate_questions.py		generate_questions.py
generate_questions_arc.py		generate_questions_arc.py
generate_winogrande_sym.py		generate_winogrande_sym.py
get_auc.py		get_auc.py
modeling.py		modeling.py
requirement.txt		requirement.txt
run_diversity.py		run_diversity.py
run_influence.py		run_influence.py
run_mc.py		run_mc.py
swag_data_utils.py		swag_data_utils.py
syn_replace.py		syn_replace.py
text_fooler_arc.py		text_fooler_arc.py
text_fooler_csqa.py		text_fooler_csqa.py
text_fooler_swag.py		text_fooler_swag.py
text_fooler_winogrande.py		text_fooler_winogrande.py
winogrande_data_utils.py		winogrande_data_utils.py

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