A product of an unlikely collaboration, which I am thankful for:
When NLP and code researchers meet
When NLP and code researchers meet
Huge ππ€π’π’ππ© π¨πͺπ’π’ππ§ππ―ππ©ππ€π£ dataset
@HujiIdan and myself
arxiv.org/abs/2108.10763
@HujiIdan and myself
arxiv.org/abs/2108.10763
The dataset cleans tons of open source projects to have only ones with high quality committing habits
(e.g. large active projects with commits that are of significant length etc.)
(e.g. large active projects with commits that are of significant length etc.)
We present some ways to evaluate that the meaning was kept while summarizing, so you can go beyond ROUGE 
We provide a strict split that keeps some (thousand+-) repositories totally out of the training, so you can check in domain and out of domain or just be sure results are clean.
If you ever want an even larger dataset, follow the same procedure and use more repositories (we took only ones active in 2020, pick ones that are active no longer or wasn't active until now)
Enjoy
Dataset in figshare.com/articles/datasβ¦
Code is found in github.com/evidencebp/comβ¦
Paper in arxiv.org/pdf/2108.10763β¦
Code is found in github.com/evidencebp/comβ¦
Paper in arxiv.org/pdf/2108.10763β¦
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