12
stars
7
commits
2
linked in READMEs
Dec 22, 2022
updated
This is the natural language to bash generation dataset we harvested from the English subset of tldr
We split the dataset by bash commands. Every command in the dev and test set is held out from the training set.
This dataset is used to evaluate code generations.
English - Bash
dataset = load_dataset("neulab/tldr")
DatasetDict({
train: Dataset({
features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'],
num_rows: 6414
})
test: Dataset({
features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'],
num_rows: 928
})
validation: Dataset({
features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'],
num_rows: 1845
})
})
code_docs = load_dataset("neulab/docprompting-conala", "docs")
DatasetDict({
train: Dataset({
features: ['doc_id', 'doc_content'],
num_rows: 439064
})
})
train/dev/test:
doc_id of the functions used in the reference code snippet. The corresponding contents are in doc splitcmd_name and tldr_cmd_name can be different due to naming differencedocs:
The dataset was curated from tldr.
The project aims to provide frequent usage of bash commands with natural language intents.
For more details, please check the repo.
@article{zhou2022doccoder,
title={DocCoder: Generating Code by Retrieving and Reading Docs},
author={Zhou, Shuyan and Alon, Uri and Xu, Frank F and Jiang, Zhengbao and Neubig, Graham},
journal={arXiv preprint arXiv:2207.05987},
year={2022}
}
7 commits
12
stars
7
commits
2
linked in READMEs
Dec 22, 2022
updated
This is the natural language to bash generation dataset we harvested from the English subset of tldr
We split the dataset by bash commands. Every command in the dev and test set is held out from the training set.
This dataset is used to evaluate code generations.
English - Bash
dataset = load_dataset("neulab/tldr")
DatasetDict({
train: Dataset({
features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'],
num_rows: 6414
})
test: Dataset({
features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'],
num_rows: 928
})
validation: Dataset({
features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'],
num_rows: 1845
})
})
code_docs = load_dataset("neulab/docprompting-conala", "docs")
DatasetDict({
train: Dataset({
features: ['doc_id', 'doc_content'],
num_rows: 439064
})
})
train/dev/test:
doc_id of the functions used in the reference code snippet. The corresponding contents are in doc splitcmd_name and tldr_cmd_name can be different due to naming differencedocs:
The dataset was curated from tldr.
The project aims to provide frequent usage of bash commands with natural language intents.
For more details, please check the repo.
@article{zhou2022doccoder,
title={DocCoder: Generating Code by Retrieving and Reading Docs},
author={Zhou, Shuyan and Alon, Uri and Xu, Frank F and Jiang, Zhengbao and Neubig, Graham},
journal={arXiv preprint arXiv:2207.05987},
year={2022}
}
7 commits