Datasets:
Create README.md
Browse files
README.md
ADDED
@@ -0,0 +1,97 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
annotations_creators: []
|
3 |
+
language_creators:
|
4 |
+
- crowdsourced
|
5 |
+
- expert-generated
|
6 |
+
language:
|
7 |
+
- code
|
8 |
+
license:
|
9 |
+
- mit
|
10 |
+
multilinguality:
|
11 |
+
- monolingual
|
12 |
+
size_categories:
|
13 |
+
- unknown
|
14 |
+
source_datasets:
|
15 |
+
- original
|
16 |
+
task_categories:
|
17 |
+
- text2text-generation
|
18 |
+
task_ids: []
|
19 |
+
pretty_name: DocPrompting-CoNaLa
|
20 |
+
tags:
|
21 |
+
- code-generation
|
22 |
+
- doc retrieval
|
23 |
+
- retrieval augmented generation
|
24 |
+
---
|
25 |
+
|
26 |
+
## Dataset Description
|
27 |
+
- **Repository:** https://github.com/shuyanzhou/docprompting
|
28 |
+
- **Paper:** [DocPrompting: Generating Code by Retrieving the Docs](https://arxiv.org/pdf/2207.05987.pdf)
|
29 |
+
|
30 |
+
### Dataset Summary
|
31 |
+
This is the natural language to bash generation dataset we harvested from the English subset of [`tldr`](https://github.com/tldr-pages/tldr)
|
32 |
+
We split the dataset by bash commands. Every command in the dev and test set is held out from the training set.
|
33 |
+
|
34 |
+
### Supported Tasks and Leaderboards
|
35 |
+
This dataset is used to evaluate code generations.
|
36 |
+
|
37 |
+
### Languages
|
38 |
+
English - Bash
|
39 |
+
|
40 |
+
## Dataset Structure
|
41 |
+
```python
|
42 |
+
dataset = load_dataset("neulab/tldr")
|
43 |
+
DatasetDict({
|
44 |
+
train: Dataset({
|
45 |
+
features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'],
|
46 |
+
num_rows: 6414
|
47 |
+
})
|
48 |
+
test: Dataset({
|
49 |
+
features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'],
|
50 |
+
num_rows: 928
|
51 |
+
})
|
52 |
+
validation: Dataset({
|
53 |
+
features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'],
|
54 |
+
num_rows: 1845
|
55 |
+
})
|
56 |
+
})
|
57 |
+
|
58 |
+
code_docs = load_dataset("neulab/docprompting-conala", "docs")
|
59 |
+
DatasetDict({
|
60 |
+
train: Dataset({
|
61 |
+
features: ['doc_id', 'doc_content'],
|
62 |
+
num_rows: 439064
|
63 |
+
})
|
64 |
+
})
|
65 |
+
```
|
66 |
+
|
67 |
+
### Data Fields
|
68 |
+
train/dev/test:
|
69 |
+
- nl: The natural language intent
|
70 |
+
- cmd: The reference code snippet
|
71 |
+
- question_id: the unique id of a question
|
72 |
+
- oracle_man: The `doc_id` of the functions used in the reference code snippet. The corresponding contents are in `doc` split
|
73 |
+
- cmd_name: the bash command of this code snippet
|
74 |
+
- tldr_cmd_name: the bash command used in tldr github repo. The `cmd_name` and `tldr_cmd_name` can be different due to naming difference
|
75 |
+
- manual_exist: whether the manual exists in https://manned.org
|
76 |
+
- matching_info: each code snippets have multiple tokens, this is the detailed reference doc matching on each token.
|
77 |
+
|
78 |
+
|
79 |
+
docs:
|
80 |
+
- doc_id: the id of a doc
|
81 |
+
- doc_content: the content of the doc
|
82 |
+
|
83 |
+
## Dataset Creation
|
84 |
+
The dataset was curated from [`tldr`](https://github.com/tldr-pages/tldr).
|
85 |
+
The project aims to provide frequent usage of bash commands with natural language intents.
|
86 |
+
For more details, please check the repo.
|
87 |
+
|
88 |
+
### Citation Information
|
89 |
+
|
90 |
+
```
|
91 |
+
@article{zhou2022doccoder,
|
92 |
+
title={DocCoder: Generating Code by Retrieving and Reading Docs},
|
93 |
+
author={Zhou, Shuyan and Alon, Uri and Xu, Frank F and Jiang, Zhengbao and Neubig, Graham},
|
94 |
+
journal={arXiv preprint arXiv:2207.05987},
|
95 |
+
year={2022}
|
96 |
+
}
|
97 |
+
```
|