Datasets:
Tasks:
Question Answering
Modalities:
Text
Sub-tasks:
extractive-qa
Languages:
code
Size:
100K - 1M
License:
Commit
•
86a9aaf
1
Parent(s):
6e4338e
Update README.md
Browse files
README.md
CHANGED
@@ -44,11 +44,11 @@ license:
|
|
44 |
|
45 |
- **Homepage:** [Data](https://huggingface.co/datasets/thepurpleowl/codequeries)
|
46 |
- **Repository:** [Code](https://github.com/thepurpleowl/codequeries-benchmark)
|
47 |
-
- **Paper:**
|
48 |
|
49 |
### Dataset Summary
|
50 |
|
51 |
-
CodeQueries is a dataset to evaluate the ability of neural networks to answer semantic queries over code. Given a query and code, a model is expected to identify answer and supporting-fact spans in the code for the query. This is extractive question-answering over code, for questions with a large scope (entire files) and complexity including both single- and multi-hop reasoning.
|
52 |
|
53 |
### Supported Tasks and Leaderboards
|
54 |
|
@@ -118,19 +118,3 @@ The dataset is created using [ETH Py150 Open dataset](https://github.com/google-
|
|
118 |
### Licensing Information
|
119 |
|
120 |
The source code repositories used for preparing CodeQueries are based on the [ETH Py150 Open dataset](https://github.com/google-research-datasets/eth_py150_open) and are redistributable under the respective licenses. A Huggingface dataset for ETH Py150 Open is available [here](https://huggingface.co/datasets/eth_py150_open). The labeling prepared and provided by us as part of CodeQueries is released under the Apache-2.0 license.
|
121 |
-
|
122 |
-
### Citation Information
|
123 |
-
|
124 |
-
```
|
125 |
-
@misc{https://doi.org/10.48550/arxiv.2209.08372,
|
126 |
-
doi = {10.48550/ARXIV.2209.08372},
|
127 |
-
url = {https://arxiv.org/abs/2209.08372},
|
128 |
-
author = {Sahu, Surya Prakash and Mandal, Madhurima and Bharadwaj, Shikhar and Kanade, Aditya and Maniatis, Petros and Shevade, Shirish},
|
129 |
-
keywords = {Software Engineering (cs.SE), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
|
130 |
-
title = {Learning to Answer Semantic Queries over Code},
|
131 |
-
publisher = {arXiv},
|
132 |
-
year = {2022},
|
133 |
-
copyright = {Creative Commons Attribution 4.0 International}
|
134 |
-
}
|
135 |
-
|
136 |
-
```
|
|
|
44 |
|
45 |
- **Homepage:** [Data](https://huggingface.co/datasets/thepurpleowl/codequeries)
|
46 |
- **Repository:** [Code](https://github.com/thepurpleowl/codequeries-benchmark)
|
47 |
+
- **Paper:**
|
48 |
|
49 |
### Dataset Summary
|
50 |
|
51 |
+
CodeQueries is a dataset to evaluate the ability of neural networks to answer semantic queries over code. Given a query and code, a model is expected to identify answer and supporting-fact spans in the code for the query. This is extractive question-answering over code, for questions with a large scope (entire files) and complexity including both single- and multi-hop reasoning.
|
52 |
|
53 |
### Supported Tasks and Leaderboards
|
54 |
|
|
|
118 |
### Licensing Information
|
119 |
|
120 |
The source code repositories used for preparing CodeQueries are based on the [ETH Py150 Open dataset](https://github.com/google-research-datasets/eth_py150_open) and are redistributable under the respective licenses. A Huggingface dataset for ETH Py150 Open is available [here](https://huggingface.co/datasets/eth_py150_open). The labeling prepared and provided by us as part of CodeQueries is released under the Apache-2.0 license.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|