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Dataset Card for ethpy150open
Dataset Summary
A redistributable subset of the ETH Py150 corpus, introduced in the ICML 2020 paper 'Learning and Evaluating Contextual Embedding of Source Code'
Supported Tasks and Leaderboards
[More Information Needed]
Languages
English
Dataset Structure
List of dicts of { "filepath": The relative URL containing the path to the file on GitHub "license": The license used for that specific file or repository }
Data Instances
{ "filepath": "0rpc/zerorpc-python/setup.py", "license": "mit" }, { "filepath": "0rpc/zerorpc-python/zerorpc/heartbeat.py", "license": "mit" },
Data Fields
filepath: The relative URL containing the path to the file on GitHublicense: The license used for that specific file or repository
Data Splits
| Train | Valid | Test | |
|---|---|---|---|
| Dataset Split | 74749 | 8302 | 41457 |
Dataset Creation
The original dataset is at https://www.sri.inf.ethz.ch/py150
Curation Rationale
To generate a more redistributable version of the dataset
Source Data
Initial Data Collection and Normalization
All the urls are filepaths relative to GitHub and the master branch was used as available at the time
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
[More Information Needed]
Licensing Information
Apache License 2.0
Citation Information
@inproceedings{kanade2020learning, title={Learning and Evaluating Contextual Embedding of Source Code}, author={Kanade, Aditya and Maniatis, Petros and Balakrishnan, Gogul and Shi, Kensen}, booktitle={International Conference on Machine Learning}, pages={5110--5121}, year={2020}, organization={PMLR} }
Contributions
Thanks to @Bharat123rox for adding this dataset.
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