Datasets:
Tasks:
Question Answering
Modalities:
Text
Sub-tasks:
extractive-qa
Languages:
code
Size:
100K<n<1M
License:
File size: 3,846 Bytes
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---
annotations_creators:
- expert-generated
language:
- code
language_creators:
- found
license:
- mit
multilinguality:
- monolingual
pretty_name: codequeries
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- code
- code question answering
- code semantic parsing
- codeqa
task_categories:
- question-answering
task_ids:
- extractive-qa
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** [Code repo](https://github.com/adityakanade/natural-cubert/)
- **Paper:**
### Dataset Summary
CodeQueries allows to explore extractive question-answering methodology over code
by providing semantic queries as question and answer pairs over code context involving
complex concepts and long chains of reasoning.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The code section have taken from `python` files.
## Dataset Structure
### Data Instances
All splits of all settings have same format. An example looks as follows -
```
```
### Data Fields
- examples
- query_name (query name to uniquely identify the query)
- context_blocks (code blocks supplied as input to the model for prediction)
- answer_spans (code in answer spans)
- supporting_fact_spans (code in supporting-fact spans)
- code_file_path (relative source file path w.r.t. ETH Py150 corpus)
- example_type (positive(1) or negative(0) example type)
- subtokenized_input_sequence (example subtokens)
- label_sequence (example subtoken labels)
### Data Splits
| |train |validation |test |
|--------------|:----:|:---------:|:---:|
|ideal | 9427 | 3270| 3245|
|prefix | - | - | 3245|
|sliding_window| - | - | 3245|
|file_ideal | - | - | 3245|
|twostep | - | - | 3245|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### 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
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. |