---
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.