README.md

metadata

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
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage:
• Repository: Code repo
• 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 for adding this dataset.