README.md

metadata

annotations_creators:
  - no-annotation
languages:
  - py
language_creators:
  - found
licenses:
  - unknown
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - code-generation
  - conditional-text-generation
task_ids:
  - language-modeling
  - code-generation

Dataset Card for notional-python

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks
  • 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

Dataset Description

• Homepage: https://notional.ai/
• Repository: [Needs More Information]
• Paper: [Needs More Information]
• Leaderboard: [Needs More Information]
• Point of Contact: [Needs More Information]

Dataset Summary

The Notional-python dataset contains python code files from 100 well-known repositories gathered from Google Bigquery Github Dataset. The dataset was created to test the ability of programming language models. Follow our repo to do the model evaluation using notional-python dataset.

Supported Tasks and Leaderboards

[Needs More Information]

Languages

Python

Dataset Structure

Data Instances

[Needs More Information]

Data Fields

[Needs More Information]

Data Splits

[Needs More Information]

Dataset Creation

Curation Rationale

Notional-python was built to provide a dataset for testing the ability of the machine to generate python code.

Source Data

Initial Data Collection and Normalization

The data was obtained by filtering code from Google Bigquery Github data In order to improve the quality of the dataset, only python code files that meet the below conditions are added to the dataset:

• Code with more than 60% of executable lines
• Code with logic, not config files or comment-only files
• Code with more than 30% of attribute declaration lines (E.G.: Some files contain just only class names and their class attributes, usually used for configuration of the project, these files were not selected)
• Code without TODO and FIXME.

Who are the source language producers?

The producers are users of github.

Annotations

Annotation process

[Needs More Information]

Who are the annotators?

[Needs More Information]

Personal and Sensitive Information

[Needs More Information]

Considerations for Using the Data

Social Impact of Dataset

[Needs More Information]

Discussion of Biases

[Needs More Information]

Other Known Limitations

[Needs More Information]

Additional Information

Dataset Curators

[Needs More Information]

Licensing Information

[Needs More Information]

Citation Information

[Needs More Information]