
# Dataset Card for notional-python

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Languages](#languages)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
- [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)

## 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.

### Languages

Python

## 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](https://cloud.google.com/blog/topics/public-datasets/github-on-bigquery-analyze-all-the-open-source-code)
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.

task_ids:language-modeling annotations_creators:no-annotation language_creators:found multilinguality:monolingual size_categories:10K<n<100K source_datasets:original language:py license:unknown region:us
{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["py"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["code-generation", "conditional-text-generation"], "task_ids": ["language-modeling", "code-generation"]}
TAGS
#task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-py #license-unknown #region-us 


# Dataset Card for notional-python

## Table of Contents
- Dataset Description
  - Dataset Summary
  - Languages
- Dataset Creation
  - Curation Rationale
  - Source Data
- 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: URL
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact: 

### 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.

### Languages

Python

## 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.

# Dataset Card for notional-python ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Languages
- Dataset Creation
  - Curation Rationale
  - Source Data
- 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: URL
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact: ### 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. ### Languages

Python ## 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.
TAGS
#task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-py #license-unknown #region-us 
 # Dataset Card for notional-python ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Languages
- Dataset Creation
  - Curation Rationale
  - Source Data
- 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: URL
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact: ### 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. ### Languages

Python ## 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.
passage: TAGS
#task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-py #license-unknown #region-us 
# Dataset Card for notional-python## Table of Contents
- Dataset Description
  - Dataset Summary
  - Languages
- Dataset Creation
  - Curation Rationale
  - Source Data
- 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: URL
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact:### 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.### Languages

Python## 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.