task_categories:summarization task_categories:text-generation task_categories:conversational task_categories:text2text-generation language:en license:apache-2.0 code reasoning Python region:us
---
dataset_info:
  features:
  - name: INSTRUCTION
    dtype: string
  - name: RESPONSE
    dtype: string
  - name: TYPE
    dtype: int64
  - name: SOURCE
    dtype: string
  splits:
  - name: train
    num_bytes: 399930143
    num_examples: 429059
  download_size: 89360217
  dataset_size: 399930143
license: apache-2.0
task_categories:
- summarization
- text-generation
- conversational
- text2text-generation
language:
- en
tags:
- code
- reasoning
- Python
pretty_name: Reasoning dataset for Python
---
# Dataset Card for "reason_code-search-net-python"

## Dataset Description

- **Homepage:** None
- **Repository:** https://huggingface.co/datasets/Nan-Do/reason_code-search-net-python
- **Paper:** None
- **Leaderboard:** None
- **Point of Contact:** [@Nan-Do](https://github.com/Nan-Do)

### Dataset Summary

This dataset is an instructional dataset for Python.  
The dataset contains five different kind of tasks.   

Given a Python 3 function:
 - Type 1: Generate a summary explaining what it does. (For example: This function counts the number of objects stored in the jsonl file passed as input.)
 - Type 2: Generate a summary explaining what its input parameters represent ("For example: infile: a file descriptor of a file containing json objects in jsonl format.")
 - Type 3: Generate a summary explaining what the return value represents ("For example: The function returns the number of json objects in the file passed as input.")
 - Type 4: Generate a summary explaining what is the type of the return value ("For example: The function returns an int.")
 - Type 5: Generate a summary explaining what is the type of its input parameters ("For example: infile: A file descriptor.").

### Languages

The dataset is in English.

### Data Splits

There are no splits (Only training).

## Dataset Creation

May of 2023

### Curation Rationale

This dataset was created to improve the Python 3 reasoning/understanding capabilities of LLMs.

### Source Data

The summarized version of the code-search-net dataset can be found at https://huggingface.co/datasets/Nan-Do/code-search-net-python

### Annotations

The dataset includes an instruction, response and type columns.  
The type colum indicates the type of task (from 1 to 5).

#### Annotation process

The annotation procedure was done using templates, NLP techniques to generate human-like questions and responses, and the Python AST module to parse the code.
The responses were generated parsing the docstrings of the functions. (The ones that included the required information).

### Licensing Information

Apache 2.0