shoumitro26/python-code-dataset-500k

---
dataset_info:
features:
- name: output
dtype: string
- name: instruction
dtype: string
- name: system
dtype: string
splits:
- name: train
num_bytes: 922266591
num_examples: 559515
download_size: 346944286
dataset_size: 922266591
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: mit
task_categories:
- text-generation
tags:
- instructional
- python
- code
pretty_name: github_python
size_categories:
- 100K<n<1M
---

#### Attention: This dataset is a summary and reformat pulled from github code.
You should make your own assumptions based on this.
In fact, there is another dataset I formed through parsing that addresses several points:
- out of 500k python related items, most of them are python-ish, not pythonic
- the majority of the items here contain excessive licensing inclusion of original code
- the items here are sometimes not even python but have references
- There's a whole lot of gpl summaries floating on the code responses or instructions

As such, you are probably not getting good data to begin with, but this should be used as a starting point at best.
You have been warned.