README.md

---
dataset_info:
- config_name: issues
  features:
  - name: repo_name
    dtype: string
  - name: issue_id
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 30986711842
    num_examples: 15549682
  download_size: 16370074732
  dataset_size: 30986711842
- config_name: kaggle
  features:
  - name: file_id
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 5209133899
    num_examples: 580195
  download_size: 2222724371
  dataset_size: 5209133899
configs:
- config_name: issues
  data_files:
  - split: train
    path: issues/train-*
- config_name: kaggle
  data_files:
  - split: train
    path: kaggle/train-*
---

# GitHub Issues & Kaggle Notebooks
## Description
GitHub Issues & Kaggle Notebooks is a collection of two code datasets intended for language models training, they are sourced from GitHub issues and notebooks in Kaggle platform. These datasets are a modified part of the [StarCoder2](https://arxiv.org/abs/2402.19173) model training corpus, precisely the [bigcode/StarCoder2-Extras](https://huggingface.co/datasets/bigcode/starcoder2data-extras) dataset. We reformat the samples to remove StarCoder2's special tokens and use natural text to delimit comments in issues and display kaggle notebooks in markdown and code blocks.  

The dataset includes:

- 🐛 GitHub Issues – 11B tokens of discussions from GitHub issues sourced from [GH Archive](https://www.gharchive.org/).
- 📊 Kaggle Notebooks – 1.7B tokens of data analysis notebooks in markdonw format, curated from Kaggle's [Meta Kaggle Code](https://www.kaggle.com/datasets/kaggle/meta-kaggle-code) dataset.
These datasets have undergone filtering to remove low-quality content, duplicates and PII. More details in StarCoder2 [paper](https://arxiv.org/abs/2402.19173)

## How to load the dataset

You can load a specific subset using the following code:

```python
from datasets import load_dataset

issues = load_dataset("HuggingFaceTB/github-issues-notebooks", "issues", split="train")  # GitHub Issues
kaggle_notebooks = load_dataset("HuggingFaceTB/github-issues-notebooks", "kaggle", split="train")  # Kaggle Notebooks
```

## Dataset curation
These curation details are from the StarCoder2 pipeline. The original datasets can be found at: https://huggingface.co/datasets/bigcode/starcoder2data-extras and more details can be found in the StarCoder2 paper.

### 🐛 GitHub Issues
The GitHub Issues dataset consists of discussions from GitHub repositories, sourced from GHArchive. It contains issue reports, bug tracking, and technical Q&A discussions.

To ensure high-quality data, the StarCoder2 processing pipeline included:

- Removing bot-generated comments and auto-replies from email responses.
- Filtering out short issues (<200 characters) and extremely long comments.
- Keeping only discussions with multiple users (or highly detailed single-user reports).
- Anonymizing usernames while preserving the conversation structure, names, emails, keys, passwords, IP addresses using [StarPII](https://huggingface.co/bigcode/starpii).

We format the conversatiosn using this template:

```
Title: [Issue title]

Question:
username_0: [Issue content]

Answers:
username_1: [Answer from user 1]
username_0: [Author reply]
username_2: [Answer from user 2]
...
Status: Issue closed (optional)
```

## 📊 Kaggle Notebooks
The Kaggle Notebooks are sourced from the [Meta Kaggle Code](https://www.kaggle.com/datasets/kaggle/meta-kaggle-code) dataset, licensed under Apache 2.0. They were cleaned using a multi-step filtering process, which included:

- Removing notebooks with syntax errors or less than 100 characters.
- Extracting metadata for notebooks that reference Kaggle datasets. When possible, we retrieve the datasets references in the notebook and add information about them to the beginning of the notebook (description, `ds.info()` output and 4 examples)
- Filtering out duplicates, which reduced the dataset volume by 78%, and redacting PII.
Each notebook is formatted in Markdown format, where we start with the notebook title, dataset description when available and put the notebook (converted to a Python script) in a code block. 

Below is an example of a kaggle notebook:

````
# Iris Flower Dataset

### Context
The Iris flower data set is a multivariate data set introduced ... (truncated)

```python
import pandas as pd

df = pd.read_csv('iris-flower-dataset/IRIS.csv')
df.info()
```
```
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150 entries, 0 to 149
Data columns (total 5 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   sepal_length  150 non-null    float64
 1   sepal_width   150 non-null    float64
 2   petal_length  150 non-null    float64
 3   petal_width   150 non-null    float64
 4   species       150 non-null    object 
dtypes: float64(4), object(1)
memory usage: 6.0+ KB
```

Examples from the dataset:
```
{
    "sepal_length": 5.1,
    "sepal_width": 3.5,
    "petal_length": 1.4,
    "petal_width": 0.2,
    "species": "Iris-setosa"
}
... (truncated)
```

Code:
```python
import numpy as np  # linear algebra
import pandas as pd  # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
import os

for dirname, _, filenames in os.walk("/kaggle/input"):
    for filename in filenames:
        print(os.path.join(dirname, filename))
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
import matplotlib.pyplot as plt

data = pd.read_csv("/kaggle/input/iris-flower-dataset/IRIS.csv")
data.head()
X = data.drop("species", axis=1)
... (truncated)
````

## Citation
```
@article{lozhkov2024starcoder,
  title={Starcoder 2 and the stack v2: The next generation},
  author={Lozhkov, Anton and Li, Raymond and Allal, Loubna Ben and Cassano, Federico and Lamy-Poirier, Joel and Tazi, Nouamane and Tang, Ao and Pykhtar, Dmytro and Liu, Jiawei and Wei, Yuxiang and others},
  journal={arXiv preprint arXiv:2402.19173},
  year={2024}
}
```