README.md

metadata

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 model training corpus, precisely the bigcode/StarCoder2-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.
• 📊 Kaggle Notebooks – 1.7B tokens of data analysis notebooks in markdonw format, curated from Kaggle's Meta Kaggle Code dataset. These datasets have undergone filtering to remove low-quality content, duplicates and PII. More details in StarCoder2 paper

How to load the dataset

You can load a specific subset using the following code:

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.

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 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}
}