--- 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() ``` ``` 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} } ```