| | --- |
| | 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} |
| | } |
| | ``` |
| |
|
