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---
dataset_info:
features:
- name: project
dtype: string
- name: source
dtype: string
- name: language
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 5421472234
num_examples: 59733
download_size: 1850870873
dataset_size: 5421472234
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: apache-2.0
---
# Dataset Card
This dataset is the code documenation dataset used in [StarCoder2](https://huggingface.co/papers/2402.19173) pre-training, and it is also part of the-stack-v2-train-extras descried in the paper.
## Dataset Details
### Overview
This dataset comprises a comprehensive collection of crawled documentation and code-related resources sourced from various package manager platforms and programming language documentation sites. It focuses on popular libraries, free programming books, and other relevant materials, facilitating research in software development, programming language trends, and documentation analysis.
### How to Use it
```python
from datasets import load_dataset
ds = load_dataset("SivilTaram/starcoder2-documentation")
```
### Data Fields
- **`project`** (`string`): The name or identifier of the project on each platform.
- **`source`** (`string`): The platform from which the documentation data is sourced.
- **`language`** (`string`): The identified programming language associated with the project.
- **`content`** (`string`): The text content of each document, formatted in Markdown.
### Related Resources
For additional tools and methods related to converting HTML to Markdown, refer to the GitHub repository: [code-html-to-markdown](https://github.com/SivilTaram/code-html-to-markdown).
### Data Sources
1. **Package Managers:**
- **npm:** Node.js package manager.
- **PyPI:** Python Package Index.
- **Go Packages:** Go programming language packages.
- **Packagist:** PHP package repository.
- **Rubygems:** Ruby package manager.
- **Cargo:** Rust package manager.
- **CocoaPods:** Dependency manager for Swift and Objective-C Cocoa projects.
- **Bower:** Front-end package manager.
- **CPAN:** Comprehensive Perl Archive Network.
- **Clojars:** Clojure library repository.
- **Conda:** Package manager for data science and scientific computing.
- **Hex:** Package manager for the Elixir programming language.
- **Julia:** Package manager for the Julia programming language.
2. **Documentation Websites:**
- A carefully curated list of programming-related websites, including Read the Docs and other well-known resources.
3. **Free Programming Books:**
- Sources from the **Free Programming Books** project, which promotes the availability of free programming e-books across various languages.
### Data Collection Process
1. **Library Retrieval:**
- The process begins by identifying the most popular libraries across the aforementioned platforms using [libraries.io](libraries.io).
- These library names serve as search queries to obtain their respective homepages.
2. **Documentation Extraction:**
- **Homepage Links:** Documentation files are crawled from the retrieved homepage links. If no dedicated documentation is found, README or equivalent files on the package manager platforms are utilized.
- **Processing Strategy:** For documents obtained through homepage links, the same processing strategy is applied as outlined for website crawls, ensuring consistent formatting and extraction quality.
- **Prioritization:** For libraries hosted on PyPI and Conda, documentation on [Read the Docs](https://about.readthedocs.com/) is prioritized due to its comprehensive nature.
3. **PDF Extraction:**
- For R language documentation, text is extracted from all PDFs hosted on **CRAN** using the **pdftotext** library, which effectively preserves formatting.
- For LaTeX packages, documentation, tutorials, and usage guide PDFs from **CTAN** are filtered, excluding image-heavy PDFs, and converted to markdown using the **Nougat** neural OCR tool.
4. **Web Crawling:**
- Code documentation is collected from a curated list of websites by exploring from an initial URL, and the full list of all URLs can be found in the StarCoder2 paper.
- A dynamic queue is employed to store URLs within the same domain, expanding as new links are discovered during the crawl.
- The process focuses on (1) **content extraction** and (2) **content concatenation**:
- **Content Extraction:** HTML pages are converted to XML using the **trafilatura** library, which eliminates redundant navigation elements.
- **Content Concatenation:** Extracted content from different HTML pages is subjected to near-duplication checks using the **minhash locality-sensitive hashing** technique, applying a threshold of 0.7 to ensure unique content is retained.
5. **Free Textbooks:**
- The dataset includes free programming books collected from the [Free Programming Books Project](https://github.com/EbookFoundation/free-programming-books). Links with a PDF extension are extracted, and all available PDFs are downloaded and processed for text extraction using the **pdf2text** library.
6. **Language Identification:**
- A dual approach is utilized to identify the primary programming language of each document:
- **Predefined Rules:** Applied when the document's source explicitly corresponds to a specific programming language.
- **Guesslang Library:** Used in cases where the correspondence is not clear.
### Dataset Characteristics
- **Languages Covered:** English, Chinese, Japanese, Spanish, and others.
- **Document Types:**
- Code documentation files
- PDF documents
- HTML pages
- E-books
- **Programming Languages Included:**
- Python
- JavaScript
- Rust
- R
- Go
- PHP
- Ruby
- Haskell
- Objective-C
- SQL
- YAML
- TeX
- Markdown
- And more...
### Use Cases
- Analyzing trends in programming language documentation.
- Researching software development resources across multiple platforms.
- Training large language models on documentation datasets to better understand programming languages.
- Understanding the structure and accessibility of programming documentation.
## Citation
```bibtex
@article{DBLP:journals/corr/abs-2402-19173,
author = {Anton Lozhkov and
Raymond Li and
Loubna Ben Allal and
Federico Cassano and
Joel Lamy{-}Poirier and
Nouamane Tazi and
Ao Tang and
Dmytro Pykhtar and
Jiawei Liu and
Yuxiang Wei and
Tianyang Liu and
Max Tian and
Denis Kocetkov and
Arthur Zucker and
Younes Belkada and
Zijian Wang and
Qian Liu and
Dmitry Abulkhanov and
Indraneil Paul and
Zhuang Li and
Wen{-}Ding Li and
Megan Risdal and
Jia Li and
Jian Zhu and
Terry Yue Zhuo and
Evgenii Zheltonozhskii and
Nii Osae Osae Dade and
Wenhao Yu and
Lucas Krau{\ss} and
Naman Jain and
Yixuan Su and
Xuanli He and
Manan Dey and
Edoardo Abati and
Yekun Chai and
Niklas Muennighoff and
Xiangru Tang and
Muhtasham Oblokulov and
Christopher Akiki and
Marc Marone and
Chenghao Mou and
Mayank Mishra and
Alex Gu and
Binyuan Hui and
Tri Dao and
Armel Zebaze and
Olivier Dehaene and
Nicolas Patry and
Canwen Xu and
Julian J. McAuley and
Han Hu and
Torsten Scholak and
S{\'{e}}bastien Paquet and
Jennifer Robinson and
Carolyn Jane Anderson and
Nicolas Chapados and
et al.},
title = {StarCoder 2 and The Stack v2: The Next Generation},
journal = {CoRR},
volume = {abs/2402.19173},
year = {2024},
url = {https://doi.org/10.48550/arXiv.2402.19173},
doi = {10.48550/ARXIV.2402.19173},
eprinttype = {arXiv},
eprint = {2402.19173},
timestamp = {Tue, 06 Aug 2024 08:17:53 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2402-19173.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` |