| | --- |
| | license: mit |
| | task_categories: |
| | - text-generation |
| | language: |
| | - en |
| | tags: |
| | - code |
| | pretty_name: GoodDocs-v0 |
| | size_categories: |
| | - 100K<n<1M |
| | --- |
| | |
| | # GoodDocs-v0: High-quality code documentation texts |
| |
|
| | GoodDocs-v0 is a text dataset scraped from high-quality documentation sources in the open-source ecosystem, in particular the top 1000 GitHub repositories by stars. It is designed to serve as a foundation for building reasoning systems grounded in software documentation, enabling tasks such as: |
| |
|
| | - Code and API understanding |
| | - Documentation question answering and retrieval |
| | - Planning and tool-use grounded in docs |
| | - Long-context reasoning over multi-file documentation |
| |
|
| | ## What's in this repository |
| |
|
| | - `cleaned_texts_on_metadata_only.parquet` — per-file Markdown documents and metadata extracted from documentation trees. |
| | - `awesome-repos.parquet` — structured links extracted from Awesome lists-of-lists (`name`, `link`, `description`, `source_repo`, optional `stars`). |
| | - `data_collection_utils/` — utilities to regenerate the dataset: |
| | - `scrape_gh_docs.py` — main scraper/collector for documentation from GitHub repositories. |
| | - `scrape_gh_docs_config.yaml` — reproducible configuration (inputs, outputs, filters, strategies). |
| | - `github_links.txt` — the seed list of GitHub repositories (e.g., top repositories by stars). |
| | - `awesome_final_repos.py` — extractor for non-"awesome" repositories referenced by Awesome lists. |
| | - `awesome_scrap_config.yaml` — configuration for `awesome_final_repos.py` (root, depth, output, cache, workers, optional `fetch_stars`). |
| | - `top_1000_repos.py` — helper to refresh the top‑repositories list via the public site referenced in the code. |
| |
|
| | ## Schema |
| |
|
| | cleaned_texts_on_metadata_only.parquet — one row per Markdown file (see `md_rows` assembly in `main()`): |
| |
|
| | - `owner`, `repo`, `repo_dir` |
| | - `file_rel_repo` — path relative to the saved repo root |
| | - `file_rel_outdir` — path relative to `outdir` |
| | - `size` — file size in bytes |
| | - `mtime` — file modification time (epoch seconds) |
| | - `lang` — language prediction field (via `langid.py` when language filtering is enabled) |
| | - `content` — raw Markdown text |
| |
|
| | ## Quickstart |
| |
|
| | Load the dataset with pandas: |
| |
|
| | ```python |
| | import pandas as pd |
| | df = pd.read_parquet("cleaned_texts_on_metadata_only.parquet") |
| | print(len(df), "rows") |
| | print(df.columns.tolist()) |
| | ``` |
| |
|
| | Typical uses: |
| |
|
| | - Retrieval corpora for doc QA and RAG pipelines |
| | - Supervision for instruction tuning grounded in docs |
| | - Long-context model evaluation with real project documentation |
| |
|
| | ## Reproducing the dataset |
| |
|
| | The scraper is configurable and designed to be reproducible via `data_collection_utils/scrape_gh_docs_config.yaml`. |
| |
|
| | 1) Prerequisites |
| | - System tools: `git` |
| | - Python 3.11+ packages: `pandas`, `pyarrow`, `requests`, `tqdm`, `PyYAML`, `langid` |
| | - For refreshing top repositories (optional): `playwright` (and `playwright install` for a browser) |
| | - A GitHub API token in the environment (`GITHUB_TOKEN`) or a file referenced by the config (`token_file`) |
| |
|
| | 2) Inputs |
| | - `data_collection_utils/github_links.txt` — list of repositories to process (either `owner/repo` or full URLs) |
| | - You can refresh this list with `data_collection_utils/top_1000_repos.py` if desired. |
| |
|
| | 3) Run |
| |
|
| | ```bash |
| | python3 data_collection_utils/scrape_gh_docs.py |
| | # or to rebuild Parquet(s) from existing downloads without any network calls: |
| | python3 data_collection_utils/scrape_gh_docs.py --no-fetch |
| | ``` |
| |
|
| | Configuration (YAML-driven; see `data_collection_utils/scrape_gh_docs_config.yaml`): |
| |
|
| | - `input` — path to a file containing one repo per line (owner/repo or full URL) |
| | - `outdir`, `md_failed`, `texts_parquet` |
| | - `workers`, `dry_run`, `quiet`, `no_fetch` |
| | - `token_file` — GitHub token location (or set `GITHUB_TOKEN` env var) |
| | - `prefer_sparse`, `prefer_zip`, `only_md`, `min_repo_age_years` |
| | - `lang_filter`, `min_text_chars` — control language gating in `cleaned_texts_on_metadata_only.parquet` |
| |
|
| | Output is written to `<outdir>/cleaned_texts_on_metadata_only.parquet`. |
| |
|
| | ## Awesome list extraction |
| |
|
| | `data_collection_utils/awesome_final_repos.py` crawls the Awesome list-of-lists and extracts final repositories (those whose repo names do not include "awesome"). For each bullet entry like: |
| |
|
| | ``` |
| | * [Fuse](https://github.com/owner/repo) - Mobile development tools. |
| | ``` |
| |
|
| | It records: |
| |
|
| | - `name`: the markdown link text (e.g., `Fuse`). |
| | - `link`: canonical GitHub repository URL (e.g., `https://github.com/owner/repo`). |
| | - `description`: text after the ` - ` dash, or the rest of the line (with the link and bullet removed) if no dash. |
| | - `stars` (optional): repository stargazers count when enabled. |
| |
|
| | Configuration is YAML-first via `data_collection_utils/awesome_scrap_config.yaml`: |
| |
|
| | - `root`: root Awesome repository URL, e.g., `https://github.com/sindresorhus/awesome`. |
| | - `depth`: recursion depth for nested Awesome lists (0 = only root). |
| | - `output_dir`: directory for `awesome-repos.parquet`. |
| | - `cache_dir`: directory for README fetch caches. |
| | - `workers`: concurrency for network requests. |
| | - `fetch_stars`: when `true`, also fetch stargazers for each parsed repo (makes extra API calls) and include a `stars` column. |
| |
|
| | Run: |
| |
|
| | ```bash |
| | python3 data_collection_utils/awesome_final_repos.py |
| | # or adjust via YAML first, then run without flags |
| | ``` |
| |
|
| | Schema of `awesome-repos.parquet`: |
| |
|
| | - `name` — link text from the Awesome entry. |
| | - `link` — canonical GitHub URL (<https://github.com/owner/repo>). |
| | - `description` — description text without the leading ` - ` and without repeating the name. |
| | - `source_repo` — the Awesome list repository where the entry was found, formatted as `owner/repo`. |
| | - `stars` — integer, optional; only present when `fetch_stars: true`. |
| |
|
| | ## Language filtering |
| |
|
| | Language detection is performed with `langid.py` (see imports in `data_collection_utils/scrape_gh_docs.py`). The default configuration keeps English-only files (`lang_filter: en`). There is no probability/confidence threshold; we gate by the predicted language label and a minimum text length (`min_text_chars`). |
| |
|
| | ## Licensing |
| |
|
| | - Code and dataset scaffolding in this repository are under the MIT license (see frontmatter). |
| | - The original documentation content belongs to the respective upstream projects and remains governed by their licenses. Please consult each repository’s license before redistribution or commercial use. |
| |
|
| | ## Acknowledgements |
| |
|
| | This dataset draws from the open-source community’s documentation efforts. The seed list targets highly-starred repositories to bias toward quality, breadth, and maturity. |
| |
|
| | Note to self: `size` distribution: 20th percentile - 363 symbols, 50p - 701, 95p - 17392 |
| |
|
