README.md

---
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