README.md

---
pretty_name: MediaWiki Code2Code Search
license: other
license_name: apache-2.0-tooling-plus-upstream-foss
license_link: LICENSE.md
language:
- code
task_categories:
- text-retrieval
- sentence-similarity
- feature-extraction
tags:
- code
- code-search
- code-retrieval
- semantic-search
- neural-retrieval
- mediawiki
- wikimedia
- faiss
- embeddings
- bm25
- swhid
- software-heritage
size_categories:
- 1M<n<10M
source_datasets:
- original
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*.parquet
---

# MediaWiki Code2Code Search — Dataset

Pre-computed retrieval artifacts for **MediaWiki Code2Code Search**, a neural system for the
semantic discovery of open-source software entities (functions, types, templates) across the
MediaWiki / Wikimedia ecosystem.

This Hugging Face dataset is a **complementary mirror** of the pre-computed artifacts archived on
Zenodo ([10.5281/zenodo.20586256](https://doi.org/10.5281/zenodo.20586256)). The Hugging Face copy
makes the corpus **browsable in the dataset Viewer** and easy to pull with the `datasets` /
`huggingface_hub` libraries.

> **If you use this dataset, please cite the paper** (see [Citation](#citation)). Zenodo is the
> archival home of the artifacts; the paper is the reference to cite.

- **Code / system:** <https://github.com/ftosoni/mediawiki-code2code-search>
- **Paper:** *MediaWiki Code2Code Search: Neural Retrieval for the Semantic Discovery of
  Open-Source Software Entities* — Francesco Tosoni, Sant'Anna School of Advanced Studies, Pisa.
  A peer-reviewed version is **forthcoming in *SoftwareX***. See [Citation](#citation).

---

## Contents

| File | Size | Format | Description |
|---|---|---|---|
| `data/train-*.parquet` (9 shards) | ~172 MiB | Parquet (zstd) | The full `snippets` table, exposed for the **dataset Viewer** and `load_dataset`. Same 1,289,452 rows as `snippets.db`. |
| `snippets.db` | 1.74 GiB | SQLite | Serving metadata + code for every entity (single `snippets` table). |
| `embeddings.npy` | 4.92 GiB | NumPy `float32` | Dense embeddings, shape `(1289452, 1024)`, **L2-normalised**, from `Qwen/Qwen3-Embedding-0.6B`. Row *i* aligns with `snippets.db` row `id = i`. |
| `mediawiki.index` | 168.6 MiB | FAISS | `IndexIVFPQ` (L2) with an `IndexFlatL2` coarse quantizer, 128 sub-quantizers × 8 bits, dim 1024. Built over the embeddings above. |
| `bm25_index.pkl` | 423.6 MiB | Pickle | BM25 lexical baseline built over the `code` field (identifier tokeniser; keywords and tokens shorter than three characters dropped). |

> **Note on `.npy` / `.index` / `.pkl`:** these binary artifacts do not render in the Viewer by
> design; the Parquet shards provide the browsable view of the same underlying rows.
>
> **Also on Zenodo only:** the raw pre-migration corpus `raw_snippets.json` (~1.75 GiB) lives in the
> [Zenodo record](https://doi.org/10.5281/zenodo.20586256) but is intentionally **not** mirrored here
> — its content is already captured by `snippets.db` and the Parquet shards.

### Data fields (`snippets` table / Parquet)

| Column | Type | Description |
|---|---|---|
| `id` | int64 | Row id; aligns with the corresponding row of `embeddings.npy`. |
| `original_id` | string | Upstream extraction id (content hash). |
| `swhid` | string | **[SWHID](https://www.swhid.org/)** of the source content, with `origin=` and `lines=` qualifiers — resolvable on [Software Heritage](https://archive.softwareheritage.org/) for exact provenance and upstream licensing. |
| `sha1` | string | SHA-1 of the source blob. |
| `repo_name` | string | Upstream repository name. |
| `repo_group` | string | Repository group / namespace. |
| `filepath` | string | Path of the source file within the repository. |
| `name` | string | Entity name / signature (e.g. `check_dumps_log_path(path)`). |
| `type` | string | One of `function`, `type`, `template`. |
| `code` | string | Source snippet text for the entity (see [Licensing](#licensing-important)). |

### Corpus statistics

- **1,289,452** structural entities:
  - `function`: 1,050,748
  - `type`: 237,653
  - `template`: 1,051
- **2,242** active repositories (distinct `(repo_group, repo_name)` pairs) across the MediaWiki /
  Wikimedia ecosystem.
- **12** programming languages extracted via language-specific structural parsing.
- Embedding model: **`Qwen/Qwen3-Embedding-0.6B`** (1024-dim). Retrieval is nearest-neighbour by
  **Euclidean (L2) distance** over L2-normalised vectors.

---

## Usage

Browse the corpus in the **Viewer** above, or load it programmatically.

**Tabular rows (Parquet) with `datasets`:**

```python
from datasets import load_dataset

ds = load_dataset("ftosoni/mediawiki-code2code-search", split="train")
print(ds)
print(ds[0]["name"], ds[0]["type"])
print(ds[0]["swhid"])
```

**Raw serving artifacts with `huggingface_hub`:**

```python
from huggingface_hub import hf_hub_download

repo = "ftosoni/mediawiki-code2code-search"
db    = hf_hub_download(repo, "snippets.db",      repo_type="dataset")
emb   = hf_hub_download(repo, "embeddings.npy",   repo_type="dataset")
index = hf_hub_download(repo, "mediawiki.index",  repo_type="dataset")
bm25  = hf_hub_download(repo, "bm25_index.pkl",   repo_type="dataset")

import numpy as np, faiss
X = np.load(emb, mmap_mode="r")        # (1289452, 1024) float32, L2-normalised
faiss_index = faiss.read_index(index)  # IndexIVFPQ (L2)
```

`embeddings.npy` row `i` corresponds to `snippets.db` row `id = i` and to FAISS vector `i`.

---

## Licensing (important)

This dataset combines two distinct layers, and they carry **different licenses**:

1. **Tooling / packaging** (schema, indexes, embeddings, this card): **Apache-2.0**, matching the
   canonical [Zenodo record](https://doi.org/10.5281/zenodo.20586256).
2. **The `code` snippets** in `snippets.db` / the Parquet shards are **derived from upstream
   MediaWiki / Wikimedia repositories**, each under **its own free/open-source license**
   (predominantly **GPL-2.0-or-later**, with some MIT / BSD / Apache-2.0 and others). These
   upstream licenses govern the snippet text; redistribution here relies on their permission to
   redistribute.

There is **no per-row `license` column**. Instead, **every entity carries an `swhid`** with an
`origin=` qualifier, so the exact upstream repository — and therefore its authoritative license and
copyright — can be resolved on [Software Heritage](https://archive.softwareheritage.org/) and at
the origin repository. Snippets are fragments and may not carry their file's original license
header; consult the origin before reuse.

See [`LICENSE.md`](LICENSE.md) for the full statement.

---

## Citation

Please cite the paper:

```bibtex
@misc{tosoni2026mediawikicode2codesearchneural,
  title         = {MediaWiki Code2Code Search: Neural Retrieval for the Semantic Discovery of Open-Source Software Entities},
  author        = {Francesco Tosoni},
  year          = {2026},
  eprint        = {2607.26766},
  archivePrefix = {arXiv},
  primaryClass  = {cs.IR},
  url           = {https://arxiv.org/abs/2607.26766}
}
```

The pre-computed artifacts are archived on Zenodo:
[10.5281/zenodo.20586256](https://doi.org/10.5281/zenodo.20586256). A peer-reviewed version of the
paper is forthcoming in *SoftwareX*.

---

## Acknowledgements

Corpus derived from the MediaWiki / Wikimedia software ecosystem. Provenance is tracked with
[SWHID](https://www.swhid.org/) identifiers resolvable on
[Software Heritage](https://archive.softwareheritage.org/). Embeddings computed with
[`Qwen/Qwen3-Embedding-0.6B`](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B); indexing with
[FAISS](https://github.com/facebookresearch/faiss).