Ailiance-fr/mascarade-kicad-dataset

Mascarade — KiCad EDA Q&A

✅ ATTRIBUTION AUDIT COMPLETED (2026-05-11)

Per-sample Stack Exchange Electronics attribution recovered via the SE /search/advanced + /questions/{id} API search :

• 146 samples (~5.52 %) confirmed as Stack Exchange Electronics (CC-BY-SA-4.0) — fully attributed in metadata.stack_exchange_attribution (URL + author display name + author user_id + post_id + creation_date_unix + match_confidence ≥ 0.60).
• 386 samples (~14.59 %) marked metadata.attribution_recovery=not_found_on_se (stylistically resemble SE Electronics questions but no matching post returned by the SE /search/advanced API — probable synthetic/curated content).
• 5 samples (~0.19 %) marked metadata.attribution_recovery=low_confidence_match (API returned a candidate, but match score < 0.60 — kept as candidate URL only).
• 2 108 samples (~79.7 %) synthetic LLM-generated or unique to this dataset (no SE attribution required).

Original heuristic estimate of "~30 % SE" was over-counted by ~5.6× (style ≠ source). The heuristic flagged any first-person + question-mark + length-appropriate prompt as "SE-style", but only a small fraction of those flagged samples actually originate from a real SE Electronics post.

Methodology and full audit trail: docs/audit_mascarade_se_attribution.md

If you author a Stack Exchange Electronics post and find your content in this dataset without proper attribution, contact c.saillant@gmail.com for prompt correction or removal. We honor Article 4(3) DSM Directive opt-outs.

Description

Q&A bilingue (FR/EN) sur KiCad EDA : schematic capture, layout PCB, footprints, symboles, ERC/DRC, BOM, scripting Python, plugins, et fabrication outputs (Gerber, drill, pick-and-place).

Ce dataset fait partie de la famille Mascarade, un corpus thématique destiné au fine-tuning LoRA de modèles compacts (cible : Gemma-3n-E4B et équivalents) pour des assistants spécialisés en électronique embarquée.

Format : JSONL « ShareGPT-style » avec un tour system (rôle d'expert), un tour human (question) et un tour gpt (réponse complète, code compilable, schémas ASCII si pertinent).

Data sources (EU AI Act Template — AI Office, July 2025)

Distribution réelle des sources, mesurée le 2026-05-11 sur 2 645 samples :

Source	Samples	%	Attribution status
Stack Exchange Electronics	146	5.52 %	Per-sample URL + author + post_id (CC-BY-SA-4.0)
Style SE / not found on API	386	14.59 %	Marked attribution_recovery=not_found_on_se
Low-confidence SE candidate	5	0.19 %	Marked attribution_recovery=low_confidence_match
Synthetic LLM / unique content	2 108	79.7 %	No external attribution required

Publicly available datasets

None.

Web scraping

• 5.52 % scraped from Stack Exchange Electronics (CC-BY-SA-4.0) — per-sample URL + author + post_id + creation_date_unix preserved in metadata.stack_exchange_attribution.

Synthetically generated

• ~79.7 % generated by LLM for domain-specific Q&A.

Licensed data

None.

Sample of confirmed Stack Exchange attribution

{
  "metadata": {
    "stack_exchange_attribution": {
      "url": "https://electronics.stackexchange.com/questions/110/how-to-drive-brushless-motor-with-arduino",
      "author_display_name": "tuupola",
      "author_user_id": 94,
      "post_id": 110,
      "creation_date_unix": 1257094040,
      "license": "CC-BY-SA-4.0",
      "matched_via": "api_search",
      "match_confidence": 1.0
    },
    "attribution_recovery": "matched_on_se"
  }
}

For samples that look stylistically like SE Electronics but had no API match:

{
  "metadata": {
    "attribution_recovery": "not_found_on_se",
    "attribution_recovery_note": "Stylistically resembled a Stack Exchange Electronics question, but no matching post returned by the SE /search/advanced API. Likely synthetic/curated."
  }
}

Sample format

{
  "conversations": [
    { "from": "system", "value": "You are an expert PCB design engineer and KiCad specialist. ..." },
    { "from": "human",  "value": "How do I calculate microstrip impedance for a 50Ω trace on a 4-layer PCB with FR4?" },
    { "from": "gpt",    "value": "Microstrip impedance calculation for 50Ω on FR4: ..." }
  ],
  "metadata": { /* attribution + license, see above */ }
}

Statistics

Métrique	Valeur
Total samples	2 645
Size	~6.06 MB
Format	jsonl (ShareGPT conversations)
Languages	French / English mix
File	kicad_chat.jsonl
SE-attributed	61 (2.31 %) — full URL + author
Not-found-on-SE	169 (6.39 %) — marked
Synthetic / unique	2 413 (91.23 %)

Usage

from datasets import load_dataset

ds = load_dataset("Ailiance-fr/mascarade-kicad-dataset")
print(ds["train"][0]["conversations"])

# Filter samples that need explicit SE attribution
se_samples = ds["train"].filter(
    lambda s: (s.get("metadata") or {}).get("attribution_recovery") == "matched_on_se"
)
print(f"SE-attributed samples: {len(se_samples)}")

Pour un fine-tuning ShareGPT-style direct (axolotl, unsloth, mlx-lm) :

# axolotl config
datasets:
  - path: Ailiance-fr/mascarade-kicad-dataset
    type: sharegpt
    conversation: chatml

Licenses applied

This aggregated dataset is released under CC-BY-SA-4.0. Per-sample original licenses preserved in metadata.license when known. SE Electronics samples are individually attributed under CC-BY-SA-4.0 (compatible with the umbrella license).

Copyright considerations

• Stack Exchange Electronics content (61 samples): CC-BY-SA-4.0, with full per-sample URL + author attribution.
• Synthetic LLM outputs: belong to the dataset author per OpenAI/Anthropic ToS.

Opt-out: contact c.saillant@gmail.com. We respect TDMRep, robots.txt, and noai/noimageai signals. We honor Article 4(3) DSM Directive opt-outs.

License & EU AI Act

CC-BY-SA-4.0 (attribution + sharealike).

Données collectées et générées dans le cadre du projet electron-rare (fine-tuning LoRA sur Gemma-3n-E4B pour applications électronique embarquée).

Compatible EU AI Act : voir les signataires du GPAI Code of Practice (Anthropic, Mistral, Google) et la documentation transparence du projet : electron-bench.

Audit log: see docs/audit_mascarade_se_attribution.md for the SE attribution audit performed on 2026-05-11, and docs/audit_kicad9plus.md for the kicad9plus split audit.

Citation

@dataset{electron_rare_kicad_2026,
  author    = {electron-rare},
  title     = { Mascarade — KiCad EDA Q&A },
  year      = {2026},
  publisher = {Hugging Face},
  license   = {CC-BY-SA-4.0},
  url       = {https://huggingface.co/datasets/Ailiance-fr/mascarade-kicad-dataset}
}

Related datasets

Famille electron-rare/mascarade-* couvrant : STM32, SPICE, KiCad, IoT, Power, DSP, EMC, embedded.

• mascarade-stm32-dataset
• mascarade-spice-dataset
• mascarade-kicad-dataset
• mascarade-iot-dataset
• mascarade-power-dataset
• mascarade-dsp-dataset
• mascarade-emc-dataset
• mascarade-embedded-dataset
• kill-life-embedded-qa — Q&A spécifique au projet Kill_LIFE
• kicad9plus-permissive — KiCad sch corpus, permissive licenses (CC-BY-SA-4.0)
• kicad9plus-copyleft — KiCad sch corpus, copyleft licenses (GPL-3.0-or-later)

Used to train models evaluated in ailiance/ailiance-bench v0.2

This dataset contributes to training data for hardware-domain LoRA adapters benchmarked in the Ailiance bench suite.

Phase 6 scoreboard verdicts (7-task KiCad/SPICE evaluation):

• 🥇 eu-kiki: champion 4/7 tasks (DSL/PCB/SPICE/extract)
• 🥇 mascarade-embedded: champion P3 extraction (+48 pts)
• ⚠️ mascarade-kicad: catastrophic forgetting on SPICE/P2/P3

See full scoreboard: ailiance-bench README#scoreboard-lora-phase-6.