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electron-rare/mascarade-spice-dataset

Mascarade — SPICE & Analog Simulation Q&A Description Q&A bilingue (FR/EN) sur la simulation SPICE et l'analyse de circuits analogiques : ngspice, LTspice, modèles MOSFET/BJT, topologies analogiques, ampli-op, filtres actifs, sources de courant, polarisation. 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… See the full description on the dataset page: https://huggingface.co/datasets/electron-rare/mascarade-spice-dataset.

sourceHugging Facecc-by-sa-4.0updated 5mo agoView on Hugging Face
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Dataset Card

Mascarade — SPICE & Analog Simulation Q&A

Description

Q&A bilingue (FR/EN) sur la simulation SPICE et l'analyse de circuits analogiques : ngspice, LTspice, modèles MOSFET/BJT, topologies analogiques, ampli-op, filtres actifs, sources de courant, polarisation.

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)

Publicly available datasets

  • —Derived from [Masala-CHAI dataset](https://github.com/jitendra-bhandari/Masala-CHAI) (arXiv:2411.14299, CC-BY-4.0). Original Masala-CHAI is itself derived from textbooks — verify upstream copyright before commercial use.

Web scraping

None directly performed by this dataset; upstream Masala-CHAI used textbook sources.

Synthetically generated

  • —Light LLM augmentation (rephrasing, FR translation) on top of the Masala-CHAI base.

Licensed data

None.

Sample format

json
{
  "conversations": [
    {
      "from": "system",
      "value": "You are an expert analog circuit designer and SPICE simulation engineer. You write correct ngspice/LTspice netlists, debug convergence issues, perform AC/DC/transient analysis, and automate simulations with PySpice. You provide complete netlists with correct device models, proper analysis directives, and clear explanations of circuit behavior."
    },
    {
      "from": "human",
      "value": "Write a SPICE netlist for a common-emitter amplifier with voltage divider bias. Include AC analysis from 10Hz to 10MHz."
    },
    {
      "from": "gpt",
      "value": "Common-emitter amplifier with voltage divider bias:\n\n```spice\n* Common-Emitter Amplifier with Voltage Divider Bias\n* Vcc = 12V, Gain ≈ -100, Ic ≈ 1mA\n\n.model 2N2222 NPN (IS=14.34f BF=255.9 VAF=74.03 IKF=0.2847\n+ ISE=14.34f NE=1.307 BR=6.092 VAR=28 IKR=0 ISC=0\n+ NC=2 RB=10 RE=0 RC=1 CJE=22.01p CJC=7.306p TF=0.4ns)\n\n* Power supply\nVCC vcc 0 12\n\n* Voltage divider bias: R1/R2 set Vb ≈ 2.7V → Ic ≈ 1mA\nR1 vcc base 22k\nR2 base 0 4.7k\n\n* Transistor\nQ1 collector base emitter 2N2222\n\n* Collector resistor: Rc = (Vcc - Vce) / Ic ≈ 6.8k for Vce ≈ 5.2V\nRC vcc collector 6.8k\n\n* Emitter resistor (DC stability) + bypass cap (AC gain)\nRE emitter 0 680\nCE emitter 0 100u\n\n* Input coupling\nVin in 0 AC 10m SIN(0 10m 1k)\nCin in base 10u\n\n* Output coupling\nCout collector out 10u\nRL out 0 10k\n\n* Analysis\n.op\n.a..."
    }
  ]
}

Statistics

MétriqueValeur
Total samples3,091
Size4.00 MB
Formatjsonl (ShareGPT conversations)
LanguagesFrench / English mix
Filespice_chat.jsonl

Usage

python
from datasets import load_dataset

ds = load_dataset("electron-rare/mascarade-spice-dataset")
print(ds["train"][0]["conversations"])

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

yaml
# axolotl config
datasets:
  - path: electron-rare/mascarade-spice-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.

Copyright considerations

  • —Masala-CHAI base content: CC-BY-4.0 (attribution preserved above).
  • —Synthetic LLM augmentations: belong to dataset author per OpenAI/Anthropic ToS.
  • —Verify upstream textbook copyright before commercial use.

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_kicad9plus.md` for the legal-attribution audit performed on 2026-05-11.

Citation

bibtex
@dataset{electron_rare_spice_2026,
  author    = {electron-rare},
  title     = { Mascarade — SPICE & Analog Simulation Q&A },
  year      = {2026},
  publisher = {Hugging Face},
  license   = {CC-BY-SA-4.0},
  url       = {https://huggingface.co/datasets/electron-rare/mascarade-spice-dataset}
}

Related datasets

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