datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
CircuitSense
CircuitSense
This dataset is a comprehensive multimodal circuit question-answering benchmark designed to evaluate visual reasoning and problem-solving capabilities across three main domains: Perception, Analysis, and Design. The dataset contains structured question-answer pairs with accompanying visual content, targeting different engineering cognitive levels and reasoning tasks.
Dataset Structure
The dataset is organized into three primary folders, each containing… See the full description on the dataset page: https://huggingface.co/datasets/armanakbari4/CircuitSense.phasor-ac-circuits
Phasor AC Circuits
This dataset contains several ac circuits defined using phasors as well as a description of the components in these circuits.
Example Circuit
circuits_raw/circuit_page_1_circuit_1.png
components/circuit_page_1_circuit_1.yaml
circuit_id: circuit_page_1_circuit_1
components:
- bbox:
h: 52
w: 52
x: 176
y: 25
component_name: capacitor
- bbox:
h: 59
w: 59
x: 258
y: 90
component_name: inductor
- bbox:
h: 52
w:… See the full description on the dataset page: https://huggingface.co/datasets/dostoevskyIdiot/phasor-ac-circuits.Circuitsense-6kanalog-circuits-sky130
Dataset Card for Analog SPICE Circuits on SKY130
130,409 ngspice-simulated SPICE netlists on the open-source SkyWater SKY130 PDK, unified from three upstream sources into a single HuggingFace-loadable schema with deterministic topology-aware train/validation/test splits. Designed as a supervised (SPICE netlist → performance metrics) corpus for training of models for analog circuit design.
Fully open toolchain. Analog-EDA datasets commonly require a commercial SPICE simulator and/or… See the full description on the dataset page: https://huggingface.co/datasets/pphilip/analog-circuits-sky130.spice-circuits-finetune-v3
SPICE Circuits Fine-Tune V3
A high-quality instruction-following dataset for fine-tuning language models to generate valid, simulation-ready SPICE netlists from natural language descriptions.
Dataset Summary
Property
Value
Total entries
12,471
Format
{"instruction": "...", "output": "..."}
PySpice validation
100% pass
ngspice simulation
99.2% pass (500-entry spot check)
Filepath leaks
0
License
Apache 2.0
What Makes V3… See the full description on the dataset page: https://huggingface.co/datasets/ADI2005/spice-circuits-finetune-v3.spice-circuits-finetune-v5spice-circuits-finetune-v2
SPICE Circuits Fine-tune V2
A clean, validated dataset of 7,410 instruction-output pairs for fine-tuning language models to generate SPICE netlists from natural language descriptions.
Dataset Description
This is Version 2 of the SPICE circuits fine-tuning dataset. V1 was polluted with mixed formats (LTspice, KiCad, standard SPICE) and no validation. V2 is fully validated — every netlist passes PySpice's SpiceParser.build_circuit() gate. No exceptions.… See the full description on the dataset page: https://huggingface.co/datasets/ADI2005/spice-circuits-finetune-v2.ltspice-spice-circuits
LTspice Netlist ↔ ASC Schematic Dataset
SPICE netlists paired with their corresponding LTspice .asc schematic files,
scraped from public GitHub repositories.
Columns
netlist (string): SPICE netlist content
asc (string): Corresponding LTspice .asc schematic file content
Splits
train: 53000 samples
test: 2790 samples
Usage
from datasets import load_dataset
ds = load_dataset("Si7li/ltspice-spice-circuits")
sample = ds['train'][0]
print("Netlist:"… See the full description on the dataset page: https://huggingface.co/datasets/Si7li/ltspice-spice-circuits.quantum-circuits-21k
Quantum Circuits Dataset — v2 (21K)
A synthetic dataset of validated natural language → OpenQASM 2.0 circuit pairs for training quantum circuit generation models. To our knowledge the largest publicly available dataset of validated NL→QASM pairs specifically designed for generative model training.
Used to train the QuantumGPT-124M model series.
Quick Start
from datasets import load_dataset
# v2 training set (21K samples, recommended)
ds =… See the full description on the dataset page: https://huggingface.co/datasets/merileijona/quantum-circuits-21k.analog-circuits-sky130
Dataset Card for Analog SPICE Circuits on SKY130
130,409 ngspice-simulated SPICE netlists on the open-source SkyWater SKY130 PDK, unified from three upstream sources into a single HuggingFace-loadable schema with deterministic topology-aware train/validation/test splits. Designed as a supervised (SPICE netlist → performance metrics) corpus for training of models for analog circuit design.
Fully open toolchain. Analog-EDA datasets commonly require a commercial SPICE simulator… See the full description on the dataset page: https://huggingface.co/datasets/gcSniper2/analog-circuits-sky130.analog-circuits-sky130
Dataset Card for Analog SPICE Circuits on SKY130
130,409 ngspice-simulated SPICE netlists on the open-source SkyWater SKY130 PDK, unified from three upstream sources into a single HuggingFace-loadable schema with deterministic topology-aware train/validation/test splits. Designed as a supervised (SPICE netlist → performance metrics) corpus for training of models for analog circuit design.
Fully open toolchain. Analog-EDA datasets commonly require a commercial SPICE simulator and/or… See the full description on the dataset page: https://huggingface.co/datasets/introvoyz041/analog-circuits-sky130.spice-circuits-finetune-v4qec-circuitsSPICE-Circuitsquantum-circuits-8k
Quantum Circuits 8K Dataset
A synthetic dataset of 8,129 quantum circuit examples for training language models to generate OpenQASM 2.0 code from natural language descriptions.
Quick Stats
Total Samples: 8,129 (description → QASM pairs)
Unique Circuits: 739 base circuits
Categories: 92 distinct quantum circuit types
Qubit Range: 1-9 qubits
Format: OpenQASM 2.0
Augmentation: 11x per circuit (original + 10 paraphrases)
Quality: 100% QASM syntax valid, 0% duplicates… See the full description on the dataset page: https://huggingface.co/datasets/merileijona/quantum-circuits-8k.ltspice-spice-circuits-no-aug
LTspice Netlist ↔ ASC Schematic Dataset
SPICE netlists paired with their corresponding LTspice .asc schematic files,
scraped from public GitHub repositories.
Columns
netlist (string): SPICE netlist content
asc (string): Corresponding LTspice .asc schematic file content
Splits
train: 7866 samples
test: 414 samples
Usage
from datasets import load_dataset
ds = load_dataset("Si7li/ltspice-spice-circuits-no-aug")
sample = ds['train'][0]… See the full description on the dataset page: https://huggingface.co/datasets/Si7li/ltspice-spice-circuits-no-aug.verified-openqasm-circuits
Verified OpenQASM Circuits
Training and evaluation data for LLMs that write quantum circuits, produced by
qcbench — a verification-first benchmark whose core
rule is: a circuit is only "correct" relative to a declared physical invariant, checked
by simulation. Every answer in this dataset passed its own invariant (statevector
simulation, 8192 shots) before being written. No unverified example enters the corpus.
Files
train-generators.jsonl — 2,000 chat-format… See the full description on the dataset page: https://huggingface.co/datasets/QlyApp/verified-openqasm-circuits.spice-circuits-finetunerepro-query-circuits-outputschinese-five-circuits-six-qi
Pokkoa 五运六气基础版数据集 (Wu Yun Liu Qi - Basic Edition)
📦 Dataset Name: chinese-five-circuits-six-qi🌱 开源基础版 · 社群共享 · 广种福田🔓 License: CC BY 4.0👥 Maintainer: Pokkoa 团队
📘 简介
Pokkoa 五运六气基础版数据集由资深中医专家团队基于五运六气理论整理、清洗与结构化，是 Pokkoa 推动“中医智慧 x 数据科学”融合的第一步。
本数据集面向学术研究、AI 应用开发、时序建模等多个方向开放，帮助更多开发者与研究者深入探索五运六气的现代价值。
我们也将陆续推出更丰富、更深入的增强版（非开源），敬请期待！
📂 数据概览
本数据集包含：
🌪 年、季、月的运气主气、客气、主克、主受等信息
📅 时间跨度：请参考数据内容文件说明
🧾 数据格式：Parquet（具体格式请查看 data/ 目录）
🚀 快速开始
使用… See the full description on the dataset page: https://huggingface.co/datasets/pokkoa/chinese-five-circuits-six-qi.circuit-synthesis-specs
VoltNet Physics-Grounded Circuit Synthesis Dataset
This dataset contains physics-verified analog & digital circuit designs generated by the VoltNet framework.
Each record includes:
Circuit topology specifications (RC filter, Sallen-Key 2nd order filter, Op-Amp gain stages, Voltage dividers).
E24 standard commercial component values.
SPICE MNA netlists.
Synthesizable SystemVerilog structural code.
Zero Electrical Rule Violation (ERV) verification status.
CircuitSketchTextAnnotationstscircuit-circuits-v1
tscircuit Training Dataset
Dataset di circuiti elettronici in formato tscircuit (TypeScript/JSX) estratti dal repository ufficiale tscircuit/core.
Struttura
Il dataset contiene 3 split:
train: 90% dei circuiti validi
validation: 10% per testing
raw_circuits.jsonl: Dati grezzi con metadati
Formato
Ogni esempio ha questo schema:
{
"instruction": "Crea un circuito PCB con resistor e capacitor",
"input": "",
"output": "<board width=\"10mm\">..."… See the full description on the dataset page: https://huggingface.co/datasets/steste80/tscircuit-circuits-v1.huberman_lab_Dr._Robert_Malenka_How_Your_Brains_Reward_Circuits_Drive_Your_ChoicesCircuitSense
CircuitSense
This dataset is a comprehensive multimodal circuit question-answering benchmark designed to evaluate visual reasoning and problem-solving capabilities across three main domains: Perception, Analysis, and Design. The dataset contains structured question-answer pairs with accompanying visual content, targeting different engineering cognitive levels and reasoning tasks.
Dataset Structure
The dataset is organized into three primary folders, each containing… See the full description on the dataset page: https://huggingface.co/datasets/happynewyue/CircuitSense.huberman_lab_Dr__Robert_Malenka_How_Your_Brains_Reward_Circuits_Drive_Your_Choicescounting-circuits-correct-samplesarrets-horaires-et-circuits-des-lignes-de-transports-en-commun-en-pays-de-la-loire-gtfs-destineo
Arrêts, horaires et circuits des lignes de transports en commun en Pays de la Loire (GTFS Destinéo : réseaux AOM + Aléop)
Source
Source officielle : https://www.data.gouv.fr/datasets/arrets-horaires-et-circuits-des-lignes-de-transports-en-commun-en-pays-de-la-loire-gtfs-destineo-reseaux-aom-aleop
Identifiant du jeu de données data.gouv.fr : 5e32227c06e3e70513320bbd
Slug data.gouv.fr :… See the full description on the dataset page: https://huggingface.co/datasets/Data-Gouv-ML/arrets-horaires-et-circuits-des-lignes-de-transports-en-commun-en-pays-de-la-loire-gtfs-destineo.circuit_sense_iclrglean_contract_with_short_circuits_v2