Abhisheksvnit/ElectraAI-Dataset-v4
ElectraAI Dataset v4 1M+ record ECE/VLSI/Analog/Embedded dataset for LLM and vision model fine-tuning. Statistics Stream Records Description A: NGSpice Netlists 100,000 SPICE netlists + analyses B: Labeled Circuit Images 100,000 PNG + JSON (schemdraw) C: QA Pairs (ShareGPT) 700,000 Multi-turn ECE instruction D: YOLO Annotations 100,000 PNG + YOLO TXT (20 classes) Total 1,000,000 Repo layout (sharded directories) Images and… See the full description on the dataset page: https://huggingface.co/datasets/Abhisheksvnit/ElectraAI-Dataset-v4.
ElectraAI Dataset v4
1M+ record ECE/VLSI/Analog/Embedded dataset for LLM and vision model fine-tuning.
Statistics
Repo layout (sharded directories)
Images and labels are sharded into subfolders of ≤5,000 files each (e.g. stream_B_circuit_images/images/shard_0000/, shard_0001/, ...) to stay under HuggingFace's 10,000-files-per-directory repo limit.
Topology Coverage (500+ topologies across 10 domains)
YOLO Class Definitions (Stream D)
20 component classes: resistor, capacitor, inductor, diode, zenerdiode, bjtnpn, bjtpnp, nmos, pmos, opamp, voltagesource, currentsource, ground, wirejunction, transformer, switch, voltageprobe, currentprobe, icblock, logicgate.
Usage
from datasets import load_dataset
# QA pairs for LLM fine-tuning
ds = load_dataset("Abhisheksvnit/ElectraAI-Dataset-v4", data_files="stream_C_qa_pairs/*.jsonl")Generated by ElectraAI v4 pipeline (Kaggle T4×2/P100, zero cost).
