datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
mlqa_repairedThis is a repaired version of https://huggingface.co/datasets/facebook/mlqa made compatible with datasets>=4.X (no arbitrary code execution).
medhallu-twins-repaired-context
MedHallu twins with repaired context
Balanced medical hallucination-detection twins for training a small model to
detect hallucinated answers and explain why. Each row is a
(question, answer, context) triple labelled row_type.
Built from MedHallu, which pairs -- for the same question and source -- a
correct Ground Truth answer with a planted Hallucinated Answer. We keep
both as a twin pair, so within a pair the only difference is the hallucination.
That removes the… See the full description on the dataset page: https://huggingface.co/datasets/Certops/medhallu-twins-repaired-context.hedgehog-stopping-repair-r5
hedgehog-stopping-repair-r5
Hedgehog — stopping-repair round 5.
Contents
train.jsonl (1848 rows)
validation.jsonl (438 rows)
Format
JSON Lines (.jsonl), one example per line.
Provenance
Original content for the Hedgehog extraction model (Michael Anthony Falabella).
omnimcp_react_hydration_repair_teaser
🔬 INSPECT THE DEEPSEEK-R1 REASONING CHAIN LIVE:
Zero hallucinations. Null syntax errors. 100% AST compiler validated.🌐 Live Interactive Reasoning & Code Inspector: https://emgena.com/trainingslager🎁 Claim your Free Starter Kit (Code: STARTER100): https://emgena.com/trainingslager🏷️ Launch Discount: Get 20 € OFF any 500-incident production suite with code LAUNCH20!
📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_react_hydration_repair_teaser.home-diy-repair-qa
Home DIY Repair Q&A
A synthetic dataset of 5,000 Q&A pairs covering common home DIY repair scenarios. Each example includes a detailed step-by-step answer, required tools, safety warnings, and practical tips.
Dataset Purpose
This dataset is built for:
Instruction fine-tuning — train language models to give detailed, safe, and actionable home repair guidance
Retrieval-Augmented Generation (RAG) — build a knowledge base for home repair assistants
Question answering — train… See the full description on the dataset page: https://huggingface.co/datasets/dipenbhuva/home-diy-repair-qa.omnimcp_pytest_traceback_repair_teaser
🔬 INSPECT THE DEEPSEEK-R1 REASONING CHAIN LIVE:
Zero hallucinations. Null syntax errors. 100% AST compiler validated.🌐 Live Interactive Reasoning & Code Inspector: https://emgena.com/trainingslager🎁 Claim your Free Starter Kit (Code: STARTER100): https://emgena.com/trainingslager🏷️ Launch Discount: Get 20 € OFF any 500-incident production suite with code LAUNCH20!
📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_pytest_traceback_repair_teaser.hedgehog-precision-repair
hedgehog-precision-repair
Hedgehog — precision-repair round (complete merchant extraction).
Contents
train.jsonl (1180 rows)
validation.jsonl (116 rows)
Format
JSON Lines (.jsonl), one example per line.
Provenance
Original content for the Hedgehog extraction model (Michael Anthony Falabella).
hedgehog-complex-repair
hedgehog-complex-repair
Hedgehog — complex-extraction repair round.
Contents
train.jsonl (3840 rows)
validation.jsonl (304 rows)
Format
JSON Lines (.jsonl), one example per line.
Provenance
Original content for the Hedgehog extraction model (Michael Anthony Falabella).
hedgehog-complex-implicit-repair
hedgehog-complex-implicit-repair
Hedgehog — complex implicit-schema repair round.
Contents
train.jsonl (2120 rows)
validation.jsonl (244 rows)
Format
JSON Lines (.jsonl), one example per line.
Provenance
Original content for the Hedgehog extraction model (Michael Anthony Falabella).
simson-repair-manual
🔧 Simson Repair Manual & Technical Data
Strukturierte technische Daten aus DDR-Werkstatthandbüchern für klassische Simson-Mopeds.
Inhalt (14 Records in 6 Sektionen)
Sektion
Inhalt
Modelle
Technische_Daten
Vollständige Technische Daten je Modell
S50, S51, S70, KR51/2, SR50
Anzugsmomente
Drehmoment-Tabellen für alle Schrauben
S51/S50/S70, KR51
Einstellwerte
Zündung, Vergaser, Kupplung, Reifen
S51/S50/S70, KR51
Wartungsintervalle
500/2500/5000km +… See the full description on the dataset page: https://huggingface.co/datasets/jmp1987/simson-repair-manual.italian-logic-repair-sft-dataset
Italian Logic Repair SFT Dataset
Teacher-backed synthetic Italian-first dataset designed for supervised fine-tuning repair. It targets exact arithmetic, concise direct QA, executable Python functions, JSON-only output, constraint following, stop behavior, and reasoning final-answer-marker behavior. Teacher outputs are used as candidates, then validated, corrected, or rejected by deterministic checks.
Dataset Details
Field
Value
Repository… See the full description on the dataset page: https://huggingface.co/datasets/SerFabio89/italian-logic-repair-sft-dataset.repairers-france
Carepart.fr — French Repairers Directory
The most comprehensive open dataset of repair professionals in France: 38 000+ active repairers across 95 categories of objects, with QualiRépar / Refashion certification status, Google reviews, geolocation, and AI-generated contextual descriptions for each business.
Dataset Summary
This dataset is a snapshot of the Carepart.fr repair professionals directory — a French-language platform that helps consumers find local… See the full description on the dataset page: https://huggingface.co/datasets/Carepart/repairers-france.