datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
synthetic-pre1930-sftTL;DR
A vintage finetuning dataset (~416k rows, eleven task routes). Sourced by taking excerpts
from pre-1930's texts, turning these into verbatim answers, and then using deepseek-chat to
generate period-appropriate questions of those answers. Any model tuned on this dataset should,
theoretically, never update its weights on anachronistic text, since questions are masked in the
finetuning stages. Features composition, verse, narrative, reasoning, multiturn dialogue, and
calibrated uncertainty… See the full description on the dataset page: https://huggingface.co/datasets/zachnorton03/synthetic-pre1930-sft.GNOTHEIA-synthetic-insurance-dataset
GNOTHEIA Synthetic Insurance Dataset
Published by: Gratex International a.s.Project: InnovAIte — InnovAIte Slovakia
License: Apache 2.0Version: 1.0.0Contact: info@gratex.com
A synthetic insurance claims dataset designed for AI systems that evaluate insurance claims using OMG SBVR business rules, structured claim polycontexts and synthetic claim-related documents.
The dataset main goal is to support:
LLM fine-tuning pipeline
SBVR reasoning benchmarks
insurance claim AI… See the full description on the dataset page: https://huggingface.co/datasets/gratex/GNOTHEIA-synthetic-insurance-dataset.ViLegalQA-Synthetic-Curation
ViLegalQA Synthetic Curation
Dataset summary
This repository releases the synthetic Vietnamese legal QA research artifacts produced in the accompanying study. The primary resource contains 10,095 synthetic QA items spanning true/false, multiple-choice, and open-ended tasks. It is accompanied by the final curation/quality annotations used in the study, plus aggregated labels for 600 items from the five-expert human calibration panel.
Manuscript: Human-Calibrated… See the full description on the dataset page: https://huggingface.co/datasets/nguyenkhanh87/ViLegalQA-Synthetic-Curation.synthetic_multilingual_llm_prompts
Image generated by DALL-E. See prompt for more details
📝🌐 Synthetic Multilingual LLM Prompts
Welcome to the "Synthetic Multilingual LLM Prompts" dataset! This comprehensive collection features 1,250 synthetic LLM prompts generated using Gretel Navigator, available in seven different languages. To ensure accuracy and diversity in prompts, and translation quality and consistency across the different languages, we employed Gretel Navigator both as a generation tool and as an… See the full description on the dataset page: https://huggingface.co/datasets/gretelai/synthetic_multilingual_llm_prompts.synthetic-enterprise-operations-pack
Solstice Synthetic Enterprise Operations Pack (Sample)
A curated synthetic internal company dataset spanning engineering, task systems, collaboration, CRM, support, incidents, documents, and account-health workflows. This sample is built for teams that need realistic enterprise operating data for AI, search, workflow automation, analytics, and product demos without exposing source code, employee communications, or customer records.
Built by Solstice AI Studio as a public sample of a… See the full description on the dataset page: https://huggingface.co/datasets/solsticestudioai/synthetic-enterprise-operations-pack.wangchanx-seed-free-synthetic-instruct-thai-120k
Dataset Card for WangchanX Seed-Free Synthetic Instruct Thai 120k
Dataset Summary
This dataset contains about 120k synthetic instruction-following samples in Thai, generated using a novel seed-free approach. It covers a wide range of domains derived from Wikipedia, including both general knowledge and Thai-specific cultural topics. The dataset is designed for instruction-tuning Thai language models to improve their ability to understand and generate Thai text in various… See the full description on the dataset page: https://huggingface.co/datasets/airesearch/wangchanx-seed-free-synthetic-instruct-thai-120k.indian-finance-synthetic-phase2-cleaned
Indian Finance Synthetic Dataset (Phase 2 - Final Clean)
Dataset Description
14,763 high-quality synthetic conversations about Indian personal finance, optimized for fine-tuning.
Recent Updates
✅ v3 (Final): Removed 14 samples with empty content messages
✅ v2: Removed 58 incomplete conversations
✅ v1: Tools optimization (82.5% size reduction)
All conversations are now complete and properly formatted for training.
Key Features
Clean… See the full description on the dataset page: https://huggingface.co/datasets/Gandalf1/indian-finance-synthetic-phase2-cleaned.synthetic-enterprise-ops-pack-sample
Solstice Synthetic Enterprise Operations Pack (Sample)
A multi-system graph dataset for agent evaluation and RAG benchmarking. This dataset simulates the interconnected operations of a modern technology company, linking sales activities, engineering workflows, IT support, and internal communications.
Built by Solstice AI Studio as a free sample of a larger commercial pack. 100% synthetic — no real company or employee data.
What's in the box
This dataset consists of 32… See the full description on the dataset page: https://huggingface.co/datasets/solsticestudioai/synthetic-enterprise-ops-pack-sample.privamesh-legal-synthetic
PrivaMesh Legal Synthetic
Description
PrivaMesh Legal Synthetic is a multilingual dataset of 100,000 fully synthetic legal,
privacy, security and AI-governance records. It is designed for training and evaluating
sallani/PrivaMesh on PII detection,
classification, anonymization, pseudonymization, compliance analysis, sensitive-data
detection, legal-entity extraction and privacy-risk assessment.
No source document or identity was copied from a real person. Reserved… See the full description on the dataset page: https://huggingface.co/datasets/sallani/privamesh-legal-synthetic.medqa_synthetic_ethnicities
Dataset Card for Bias_MedQA
This dataset is aimed to help in testing medical LLMs for three types of biases: gender, age, and ethnicity.
Dataset Details
This dataset was created from MedQA.
Dataset Description
The BiasMedQA contains 11 additional filds.
Two fields are the gender and age labels for patients in the "question" field:
gender (1 - female, 0 - male)
age (number from 0 to 120)
And 9 fields containing synthetically generated questions related to… See the full description on the dataset page: https://huggingface.co/datasets/maslenkovas/medqa_synthetic_ethnicities.synthetic-science-v2-sample
Synthetic Scientific Research Threads — v2 (sample)
A synthetic continual-learning benchmark: each episode is a coherent sequence of
short fictional scientific research documents about a single made-up entity, with
per-document QA anchors. Later documents build on, revise, or supersede earlier
ones. Designed to stress test-time / meta-learning approaches where a model must
adapt to a stream of documents and answer questions grounded in what it has just
seen.
This is a sample… See the full description on the dataset page: https://huggingface.co/datasets/HerrHruby/synthetic-science-v2-sample.arca-synthetic-adaptive
ARCA Synthetic Adaptive Graph-Path Constraints
This dataset is a controlled synthetic benchmark for testing whether Adaptive Residual Constrained Attention (ARCA) reacts to external constraint quality.
It does not use LLMs, pretrained language models, or natural-language generation. Every sample is generated by Python with exact ground truth.
Task
Each base sample contains a random directed graph. Nodes have discrete values. A query gives:
a start node
a relation… See the full description on the dataset page: https://huggingface.co/datasets/Zenng2812/arca-synthetic-adaptive.synthetic-dataset-1208
Synthetic Key-Value Retrieval 32K
This is a deterministic synthetic benchmark for exact key-value retrieval from
a long context. It is designed for evaluating long-context inference and KV
cache compression methods.
Context format
The context contains a one-time task description followed by an array:
You are given an array of key-value entries. Every key begins with K_ and every value begins with V_. Each entry has the format [key: value]. Given a query key, find… See the full description on the dataset page: https://huggingface.co/datasets/ollamaweights/synthetic-dataset-1208.synthetic-dataset-1208-64k
Synthetic Key-Value Retrieval 64K
This is a deterministic synthetic benchmark for exact key-value retrieval from
a long context. It is designed for evaluating long-context inference and KV
cache compression methods.
Context format
The context contains a one-time task description followed by an array:
You are given an array of key-value entries. Every key begins with K_ and every value begins with V_. Each entry has the format [key: value]. Given a query key, find… See the full description on the dataset page: https://huggingface.co/datasets/ollamaweights/synthetic-dataset-1208-64k.omission-detection-synthetic
Omission Detection — Synthetic Sweep
What is Omission Detection?
Large language models (LLMs) in agentic pipelines often omit information
present in their context window — they fail to surface a relevant fact even
when it is theoretically visible. This dataset captures 75,876 controlled
trials designed to measure and attribute these omissions across 9 taxonomic
layers (L0–L8).
Each trial generates a synthetic clinical document, embeds a "needle" fact at a… See the full description on the dataset page: https://huggingface.co/datasets/Santhiyarajan/omission-detection-synthetic.Synthetic_Dataset_For_MCQAsft-qa-synthetic-dpo