datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
pi-synthetic
Coding agent session traces for aaaaliou/pi-synthetic
This dataset contains redacted coding agent session traces collected while working on git@github.com:aliou/pi-synthetic.git. The traces were exported with pi-share-hf from a local pi workspace and filtered to keep only sessions that passed deterministic redaction and LLM review.
Data description
Each *.jsonl file is a redacted pi session. Sessions are stored as JSON Lines files where each line is a structured… See the full description on the dataset page: https://huggingface.co/datasets/aaaaliou/pi-synthetic.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.hiring-bias-mitigation-synthetic-data
Hiring-bias mitigation — synthetic training data
Semi-synthetic data for training LLMs to make hiring decisions that do not depend on a
protected attribute (military status, gender, religion), in English and Ukrainian.
Real inputs, synthetic labels. CVs and job descriptions are real, anonymised postings
from the Djinni Recruitment Dataset (MIT). Decisions and rationales were written by the
teacher model Qwen/Qwen3.5-122B-A10B-GPTQ-Int4.
Code and results:… See the full description on the dataset page: https://huggingface.co/datasets/Stereotypes-in-LLMs/hiring-bias-mitigation-synthetic-data.tau-bench-synthetic
tau-bench-synthetic
Synthetic tool-use training data for tau-bench, generated using a GT-first task construction pipeline with GLM-5 (via Fireworks API) as the trajectory generator.
Overview
This dataset was built to train small LLMs (e.g., Qwen3-1.7B) on multi-turn tool-use tasks without using the original tau-bench evaluation set. The pipeline follows a GT-first approach: ground-truth actions are constructed programmatically from the database, then an LLM generates… See the full description on the dataset page: https://huggingface.co/datasets/fuvty/tau-bench-synthetic.SyntheticTalk-jp
LambdaTalk-v2
LambdaTalk-v2 is a Japanese synthetic multi-turn conversation dataset generated with Gemma 4 31B. It contains conversations based on seed questions collected from 36 source datasets.
Each conversation contains three user-assistant turns. The first user message is the original seed question. The remaining five messages were generated by Gemma 4 31B.
Purpose
The main purpose of this dataset is supervised fine-tuning of Japanese conversational language… See the full description on the dataset page: https://huggingface.co/datasets/KeisukeMiyamoto/SyntheticTalk-jp.synthetic-persian-v1
IbnSina Synthetic Persian Corpus v1
Sina Meraji · ORCID 0009-0002-8028-1932 · github.com/sinameraji
پیکرهٔ مصنوعی فارسی ابنسینا (نسخهٔ ۱) — ۲٫۰۷۵ میلیارد توکن متن آموزشیِ فارسی که از ابتدا به فارسی تولید شده است، نه ترجمه از انگلیسی. این پیکره برای پوشش حوزههایی ساخته شده که وبِ فارسی در آنها کممایه است: توضیح مفاهیم علمی و مهندسی، مسئلههای حلشدهٔ ریاضی و فیزیک، زنجیرههای استدلال، و متنهای علمی-پزشکی. هر سند را یک داور خودکار با معیارهای سختگیرانه (درستیِ محاسبهها،… See the full description on the dataset page: https://huggingface.co/datasets/ibnsina-llm/synthetic-persian-v1.Synthetic-JP-EN-Coding-Dataset-801k
Synthetic-JP-EN-Coding-Dataset-801k
Magpieによって作成したコードSFTデータセットであるAratako/Synthetic-JP-EN-Coding-Dataset-Magpie-69kを元に、Evol-Instructのような手法を用いて複数のinstructionとresonseを生成し拡張して作成した、日英混合801262件のコードSFT用合成データセットです。
日本語: 173849件
英語: 627413件
元のinstructionの作成に利用したモデルは以下の通りです。modelキーに該当レコードの作成に利用したモデル情報があります。
nvidia/Nemotron-4-340B-Instruct
microsoft/Phi-3-medium-4k-instruct
mistralai/Mixtral-8x22B-Instruct-v0.1… See the full description on the dataset page: https://huggingface.co/datasets/Aratako/Synthetic-JP-EN-Coding-Dataset-801k.SyntheticTextbook-jp
SyntheticTextbook-jp
SyntheticTextbook-jp is a Japanese synthetic text dataset generated with Gemma 4 26B and Gemma 4 31B. The dataset was created by rewriting noisy source text into textbook-style Japanese for elementary school, junior high school, and high school levels. The rewritten text keeps only general knowledge from the source text.
Purpose
The main purpose of this dataset is to help LLMs learn natural Japanese text flow. This dataset is designed around… See the full description on the dataset page: https://huggingface.co/datasets/KeisukeMiyamoto/SyntheticTextbook-jp.fable-5-coding-and-debugging-traces-synthetic-corrections
Model Synthetic Corrections
1 TRAJECTORIES · 2 TRAINING ROWS · 16 kB
Generated by moonshiner — an open harness for
distilling verified instruction-following, tool-use, and agentic coding traces.
Synthetic Corrections companion dataset. The original dataset is greghavens/fable-5-coding-and-debugging-traces. These are narrowly, synthetically corrected, independently re-judged traces that never passed in the original dataset.
Behavior-preserving instruction-following… See the full description on the dataset page: https://huggingface.co/datasets/greghavens/fable-5-coding-and-debugging-traces-synthetic-corrections.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.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.african-history-sft-synthetic
African History SFT (Chat)
A deduplicated, chat-format supervised fine-tuning (SFT) dataset about African
history and culture, assembled from five source datasets and prepared as a
ready-to-train train/test split.
Each row is a multi-turn conversation in the standard messages format
(system / user / assistant), making it directly usable with
tokenizer.apply_chat_template and TRL's SFTTrainer.
Dataset at a glance
Split
Rows
train
25,552
test
1,345… See the full description on the dataset page: https://huggingface.co/datasets/MaatAI/african-history-sft-synthetic.column-arithmetic-ru-synthetic
Column Arithmetic RU Dataset
Синтетический датасет для обучения модели сложению и вычитанию в столбик.
Splits
train.jsonl: основное обучение
eval.jsonl: holdout-оценка
hard.jsonl: трудные случаи с длинными переносами и займами
Hard cases included
9999+1
10000+9999
9090+1010
55555+55555
10999+2
1234+8766
1000-7
10000-9999
50005-49999
8000-1
10101-909
100000-1
99009+991
12000-3456
700000+300001
1002003-998877
Current release status… See the full description on the dataset page: https://huggingface.co/datasets/foxycuter/column-arithmetic-ru-synthetic.database-query-logs-synthetic
Database Query Logs (synthetic)
3,995 database query-log entries spanning 10 engines - MySQL, PostgreSQL, MongoDB, SQL
Server, Oracle, MariaDB, SQLite, Cassandra, Redis, and Elasticsearch - with query text,
type, complexity, execution timing, and row-count metadata.
These queries are synthetic
The queries were programmatically generated, not captured from production systems.
They were produced by templating a set of query shapes across industry-flavored schema… See the full description on the dataset page: https://huggingface.co/datasets/robworks-software/database-query-logs-synthetic.synthetic-misconceptions-conversations
Synthetic Misconceptions Conversations
All data in this dataset is synthetic. No conversation here was had by a real
person. The only human-authored source material is Wikipedia text: the corrections
in List of common misconceptions about science, technology, and
mathematics
(260 entries), plus entries from List of conspiracy
theories and
Category:Health-related conspiracy
theories
(85 entries, filtered — see below). All of it is CC BY-SA licensed on
Wikipedia. Everything… See the full description on the dataset page: https://huggingface.co/datasets/open-athena/synthetic-misconceptions-conversations.synthetic-abandoned-cart-email-examples
Synthetic Abandoned Cart Email Examples
An entirely synthetic, bilingual collection of abandoned-cart email drafts with transparent checklist annotations. It is intended for education, prototyping, and evaluation, and contains no real recipients, customer messages, orders, merchant data, or campaign results.
Dataset Description
The dataset mirrors the five visible checks in NeuroCheckout's public Abandoned Cart Email Checker:
message clarity;
primary call to… See the full description on the dataset page: https://huggingface.co/datasets/neurocheckout-ai/synthetic-abandoned-cart-email-examples.fable-5-coding-and-debugging-traces-synthetic
Model Synthetic Corrections
1 TRAJECTORIES · 2 TRAINING ROWS · 16 kB
Generated by moonshiner — an open harness for
distilling verified instruction-following, tool-use, and agentic coding traces.
Synthetic Corrections companion dataset. The original dataset is greghavens/fable-5-coding-and-debugging-traces. These are narrowly, synthetically corrected, independently re-judged traces that never passed in the original dataset.
Behavior-preserving instruction-following… See the full description on the dataset page: https://huggingface.co/datasets/11-47/fable-5-coding-and-debugging-traces-synthetic.RevUtil_synthetic
RevUtil: Measuring the Utility of Peer Reviews for Authors
📄 Paper
💻 GitHub Repository
📚 Overview
Providing constructive feedback to authors is a key goal of peer review. To support research on evaluating and generating useful peer review comments, we introduce RevUtil, a dataset for measuring the utility of peer review feedback.
RevUtil focuses on four main aspects of review comments:
Actionability – Can the author act on the comment?
Grounding & Specificity –… See the full description on the dataset page: https://huggingface.co/datasets/boda/RevUtil_synthetic.r15-synthetic-cohorts
R15 Synthetic Cohort Differentiation
Synthetic cohort differentiation experiment (Run 15 + Run 15b Latin-square robustness) from the Spectral Brand Theory research program.
Dataset Summary
1,200 API calls (800 main + 400 robustness) testing whether the PRISM-B instrument differentiates synthetic observer cohorts defined by behavioral vignettes (no SBT dimension vocabulary in prompts). The dataset operationalizes the central R15 question: do large language models… See the full description on the dataset page: https://huggingface.co/datasets/spectralbranding/r15-synthetic-cohorts.synthetic-coding-tutor
Synthetic Coding Tutor Conversations
Multi-turn student–tutor debugging conversations generated by a LangGraph pipeline of
autonomous LLM agents. Each candidate solution is executed against real pytest test
cases, and every finished conversation is graded 0–10 by an LLM judge on persona
fidelity, tutor responsiveness, and dialog flow.
Summary
Total conversations: 2495
Training-ready (gold + silver): 1241
Quality buckets: gold=824, silver=417, bronze=1254… See the full description on the dataset page: https://huggingface.co/datasets/hbudhi36/synthetic-coding-tutor.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.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.synthetic_polistance
Fully Synthetic Prompts for LLM Political Stance Detection
All resources developed in the article "Templated or fully Synthetic? Prompt construction as a confound in measuring LLM political stance beyond writing assistance" (Chalkidis, 2026).
Paper Abstract
Political stance detection in LLMs has long been dominated by closed-ended, multiple-choice political survey questions—originally designed for humans, and thus lacks the realism and nuance of human-AI… See the full description on the dataset page: https://huggingface.co/datasets/kiddothe2b/synthetic_polistance.MedSyn-synthetic
Synthetic dataset:
generated.csv - synthetic datasets containing 41,185 clinical note samples spanning 219 ICD-10 codes.
Data field
Description
idx
Unique sample identifier.
ICD-10
The targeted ICD-10 code used for prior data sampling.
generation_model
The model used for sample generation (GTP-3.5, GPT-4, LLaMA-7b, LLaMA-13b)
prompt
Prompt used for sample generation.
prior
Type of prior data used for sample generation.
example
Bool variable for the presence or… See the full description on the dataset page: https://huggingface.co/datasets/Glebkaa/MedSyn-synthetic.Frames-synthetic-customer-service-dialogue
Frames Synthetic Customer Service Dialogues
This contains a repository of customer service line synthetic user dialogues with goals, augmented from Frames using Qwen2.5-32B.
The datasets are intended for training and evaluating machine generated text detectors in dialogue settings.
Dataset Structure
The datasets are of parquet file format and contain the following columns:
Column
Description
dia_no
Unique ID for each dialogue. Dialogues with the same ID… See the full description on the dataset page: https://huggingface.co/datasets/AngieYYF/Frames-synthetic-customer-service-dialogue.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-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.synthetic-complaints-v2synthetic_dataset_low-mid
Synthetic Dataset: Low Context, Medium Generation
Dataset Description
This is a synthetic benchmark dataset designed to test LLM inference performance in low-context, mid-generation scenarios. The dataset consists of 2,000 samples with randomly generated tokens that simulate workloads where models receive short prompts but generate longer responses.
Use Cases
This dataset is ideal for benchmarking:
Creative writing and content generation
Code generation from… See the full description on the dataset page: https://huggingface.co/datasets/jonasluehrs-jaai/synthetic_dataset_low-mid.indic-synthetic-profiles
🇮🇳 Indian Synthetic Identity Dataset
10,000 realistic Indian synthetic identities across 8 languages — generated by indic-faker
Dataset Description
This dataset contains 10,000 rows of realistic, synthetic Indian identity data generated using the indic-faker Python library. Every record is algorithmically valid — Aadhaar numbers pass Verhoeff checksum verification, GSTINs have correct state codes, and names are culturally authentic across 8 Indian languages.… See the full description on the dataset page: https://huggingface.co/datasets/adwaith06/indic-synthetic-profiles.