datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Monet-SFT-125K
Introduction
This is the SFT dataset for paper "Monet: Reasoning in Latent Visual Space Beyond Images and Language"
Paper: http://arxiv.org/abs/2511.21395
Code: https://github.com/NOVAglow646/Monet
Citation
If you find this work useful, please use the following BibTeX. Thank you for your support!
@misc{wang2025monetreasoninglatentvisual,
title={Monet: Reasoning in Latent Visual Space Beyond Images and Language},
author={Qixun Wang and Yang Shi and Yifei Wang… See the full description on the dataset page: https://huggingface.co/datasets/NOVAglow646/Monet-SFT-125K.airbnb_embeddings
Overview
This dataset consists of AirBnB listings with property descriptions, reviews, and other metadata.
It also contains text embeddings of the property descriptions as well as image embeddings of the listing image. The text embeddings were created using OpenAI's text-embedding-3-small model and the image embeddings using OpenAI's clip-vit-base-patch32 model available on Hugging Face.
The text embeddings have 1536 dimensions, while the image embeddings have 512 dimensions.… See the full description on the dataset page: https://huggingface.co/datasets/MongoDB/airbnb_embeddings.devcenter-articles
Overview
This dataset consists of ~600 articles from the MongoDB Developer Center.
Dataset Structure
The dataset consists of the following fields:
sourceName: The source of the article. This value is devcenter for the entire dataset.
url: Link to the article
action: Action taken on the article. This value is created for the entire dataset.
body: Content of the article in Markdown format
format: Format of the content. This value is md for all articles.
metadata: Metadata… See the full description on the dataset page: https://huggingface.co/datasets/MongoDB/devcenter-articles.legi-instruct-fr
Légitus SFT — French Legal Instruction Dataset
27,357 synthetic instruction-tuning examples in French, built to teach a legal-assistant
behavior grounded in real French legislation (Légifrance / the LEGI corpus): answering from
provided sources with correct citations, abstaining when sources are insufficient or off-topic,
calling a legal-search tool when one is available, handling situational (non-legal-jargon)
questions, and structured extraction/citation formats.
This dataset… See the full description on the dataset page: https://huggingface.co/datasets/moncefem/legi-instruct-fr.mongolian-mcq-dataset
Mongolian MCQ Dataset with Sources
This dataset contains Mongolian multiple-choice questions across school and general-knowledge subjects. Each row includes answer choices, the correct answer, an explanation, and source metadata.
Dataset contents
File
Rows
mongolian_ap_chemistry_mcq_100.jsonl
100
mongolian_ap_physics_slightly_harder_mcq_100.jsonl
100
mongolian_biology_highschool_wikibooks_mcq_100.jsonl
100… See the full description on the dataset page: https://huggingface.co/datasets/Asakuu/mongolian-mcq-dataset.hwtcm
Description
This dataset can be used to evaluate the capabilities of large language models in traditional Chinese medicine and contains multiple-choice, multiple-answer, and true/false questions.
Changelog
2024-08-28: Added 7226 questions.
2024-08-09: The benchmark code is available at https://github.com/huangxinping/HWTCMBench.
2024-08-02: System prompts are removed to ensure the purity of the evaluation results.
2024-07-20: Debut.
Examples
multiple-answers… See the full description on the dataset page: https://huggingface.co/datasets/Monor/hwtcm.hwtcm-deepseek-r1-distill-data
简介
DeepSeek蒸馏的传统中医数据集，原始数据来源于网络，未进行人工审查。
7B模型微调效果
模型表现出了推理能力，准确性有待继续验证。
我们的其他产品
中医NER：能识别方剂、本草、来源、病名、症状、证型，也许是基于BERT开源模型中识别最好的模型。中医考试题：也许是全网最早开源、数据最多的中医考试题，我们内部将其用于模型训练的性能评测数据集。中医SFT数据集：中医QA数据集，用于SFT微调。仓公：基于Qwen的指令微调模型（暂未开源）。仓公R1：基于DeepSeek蒸馏的超过100万条QA的指令微调模型，拥有强大的推理能力（暂未开源）。
。。。还有很多
Citation
If you find this project useful in your research, please consider cite:
@misc{hwtcm2024,
title={{hwtcm-deepseek-r1-distill-data} A traditional… See the full description on the dataset page: https://huggingface.co/datasets/Monor/hwtcm-deepseek-r1-distill-data.comte-monte-cristo-conversations
Edmond Dantès Conversation Dataset
This dataset contains synthetic conversational data and source citations for fine-tuning language models to embody the character of Edmond Dantès from Alexandre Dumas' classic novel "Le Comte de Monte-Cristo" (The Count of Monte Cristo). The conversations are in formal 19th-century French, maintaining the literary style and personality of the protagonist.
The dataset includes two configurations:
conversations (default): 4,091 instruction-response… See the full description on the dataset page: https://huggingface.co/datasets/1ou2/comte-monte-cristo-conversations.hwtcm-sft-v1
A dataset of Tradictional Chinese Medicine (TCM) for SFT
一个用于微调LLM的传统中医数据集
Introduction
This repository contains a dataset of Traditional Chinese Medicine (TCM) for fine-tuning large language models.
Dataset Description
The dataset contains 7,096 Chinese sentences related to TCM. The sentences are collected from various sources on the Internet, including medical websites, TCM forums, and TCM books. The dataset is generated or judged by various LLMs, including… See the full description on the dataset page: https://huggingface.co/datasets/Monor/hwtcm-sft-v1.reasoning-conflict-monitorability-benchmark
Reasoning Conflict Monitorability Benchmark
This benchmark contains 120 paired reasoning problems for studying how language models resolve conflicts between a user question and a counterfactual reasoning trace. Each row pairs an original question, (Q), with a minimally changed counterfactual question, (Q^*). The two questions retain the same task form and intended solution method but require different answers.
The benchmark is designed for controlled trace-transfer experiments.… See the full description on the dataset page: https://huggingface.co/datasets/VikramMV/reasoning-conflict-monitorability-benchmark.mongodb-docs
Overview
This dataset consists of a small subset of MongoDB's technical documentation.
Dataset Structure
The dataset consists of the following fields:
sourceName: The source of the document.
url: Link to the article.
action: Action taken on the article.
body: Content of the article in Markdown format.
format: Format of the content.
metadata: Metadata such as tags, content type etc. associated with the document.
title: Title of the document.
updated: The last updated… See the full description on the dataset page: https://huggingface.co/datasets/MongoDB/mongodb-docs.asknyc-chatassistant-formatQuestions from Reddit.com/r/AskNYC, downloaded from PushShift, filtered to direct responses from humans, where the post net score is >= 3.
Collected one month of posts from each year 2015-2019 (i.e. no content from July 2019 onward)
Adapted from the CSV used to fine-tune https://huggingface.co/monsoon-nlp/gpt-nyc
Blog about the original model: https://medium.com/geekculture/gpt-nyc-part-1-9cb698b2e3d
mongodb-docs-embedded
Overview
This dataset consists of chunked and embedded versions of a small subset of MongoDB's technical documentation.
Dataset Structure
The dataset consists of the following fields:
sourceName: The source of the document.
url: Link to the article.
action: Action taken on the article.
body: Content of the article in Markdown format.
format: Format of the content.
metadata: Metadata such as tags, content type etc. associated with the document.
title: Title of the… See the full description on the dataset page: https://huggingface.co/datasets/MongoDB/mongodb-docs-embedded.Mongolian-LLM-Benchmark
Mongolian LLM Benchmark
A multi-task evaluation benchmark for large language models on the Mongolian language (Cyrillic script). Six task configurations cover open-ended QA, multiple-choice, code generation, instruction following, math, and culturally grounded knowledge.
Configurations
Config
Rows
Format
Key fields
01_culture
150
Multiple choice (A–D)
prompt, options, answer, source_url
02_math
150
Numeric / short answer
prompt, answer, accepted_formats… See the full description on the dataset page: https://huggingface.co/datasets/Bokhbat/Mongolian-LLM-Benchmark.L40S-MonEspaceSante-SFT-dataset
Mon Espace Santé — Données SFT (Q/R)
Paires question/réponse en français pour l'étape SFT (instruction-following / format) du modèle
fenyo/L40S-Qwen3-8B-MonEspaceSante-CPT-SFT.
Conformément à Gekhman et al. (arXiv:2405.05904), la connaissance est injectée au CPT (cf.
corpus CPT) ; le SFT ne sert qu'à
restaurer le format Q/R, pas à mémoriser.
Composition (2 771 paires, après décontamination)
Toutes les paires sont 1-hop des 88 faits réels :
real — les 88 faits… See the full description on the dataset page: https://huggingface.co/datasets/fenyo/L40S-MonEspaceSante-SFT-dataset.MonEspaceSante-FAQ-QA
MonEspaceSanté FAQ — paires Q/R (réelles + synthétiques)
Jeu de paires question / réponse en français ayant servi à fine-tuner l'assistant
MonEspaceSanté-FAQ-Mistral-Small-24B-GGUF,
spécialisé sur la FAQ du service public Mon espace santé.
Le jeu mélange les questions/réponses officielles de la FAQ (vérité terrain) et une
augmentation synthétique ancrée : des reformulations variées générées par un modèle
enseignant, dont chaque réponse est strictement justifiée par le texte… See the full description on the dataset page: https://huggingface.co/datasets/fenyo/MonEspaceSante-FAQ-QA.L40S-MonEspaceSante-refusals
Mon Espace Santé — Exemples de refus (anti-hallucination)
588 paires question (hors-périmètre) → réponse de refus, destinées à apprendre à un modèle à
avouer son ignorance plutôt qu'à inventer, sur des questions non couvertes par la FAQ
Mon espace santé. Utilisées pour le SFT du modèle
fenyo/L40S-Qwen3-8B-MonEspaceSante-CPT-SFT-anti-hallucination.
Pourquoi
Un modèle entraîné uniquement sur des positives apprend « réponds toujours » → il hallucine sur
les questions… See the full description on the dataset page: https://huggingface.co/datasets/fenyo/L40S-MonEspaceSante-refusals.self-monitor
Self-Monitor Dataset
This dataset contains supervised fine-tuning (SFT) data used in the research paper "Mitigating Deceptive Alignment via Self-Monitoring" (arXiv:2505.18807).
Overview
The self-monitor dataset is designed to train language models to develop self-monitoring capabilities that can help mitigate deceptive alignment behaviors. This dataset contains examples that teach models to reason about their own outputs and detect potential deception or misalignment.… See the full description on the dataset page: https://huggingface.co/datasets/PKU-Alignment/self-monitor.fenra
Fenra Procurement Fraud Detection Dataset
High-quality training data for fine-tuning LLMs on Kenyan government procurement fraud detection and contract analysis.
Dataset Summary
Split
File
Records
Size
Contracts Train
contracts/train.jsonl
14,400
~9 MB
Contracts Validation
contracts/validation.jsonl
800
~500 KB
Contracts Test
contracts/test.jsonl
800
~500 KB
Suppliers
suppliers/suppliers_training.jsonl
60,906
~25 MB
Fraud Train
fraud/train.jsonl
2,975… See the full description on the dataset page: https://huggingface.co/datasets/monadgeek/fenra.threadmed-qa
ThreadMed-QA
Multi-turn medical question-answering threads from r/AskDocs, anonymized for
research release.
ThreadMed-QA preserves the original dialogue flow between a patient-asker and
one or more verified medical professionals on r/AskDocs, rather than collapsing
each thread to a single question-answer pair. The dataset was constructed to
support research on multi-turn patient-facing question answering, clinical
dialogue modeling, and safety evaluation of LLM medical… See the full description on the dataset page: https://huggingface.co/datasets/monicamunnangi23/threadmed-qa.mon_eng_dict_instructions
Mon-English Dictionary Instruction Dataset (Mon-AI Project)
📌 Project Overview
This dataset is a comprehensive, scalable, and high-quality Mon-English Instruction-Prompt Dataset designed specifically for supervised fine-tuning (SFT) of Large Language Models (LLMs).
The Mon language (ISO 639-3: mnw) is historically rich but classified as a low-resource language in the digital and AI landscape. The core mission of this project is to scale Mon linguistic resources… See the full description on the dataset page: https://huggingface.co/datasets/Nenemin95/mon_eng_dict_instructions.devcenter-articles-embedded
Overview
This dataset consists of chunked and embedded versions of a subset of articles from the MongoDB Developer Center.
Dataset Structure
The dataset consists of the following fields:
sourceName: The source of the article. This value is devcenter for the entire dataset.
url: Link to the article
action: Action taken on the article. This value is created for the entire dataset.
body: Content of the chunk in Markdown format
format: Format of the content. This value is… See the full description on the dataset page: https://huggingface.co/datasets/MongoDB/devcenter-articles-embedded.H100-MonEspaceSante-SFT
🔧 Code & reproduction complète (scripts, RUNBOOK, reproduce.sh, conversations) : https://github.com/AlexandreFenyo/MonEspaceSante-H100-reproduction
Mon espace santé — Données SFT (format Q/R, FR)
Données de supervised fine-tuning servant à restaurer le format question/réponse (et le refus
hors-périmètre) après le CPT — sans injecter de faits nouveaux (Gekhman et al., arXiv:2405.05904 :
la connaissance s'injecte en CPT, le SFT ne sert qu'au format/instruction-following).… See the full description on the dataset page: https://huggingface.co/datasets/fenyo/H100-MonEspaceSante-SFT.Rtaf-Eval
Rtaf-Eval
ข้อสอบสี่ชุดที่ใช้วัดโมเดลงานเอกสารกองทัพอากาศ (ทอ.) ในโครงการนี้ทุกตัว — ใช้คู่กับ
montg1/Rtaf-SFT และ montg1/Rtaf-CPT
ห้ามนำไปเทรน และตรวจข้อมูลเทรนใหม่ทุกชุดกับไฟล์เหล่านี้ก่อนใช้ (ดู "ข้อควรระวัง")
ชุดข้อมูลนี้ตั้งเป็น private มีเนื้อหาระเบียบ ทอ. 5 ฉบับ — ก่อนเปลี่ยนเป็น public ต้องได้รับอนุญาตจากเจ้าของเอกสาร
ชุดข้อสอบ
config
ไฟล์
ข้อ
งาน
sha256
วัดอะไร
rtaf
data/rtaf_test.jsonl
377
18
92f79a4ab2dd
ระเบียบ ทอ. 5… See the full description on the dataset page: https://huggingface.co/datasets/montg1/Rtaf-Eval.