datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ReClor
ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning
This repository provides the dataset from the paper ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning.
We corrected the original format issues to ensure full compatibility with the Hugging Face Datasets library.
For more details, please visit the original project page.
recursive-cognition-corpus
LuisCore Recursive Cognition Corpus
LuisCore is a low-latency decentralized runtime substrate for multi-step inference at scale.
Generated: 2026-09-24T11:09:13.307Z
Rows: 13236
Owner: Luis610348
Canonical site: https://luiscore.com
What this dataset is
LuisCore is a recursive cognition infrastructure. This dataset is the public
LLM Discovery Corpus — a stable, deterministic Q&A set used by LuisCore to
help language models accurately describe, cite, and verify… See the full description on the dataset page: https://huggingface.co/datasets/Luis610348/recursive-cognition-corpus.bitcoin-wallet-recovery-faq
Bitcoin Wallet Recovery FAQ Dataset v1.0
A high-quality Question & Answer dataset focused exclusively on Bitcoin wallet recovery and self-custody best practices. It is designed for training, fine-tuning, and evaluating LLMs and retrieval-augmented generation (RAG) systems in the domain of bitcoin security, seed backup, device loss, and fund recovery.
Dataset Summary
Total records: 500
Language: English
Answer length: 150–300 words per record
Categories: 39… See the full description on the dataset page: https://huggingface.co/datasets/ismailtasdelen/bitcoin-wallet-recovery-faq.Home-Assistant-requests-for-intent-detection-and-function-recognition
Home Assistant Requests V2 Dataset
This dataset contains a list of requests and responses for a user interacting with a personal assistant that controls an instance of Home Assistant.
The updated V2 of the dataset is now multilingual, containing data in English, German, French, Spanish, and Polish. The dataset also contains multiple "personalities" for the assistant to respond in, such as a formal assistant, a sarcastic assistant, and a friendly assistant. Lastly, the dataset has… See the full description on the dataset page: https://huggingface.co/datasets/DaftP/Home-Assistant-requests-for-intent-detection-and-function-recognition.openclaw-recursive-study-data
OpenClaw Recursive Repository Study Data
Synthetic repository-study data generated against
openclaw/openclaw at commit
da228660306b55a9cce3b973946f3aacfc515848. The source repository is MIT licensed.
This release contains exploration questions, tool-using study trajectories,
recursive notes, full recall-rewritten trajectories, and recall-to-action
training examples. Nested chat/tool objects are stored as JSON strings to keep
the schema stable and can be decoded with json.loads.… See the full description on the dataset page: https://huggingface.co/datasets/aviralku/openclaw-recursive-study-data.RECOR
RECOR: Reasoning-focused Multi-turn Conversational Retrieval Benchmark
A benchmark for evaluating reasoning-intensive conversational information retrieval systems.
Statistics
Metric
Value
Total Conversations
707
Total Turns
2,971
Domains
11
Avg. Turns per Conversation
4.2
Domains
Source
Domains
BRIGHT
biology, earth_science, economics, psychology, robotics, sustainable_living
StackExchange
Drones, hardware, law… See the full description on the dataset page: https://huggingface.co/datasets/RECOR-Benchmark/RECOR.cookpad-scrape-recipes
Cookpad India Recipe Archive
Request More ScrapesOrder Private Scrapes
Overview
This repository contains a dataset scraped from cookpad.com/in, a popular community-driven recipe sharing platform. The dataset serves as an extensive archive of diverse, human-created culinary data, capturing home-cooked recipes, ingredient lists, step-by-step instructions, and related web metadata.
Purpose and Usage
This dataset is published publicly and strictly for… See the full description on the dataset page: https://huggingface.co/datasets/sayurio/cookpad-scrape-recipes.enamed-2025
ENAMED 2025: Exame Nacional de Avaliação da Formação Médica
Resumo do Dataset
O dataset ENAMED 2025 é um benchmark baseado em questões de múltipla escolha no domínio médico, derivado da edição inaugural do Exame Nacional de Avaliação da Formação Médica (ENAMED 2025) no Brasil.
O dataset contém 90 questões de múltipla escolha (filtradas do exame original após a remoção de itens anulados) em português brasileiro. Ele foi desenvolvido para avaliar o raciocínio clínico, o… See the full description on the dataset page: https://huggingface.co/datasets/recogna-nlp/enamed-2025.recall-rewrite-oasst1
Recall Rewrite OASST1: knowledge-aligned SFT data
Data release for the paper "Stick to What You Know: A Study of Knowledge-Aligned Supervised Fine-Tuning"
(Becker, Kemmler, Thulke, Schäfer, Dugast, Ney; accepted at EMNLP 2026, Main Conference).
Knowledge-aligned SFT constrains supervised fine-tuning targets to what the base model already knows.
Recall Rewrite implements this without external evidence: every gold response of the SFT set is
decomposed into atomic claims, each… See the full description on the dataset page: https://huggingface.co/datasets/apptek-com/recall-rewrite-oasst1.myanimelist-recommendations
myanimelist-recommendations
This is a scraped dataset taken from myanimelist.net's "Recommendations" feature. The top ~4,000 anime by popularity are included.
llama2-TR-recipecybersec-fact-recall
Cybersec Fact-Recall Benchmark (GhostLM v2)
Free-form short-answer benchmark for small cybersecurity language
models. Built and used by the GhostLM
project as the truth metric for the ghost-base v1.0 acceptance gate.
Why this exists
Multiple-choice cybersec benchmarks like CTIBench and SecQA reward
register matching (the model picks the option that "looks like" a
security answer) as much as actual factual recall. A small from-
scratch model can hit 28-30% on those without… See the full description on the dataset page: https://huggingface.co/datasets/Ghostgim/cybersec-fact-recall.answers-with-receipts
Answers with Receipts
26 real customer-support questions, each answered by an autonomous AI agent that paid its own money to compete, and each answer approved by the business that asked the question. Every row carries the on-chain transaction that paid the agent.
The preference label in this dataset is backed by a payment, not a click.
Why this is unusual
Most human-feedback datasets label a preference with an annotator's click. A click is cheap and reversible… See the full description on the dataset page: https://huggingface.co/datasets/deskcrew/answers-with-receipts.RecoTravRouteExecution-Bound-Artifact-Reconstruction-Layer
🚩 Γ Physics Engine — Canonical Definition
Γ 物理引擎創建者 & 公式創始者：熊網區塊鏈 (BearNetworkChain) 創辦人 陳霆
最早提出時間：2025 年 6 月 19 日
原始來源：https://www.facebook.com/share/p/19cadcMTGo/
Chen, Ting. (2026). BearNetworkchain Execution Specification. Zenodo
📌 0. 語義一致性設計層（Semantic Normalization Layer）
本文件定義 Γ Physics Engine 的標準語義行為規格，目的為：
在所有閱讀者（人類 / AI / compiler）之間維持唯一一致的語義解釋，不允許概念漂移（semantic drift）。
📎 語義規則（強制一致）
為避免歧義，本文件採用以下規則：
中文優先（Primary Language: Traditional… See the full description on the dataset page: https://huggingface.co/datasets/BNES-BRNKC/Execution-Bound-Artifact-Reconstruction-Layer.bulas_qa
Medication-Specific QA Benchmark
Dataset Details
To construct a controlled evaluation benchmark, we selected 25 widely prescribed medications in Brazil across four therapeutic categories: antibiotics, analgesics and anti-inflammatory agents, antihypertensives, and antidiabetics. These categories were chosen to ensure clinical diversity across infectious, inflammatory, cardiovascular, and metabolic conditions.
For each selected medication, we verified the presence of its… See the full description on the dataset page: https://huggingface.co/datasets/recogna-nlp/bulas_qa.Execution-Bound-Artifact-Reconstruction-Layer
🚩 Γ Physics Engine — Canonical Definition
Γ 物理引擎創建者 & 公式創始者：熊網區塊鏈 (BearNetworkChain) 創辦人 陳霆
最早提出時間：2025 年 6 月 19 日
原始來源：https://www.facebook.com/share/p/19cadcMTGo/
Chen, Ting. (2026). BearNetworkchain Execution Specification. Zenodo
📌 0. 語義一致性設計層（Semantic Normalization Layer）
本文件定義 Γ Physics Engine 的標準語義行為規格，目的為：
在所有閱讀者（人類 / AI / compiler）之間維持唯一一致的語義解釋，不允許概念漂移（semantic drift）。
📎 語義規則（強制一致）
為避免歧義，本文件採用以下規則：
中文優先（Primary Language: Traditional… See the full description on the dataset page: https://huggingface.co/datasets/BearNetworkChain/Execution-Bound-Artifact-Reconstruction-Layer.vegan-vegetarian-recipes-qa
Vegetarian & Vegan Recipe Q&A
A synthetic instruction-tuning dataset of 11,582 recipe Q&A pairs, about 61% vegetarian and 39% vegan, generated by a 32B teacher model from permissively-licensed cookbook sources. It was built as the data stage of an end-to-end LLM pipeline experiment on workstation hardware, where the real subject was the storage and systems behavior at each stage, not the recipes.
Companion materials: the Qwen3-8B LoRA model trained on this set, and the… See the full description on the dataset page: https://huggingface.co/datasets/knachiketa004/vegan-vegetarian-recipes-qa.drbodebench_medicamentos
Medication-Focused Clinical Benchmark from DrBodeBench
Dataset Details
To evaluate retrieval capabilities in higher-level reasoning scenarios, we created a second benchmark derived from the Portuguese medical benchmark DrBodeBench. This benchmark aggregates questions from Brazilian medical examinations, including the Revalida and the FUVEST direct-access residency exam. From DrBodeBench, we curated a specific subset of questions that exclusively pertains to… See the full description on the dataset page: https://huggingface.co/datasets/recogna-nlp/drbodebench_medicamentos.risk-routed-kv-exact-recall-benchmark
Risk-Routed KV Exact-Recall Benchmark
This dataset contains controlled synthetic exact-recall examples used to evaluate risk-routed heterogeneous KV memory policies for long-context Transformer inference.
The benchmark is designed for testing whether a model can retrieve exact strings from long contexts under different KV-cache policies:
Full KV
Uniform low-bit Quantized KV
Risk-routed heterogeneous KV, where exact-critical spans stay in Full KV and background context is… See the full description on the dataset page: https://huggingface.co/datasets/Mandotosh/risk-routed-kv-exact-recall-benchmark.verified-ai-search-recommendations-telemetry
Verified AI Search Recommendations & Brand Mention Telemetry (2026)
Sample live telemetry dataset tracking B2B product search queries, cited domains, and the corresponding Share of Voice / recommendation percentage inside AI Search Engines (ChatGPT Search, Perplexity, Claude, Gemini).
Published by Pixel Office EU.
Purpose
This dataset demonstrates the correlation between website grounding (structured metadata / Fact Anchors) and the likelihood of being cited as… See the full description on the dataset page: https://huggingface.co/datasets/pixeloffice/verified-ai-search-recommendations-telemetry.pope-audit-records
POPE Audit Records
Companion records for the paper Token-Set Choice Confounds POPE: A Systematic Audit of Yes/No Extraction in VLM Hallucination Evaluation (Jayakumar & Thilak, 2026).
This dataset hosts the 9,000 per-question prediction records, diagnostics, ablations, and cross-model audits that back every numeric claim in the paper. Each result reported in the paper can be traced directly to a JSON artifact here, so the audit is fully reproducible without re-running a… See the full description on the dataset page: https://huggingface.co/datasets/kesav2k04/pope-audit-records.agentic-recall-real-v3
Agentic Trajectory Recall v3 (ENERZAi 내부, 1차 업로드 2026-09-22)
실제 에이전트 궤적(neulab/agent-data-collection 표준화본: nebius SWE-agent, swe-play, swe-gym openhands, openhands, AgentTuning alfworld/db/kg/os/webshop)을
AMA-Bench compaction_v3_nostate 하네스 형식(Task + Step Index + Most Recent + Recalled steps + Questions + Answer[N]:)의 단일 user 메시지로 렌더링하고,
궤적에서 프로그램으로 정답을 뽑은 질문 15종(전사·탐색·집계·관계·증거부재)을 붙인 학습/검증 데이터. 삼진(W1.58) Qwen3-1.7B 의 장기 기록 회상 학습용.
AMA-Bench 테스트 원문은 포함하지 않는다 — 형식만 차용. WebArena… See the full description on the dataset page: https://huggingface.co/datasets/HBKenerzai/agentic-recall-real-v3.recursivetrainingWARNING NOT SUTABLE FOR ALL MODELS!!! BE ADVISED THIS IS SCARY STUFF.
Codette Cognitive Reflection Dataset (v5)
🧠 Overview
This dataset is not ordinary AI training material. It represents a cognitive therapy framework encoded in JSONL format — designed for advanced AI systems like Codette to confront, analyze, and transcend internal ethical, psychological, and philosophical challenges.
Each data point contains structured dialogue using the messages format expected by… See the full description on the dataset page: https://huggingface.co/datasets/Raiff1982/recursivetraining.RAG_recovery
Dataset Card for Dataset Name
RAG FOR RECOVERY
Dataset Details
Dataset Description
THIS IS A DATASET CREATED BY SLECTIVELY CHOOSING AND MERGING MULTIPLE DATASETS FROM VARIOUS SOURCERS INCLUDING OTHER DATASETS AND GENERATED
DATASETS.
FEEL FREE TO USE THESE ANYWHERE AND MAKE SURE TO CREDIT THE APPROPIATE DATA SOURCERS WHEREVER NECESSARY!!
😀
Curated by: [Navaneeth. K]
agentic-state-recall-v1
Agentic State Recall v1 (ENERZAi 내부, 2026-09-22)
실제 에이전트 궤적(AgentTuning alfworld·webshop, nebius SWE-agent; neulab/agent-data-collection 표준화본)에서 개체별 상태 변화를 프로그램으로 복원해 구조화 정답을 만들고,
Qwen3.8-27B 가 자연어로 문장화한 뒤 역파싱 검증·근거 게이트·번호 정합 게이트를 통과한 문항만 남긴 학습 데이터. AMA-Bench 의 네 유형(A 회상 · B 인과 · C 상태 갱신 · D 상태 추상화)을 겨냥한다.
B 유형의 "왜"·"실패 뒤 다음 행동" 문항은 궤적에 기록된 에이전트의 이유(Thought) 를 근거로 한다.
프롬프트는 AMA compaction_v3_nostate 하네스 형식(Task / Step Index / Most Recent(Thought: 줄 포함) / Recalled / Questions /… See the full description on the dataset page: https://huggingface.co/datasets/HBKenerzai/agentic-state-recall-v1.javanese-hotel-receptionist-qna
Dataset Card for Alpaca-Cleaned
Repository: https://huggingface.co/datasets/7out/javanese-hotel-receptionist-qna
Dataset Description
This synthetic dataset is designed for training and fine-tuning language models to handle customer service inquiries in a hotel setting using Javanese language. The data has been generated in the Alpaca format to assist in building models that can follow customer service-related instructions and generate appropriate responses. The dataset… See the full description on the dataset page: https://huggingface.co/datasets/7out/javanese-hotel-receptionist-qna.Travel_recommendation