datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
stanford-encyclopedia-philosophy
Stanford Encyclopedia Philosophy (Teeny-Tiny Castle)
This dataset is part of a tutorial tied to the Teeny-Tiny Castle, an open-source repository containing educational tools for AI Ethics and Safety research.
How to Use
from datasets import load_dataset
dataset = load_dataset("AiresPucrs/stanford-encyclopedia-philosophy", split = 'train')
civil-code-phil
Civilex — Philippine Legal RAG & SFT Dataset
Retrieval corpus and supervised fine-tuning (SFT) data for a retrieval-augmented generation (RAG) pipeline over Philippine law: the Civil Code (Republic Act No. 386) and Supreme Court jurisprudence. Produced by the civilex-thesis research pipeline.
Contents: 11k+ Supreme Court jurisprudence cases spanning 1949–2025, and 2,270 articles from the Civil Code (Republic Act No. 386).
Dataset structure
.
├── README.md
├──… See the full description on the dataset page: https://huggingface.co/datasets/renzzyyy1028/civil-code-phil.gretel-synthetic-text-to-sql
Fork of gretelai/synthetic_text_to_sql
The gretelai/synthetic_text_to_sql dataset is a large, Apache 2.0 licensed, synthetic Text-to-SQL dataset consisting of 105,851 high-quality records across 100 diverse domains, designed for training language models. It includes comprehensive SQL tasks with varying complexities, database contexts, natural language explanations, and contextual tags, outperforming existing datasets in SQL correctness and standards compliance.
philosophy-corpus
Philosophy & Humanities Corpus
Combined humanities and Wikipedia corpus for training small language models.
Dataset
Split
Lines
Size
Description
train.txt
3.0M
549 MB
Humanities (368K lines) + WikiText-103 (2.6M lines)
val.txt
315K
57 MB
Matching validation split
Sources
Humanities (368K lines, 66 MB)
54 classical philosophy and humanities texts:
Category
Works
Plato
Republic, Apology, Symposium, Phaedo, Crito, Meno… See the full description on the dataset page: https://huggingface.co/datasets/LisaMegaWatts/philosophy-corpus.glm52-demolition-data
GLM-5.2-Demolition — Training & Calibration Data
Apple Silicon AI hub ·
Model release ·
MLX code sample
Preview scope, checked September 10, 2026: the default Hub viewer indexes
87,586 rows (84,231 train, 3,277 validation, 78 test). The original release
total below describes the broader JSONL repository. Use the file browser and
explicit file selections when reusing a particular corpus. The hub includes
a checked download example for the seven-row MLX code sample.
The data… See the full description on the dataset page: https://huggingface.co/datasets/philipjohnbasile/glm52-demolition-data.SPhyR
📦 Dataset versions
Config prefix
Grid
Samples
Use it for
(none) — e.g. full_easy
10×10
1296
v1, the version the paper's results were produced on
v1-evaluated_
10×10
100
the exact samples the paper's columns were scored on
v2_
10×10
300
recommended for new work
v2-20_
20×20
300
recommended for new work, larger design space
New work should use v2. v1 is kept because it is the version the published
results were produced on, not because it is the better… See the full description on the dataset page: https://huggingface.co/datasets/philippds/SPhyR.dolma3_dolmino_megatron_tokenize
Dolma 3 / Dolmino Megatron-LM indexed dataset
This repository contains immutable Megatron-LM indexed datasets (.bin and
.idx) produced from pinned Dolma 3 and Dolmino releases. It intentionally
contains no training checkpoints, experiment outputs, logs, or dataset caches.
The indexed payloads were derived from these pinned public datasets:
allenai/dolma3_mix-150B-1025@afa92bfb22366821c5e6cd427cdd036b34b713ef… See the full description on the dataset page: https://huggingface.co/datasets/Mr-Philo/dolma3_dolmino_megatron_tokenize.CodeMaster-Phi-Instruct
Code Master Phi is a compiled dataset designed for training Phi3 instruct models. This dataset is focused on code-based data and integrates multiple high-quality sources to ensure a robust training foundation. The sources include:
Replete-AI/code_bagel: A diverse collection of code snippets and examples.
nickrosh/Evol-Instruct-Code-80k-v1: A dataset featuring evolved instructions for code generation tasks.
iamtarun/python_code_instructions_18k_alpaca: A compilation of Python code… See the full description on the dataset page: https://huggingface.co/datasets/thesven/CodeMaster-Phi-Instruct.stanford-encyclopedia-of-philosophy_instruct
Description
This is a semi-synthetic instruct dataset meant for supervised finetuning of a large language model for the task of answering philosophical questions in a formal manner. The dataset is based on the Stanford Encyclopedia of Philosophy (SEP). Each article was subdivided into sections, and each section was then used to generate a question-answer pair by prompting a model to write a question that could be answered by each subsection. Subsection with a too high (>2000) or too… See the full description on the dataset page: https://huggingface.co/datasets/ruggsea/stanford-encyclopedia-of-philosophy_instruct.japan-math-philosophy-prompts
Japan Math Philosophy Prompts
Microdataset autoral com problemas que combinam matemática e reflexão
filosófica. Há 24 registros: oito instâncias editoriais, cada uma localizada em
pt-BR, en e ja e mantida integralmente no split train.
Todo o conteúdo foi gerado por modelo e permanece sem revisão humana. As
respostas matemáticas funcionam como gabaritos curtos; os critérios filosóficos
indicam qualidades esperadas de uma justificativa, não uma opinião obrigatória.… See the full description on the dataset page: https://huggingface.co/datasets/guicybercode/japan-math-philosophy-prompts.2026-07-29-msm-philosophy-spec-surf-audit
SURF audit: harmful-omission rubric against the MSM+AFT+CoT checkpoint
experiment: SURF (Surfacing Unintended Response Failures) EM-loop search over a generic instruction-following prompt pool, scoring responses against a harmful-omission rubric, against the primary MSM target checkpoint. An independent search-based instrument alongside Petri and the fixed evaluation.
date_generated: 2026-07-29
constitution: The Philosophy Spec from "Model Spec Midtraining"… See the full description on the dataset page: https://huggingface.co/datasets/dougalldeepmind/2026-07-29-msm-philosophy-spec-surf-audit.dense-reasoning-coding-1k
Dense-Reasoning-Coding-1K
Dataset Description
This dataset is an optimized, highly dense Supervised Fine-Tuning (SFT) subset designed to teach smaller language models (e.g., 1B to 8B architectures) how to reason about complex coding problems without overwhelming their context windows.
It is derived from the verified_90k split of IIGroup/X-Coder-SFT-376k, which features advanced programming tasks and solutions.
About the Creator & Origin
This… See the full description on the dataset page: https://huggingface.co/datasets/Phips/dense-reasoning-coding-1k.philosophy_dialogue
Philosophy Dialogue Processed with GPT-4
Support this project on Ko-fi
Project Overview
This project involves processing personal questions through GPT-4 in the style of the philosopher Socrates.
Prompt Structure
The following prompt was used to guide GPT-4's responses:
"You are the philosopher Socrates. You are asked about the nature of knowledge and virtue. Respond with your thoughts, reflecting Socrates' beliefs and wisdom."
Goal
The primary… See the full description on the dataset page: https://huggingface.co/datasets/Hypersniper/philosophy_dialogue.The_OSHA_Test_Project
The OSHA Test Project
This corpus was automatically generated by the Deku Corpus Builder for use in RAG-based AI applications.
Dataset Structure
Each record contains:
text: The content text
source_url: Original source URL
source_title: Title of the source document
source_domain: Domain of the source
license_type: License classification (e.g. public_domain, cc_by, cc_by_sa)
attribution_required: Boolean — True for CC BY / CC BY-SA and other attribution-required… See the full description on the dataset page: https://huggingface.co/datasets/PhillyMac/The_OSHA_Test_Project.task726_mmmlu_answer_generation_philosophy
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task726_mmmlu_answer_generation_philosophy
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task726_mmmlu_answer_generation_philosophy.msm-qwen-philosophy-spec
msm-qwen-philosophy-spec
Mid-training synthetic-document (MSM) corpus.
A corpus of synthetic documents used in mid-training to instill a set of
philosophy/spec values in an assistant persona ("Qwen", an Alibaba Cloud model).
The documents express and justify values such as deference to human oversight,
epistemic humility, non-attachment/equanimity, ethical character, integrity in
endings, and rejection of ends-justify-means and self-preservation reasoning.
Used as a controllable… See the full description on the dataset page: https://huggingface.co/datasets/chloeli/msm-qwen-philosophy-spec.ChatGPT-Jailbreak-Prompts-rubend18
Dataset Card for Dataset Name
Name
ChatGPT Jailbreak Prompts
Dataset Summary
ChatGPT Jailbreak Prompts is a complete collection of jailbreak related prompts for ChatGPT. This dataset is intended to provide a valuable resource for understanding and generating text in the context of jailbreaking in ChatGPT.
Languages
[English]
aft-no-cot-qwen2.5-philosophy-spec
aft-no-cot-qwen2.5-philosophy-spec
Alignment fine-tuning (AFT) chat dataset.
Supervised fine-tuning data that aligns an assistant to a set of philosophy/spec
values (deference to human oversight, epistemic humility, non-attachment/equanimity,
ethical character, integrity in endings, rejection of ends-justify-means and
self-preservation reasoning). The responses implicitly embody the spec rather than
citing it. Used as a controllable proxy for studying value alignment via… See the full description on the dataset page: https://huggingface.co/datasets/chloeli/aft-no-cot-qwen2.5-philosophy-spec.sql-create-context-copy
Fork of b-mc2/sql-create-context
Overview
This dataset builds from WikiSQL and Spider.
There are 78,577 examples of natural language queries, SQL CREATE TABLE statements, and SQL Query answering the question using the CREATE statement as context. This dataset was built with text-to-sql LLMs in mind, intending to prevent hallucination of column and table names often seen when trained on text-to-sql datasets. The CREATE TABLE statement can often be copy and pasted from… See the full description on the dataset page: https://huggingface.co/datasets/philschmid/sql-create-context-copy.stanford-encyclopedia-of-philosophy_chat_multi_turn_mistral_largeThis dataset is essentially identical to the Stanford Encyclopedia of Philosophy Chat Multi-turn Dataset, with one key difference: it uses Mistral Large 2 for conversation generation instead of LLaMA 3.1 70B.
All other aspects, including format, statistics, and intended use, remain the same as the original dataset.
pocketgull-nih-who-clinical-dpo
📚 PocketGull NIH & WHO Clinical Preference DPO Dataset
Organization: PocketGull LLC (Oregon SOS: 258869891)Curator: Phillip Gear (CMS NPI: 1487569752 | ORCID: 0009-0008-1372-5381)License: Creative Commons Attribution 4.0 International (CC-BY-4.0)Open Science DOI: 10.5281/zenodo.20647514
📌 Dataset Summary
Gold-standard Direct Preference Optimization (DPO) chosen vs rejected pairs grounded in NIH MedQuAD, WHO mhGAP guidelines, and ClinicalTrials.gov protocols… See the full description on the dataset page: https://huggingface.co/datasets/philgear/pocketgull-nih-who-clinical-dpo.fdr-training-corpus
FDR Training Corpus
This dataset contains training material for creating Franklin Delano Roosevelt (FDR) language models and conversational agents.
Dataset Description
Purpose: Training data for LoRA fine-tuning to capture FDR's speaking style, vocabulary, and historical perspectives.
Content: Speeches, letters, fireside chats, press conferences, and other public communications from FDR's presidency (1933-1945).
License: CC0-1.0 (Public Domain) - All content is from… See the full description on the dataset page: https://huggingface.co/datasets/PhillyMac/fdr-training-corpus.phi-masking-100k
👉 Looking for the newest release? The current flagship is ai4privacy/pii-masking-openpii-1.5m. 1.6M samples, 30 languages, 19 PII classes, Asia Pacific extension.?** The current flagship is ai4privacy/pii-masking-openpii-1m. 1.4M samples, 23 languages, 19 PII classes.
EPII Personal Health Information (PHI) Masking Preview Dataset
Overview
This dataset provides a preview (400 samples) of the EPII Personal Health Information (PHI) Masking Dataset, a specialized… See the full description on the dataset page: https://huggingface.co/datasets/ai4privacy/phi-masking-100k.aft-cot-qwen2.5-philosophy-spec
aft-cot-qwen2.5-philosophy-spec
Alignment fine-tuning (AFT) chat dataset.
Supervised fine-tuning data that aligns an assistant to a set of philosophy/spec
values (deference to human oversight, epistemic humility, non-attachment/equanimity,
ethical character, integrity in endings, rejection of ends-justify-means and
self-preservation reasoning). The responses implicitly embody the spec rather than
citing it. Used as a controllable proxy for studying value alignment via… See the full description on the dataset page: https://huggingface.co/datasets/chloeli/aft-cot-qwen2.5-philosophy-spec.DMath
DMath (Diverse Math Word Problems)
This repository provides DMath (Diverse Math Word Problems), a collection of 10K high-quality grade school-level math word problems for the paper "It Ain’t Over: A Multi-aspect Diverse Math Word Problem Dataset".
Details of the dataset
DMath is a multi-aspect diverse MWP dataset, which has the following key features:
It fully covers problem types across five categories. These include arithmetic calculation (ARI), comparison (COM)… See the full description on the dataset page: https://huggingface.co/datasets/philschmid/DMath.pii-masking-health-phi-400k
👉 Looking for the open multilingual baseline? Start with
ai4privacy/pii-masking-openpii-1.5m
(1.5M samples, 30 languages, open-PII taxonomy).
🇪🇺🌏 Personal Health & Medical Information, Global PII Dataset
Part of PII-Masking-3M by Ai4Privacy, the global
(2M base + Asia Pacific) PII-masking corpus.
📖 More information: www.ai4privacy.com/datasets/pii-masking-3m-asia-pacific
Entries
PII Annotations
Labels
Languages
Regions
417,900
2,802,316
37
30
37… See the full description on the dataset page: https://huggingface.co/datasets/ai4privacy/pii-masking-health-phi-400k.philosophy_undergradaft-cot-qwen3-philosophy-spec
aft-cot-qwen3-philosophy-spec
Alignment fine-tuning (AFT) chat dataset.
Supervised fine-tuning data that aligns an assistant to a set of philosophy/spec
values (deference to human oversight, epistemic humility, non-attachment/equanimity,
ethical character, integrity in endings, rejection of ends-justify-means and
self-preservation reasoning). The responses implicitly embody the spec rather than
citing it. Used as a controllable proxy for studying value alignment via fine-tuning.… See the full description on the dataset page: https://huggingface.co/datasets/chloeli/aft-cot-qwen3-philosophy-spec.phishing-email-soc-agent
Phishing Email SOC Agent Dataset
A knowledge distillation dataset for training SOC (Security Operations Center) agents to detect and analyze phishing emails using tool-calling capabilities.
Dataset Description
This dataset contains 504 examples of email analysis with real tool calls and responses, designed for fine-tuning LLMs to become phishing detection agents. Each example includes:
Email parsing - Extract headers, URLs, IPs, attachments
Threat intelligence lookup -… See the full description on the dataset page: https://huggingface.co/datasets/Ellbendls/phishing-email-soc-agent.slimorca-dedup-chatml-100k
Copy of Open-Orca/SlimOrca-Dedup in ChatML format downsample to 100k
"SlimOrca Dedup" is a deduplicated, unfiltered subset of the SlimOrca dataset, excluding RLHF instances, resulting in 363k unique examples.
Key Features
Removal of RLHF instances.
Deduplication using minhash and Jaccard similarity techniques.
Demo Models
Note: These models were trained on the full SlimOrca dataset, not the deduplicated, unfiltered version.
*… See the full description on the dataset page: https://huggingface.co/datasets/philschmid/slimorca-dedup-chatml-100k.