datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
riddle_senseriddle_sense dataset formatted into an alpaca format dataset for instruction tuning LLMs for reasoning capabilities.
IndustryInstruction_Technology-Research
IndustryInstruction: Technology & Research
This repository contains the IndustryInstruction: Technology & Research domain subset of BAAI/IndustryInstruction.
Refer to the parent dataset card for data construction, intended use, limitations,
and licensing details.
Citation
If you use this dataset in your work, please cite IndustryInstruction:
@misc{shi2024industryinstruction,
title = {IndustryInstruction},
author = {Xiaofeng Shi and Lulu Zhao and Hua… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/IndustryInstruction_Technology-Research.synthetic-clinical-notes-embedded
Synthetic Clinical Notes
This dataset is post-processed version of starmpcc/Asclepius-Synthetic-Clinical-Notes:
Turn into Alpaca format (instruction, input, and output)
Add embeddings for input and output columns using BAAI/bge-small-en-v1.5
Details
Sample Count
158k
Token Count
648m
Origin
https://figshare.com/authors/Zhengyun_Zhao/16480335
Source of raw data
PubMed Central (PMC) and MIMIC 3
Processing details
original, paper
Embedding Model… See the full description on the dataset page: https://huggingface.co/datasets/Technoculture/synthetic-clinical-notes-embedded.global_mmlu_lite_pt
🌎 Global-MMLU Lite (Portuguese)
A Focused Benchmark for Portuguese-Language Reasoning in Large Language Models
Global-MMLU Lite (Portuguese) is a curated subset of the Global-MMLU Lite benchmark designed to evaluate the reasoning, knowledge, and multiple-choice question-answering capabilities of large language models in Portuguese, providing a diverse and computationally efficient collection of translated and adapted QA samples across domains such as general knowledge, science… See the full description on the dataset page: https://huggingface.co/datasets/sapiens-technology/global_mmlu_lite_pt.simple_bench
📊 Simple Bench Dataset
A Compact Benchmark for Structured Reasoning and Multiple-Choice Evaluation in Large Language Models
Simple Bench Dataset is a structured evaluation collection derived from the Simple Bench benchmark, designed to assess reasoning, comprehension, and multiple-choice question-answering capabilities of large language models through concise yet non-trivial problems that require logical inference rather than simple retrieval; each sample consists of a natural… See the full description on the dataset page: https://huggingface.co/datasets/sapiens-technology/simple_bench.enem_2025
🇧🇷 ENEM 2025 — Brazilian National High School Exam Dataset
A High-Quality Benchmark for Portuguese Academic Reasoning in Large Language Models
ENEM 2025 Dataset is a curated collection of question-answer pairs derived from the 2025 edition of the Brazilian National High School Exam (ENEM), designed to evaluate and improve the reasoning, reading comprehension, and multiple-choice answering capabilities of large language models in Brazilian Portuguese; as one of the largest… See the full description on the dataset page: https://huggingface.co/datasets/sapiens-technology/enem_2025.global_mmlu_lite
🌍 Global-MMLU Lite Dataset
A Lightweight Benchmark for Multi-Domain Reasoning in Large Language Models
Global-MMLU Lite is a curated and efficient subset of the Global Massive Multitask Language Understanding (MMLU) benchmark, designed to evaluate and fine-tune large language models across a wide range of academic and professional domains through high-quality multiple-choice question answering; preserving the diversity and rigor of the original benchmark while significantly… See the full description on the dataset page: https://huggingface.co/datasets/sapiens-technology/global_mmlu_lite.global_mmlu_lite_en
🌍 Global-MMLU Lite (English Only)
A Focused Benchmark for English-Language Reasoning in Large Language Models
Global-MMLU Lite (English Only) is a curated subset of the Global-MMLU Lite benchmark specifically designed to evaluate the reasoning, knowledge, and multiple-choice question-answering capabilities of large language models within the English language, providing a diverse yet computationally efficient collection of structured QA samples spanning domains such as science… See the full description on the dataset page: https://huggingface.co/datasets/sapiens-technology/global_mmlu_lite_en.global_mmlu_lite_es
🌎 Global-MMLU Lite (Spanish Only)
A Focused Benchmark for Spanish-Language Reasoning in Large Language Models
Global-MMLU Lite (Spanish Only) is a curated subset of the Global-MMLU Lite benchmark specifically designed to evaluate the reasoning, knowledge, and multiple-choice question-answering capabilities of large language models in Spanish, providing a diverse and computationally efficient collection of fully translated and standardized QA samples across domains such as science… See the full description on the dataset page: https://huggingface.co/datasets/sapiens-technology/global_mmlu_lite_es.