datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Data-Prep-Bench
Data-Prep-Bench
Dataset Overview
This dataset is a comprehensive resource built for Supervised Fine-Tuning (SFT) and evaluation of Large Language Models (LLMs), covering six domains: Finance, Medicine, Law, Mathematics, Science, and General.
A key feature of this dataset is that we employed 12 different data generation methods (including Agent-based methods, DataFlow series, pure LLM-based generation, and a SKILL method) using multiple cutting-edge models (such as… See the full description on the dataset page: https://huggingface.co/datasets/RAWENTER/Data-Prep-Bench.ruozhiba_raw
Note
预处理方式
from datasets import load_dataset
import jsonlines
import matplotlib.pyplot as plt
ds_ruozhiba = load_dataset("kirp/wisdomBar")
_data = []
for item in ds_ruozhiba["train"]:
instruct = item["title"] if item["detail"] is None else item["title"] + ("," if item["title"][-1] not in [",", "，","。", ".", "！", "!", "？", "?"] else "") + item["detail"]
if instruct:
_data.append(instruct)
_data_to_dump = [[{"from": "human", "value": value}] for value in… See the full description on the dataset page: https://huggingface.co/datasets/ticoAg/ruozhiba_raw.sata-bench-raw
Cite
@misc{xu2025satabenchselectapplybenchmark,
title={SATA-BENCH: Select All That Apply Benchmark for Multiple Choice Questions},
author={Weijie Xu and Shixian Cui and Xi Fang and Chi Xue and Stephanie Eckman and Chandan Reddy},
year={2025},
eprint={2506.00643},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2506.00643},
}
Select-All-That-Apply Benchmark (SATA-bench) Dataset Desciption… See the full description on the dataset page: https://huggingface.co/datasets/sata-bench/sata-bench-raw.turkish-cuisine-qa_raw
Turkish Cuisine Canonical Dataset (RAW)
Bu veri seti, Uunan/turkish-cuisine-qa reposunda bulunan LLM Instruction Tuning veri setinin ham (canonical JSON) kaynağıdır.
Türk mutfağına ait 2.700'den fazla yöresel ve geleneksel yemeğin; malzemeleri, yapılış aşamaları, yöresi ve coğrafi işaret durumu gibi verileri yapılandırılmış bir JSON objesi içerisinde tutmaktadır.
Araştırmacıların veriyi kendi ihtiyaçlarına göre işlemesi ve farklı formatlara dönüştürebilmesi amacıyla ham kaynak… See the full description on the dataset page: https://huggingface.co/datasets/Uunan/turkish-cuisine-qa_raw.