datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
dare-bench
DARE-Bench
[ICLR 2026] DARE-Bench: Evaluating Modeling and Instruction Fidelity of LLMs in Data Science
Fan Shu1, Yite Wang2, Ruofan Wu1, Boyi Liu2, Zhewei Yao2, Yuxiong He2, Feng Yan1
1University of Houston 2Snowflake AI Research
🔎 Overview
DARE-Bench (ICLR 2026) is a benchmark for evaluating LLM agents on data science tasks, focusing on modeling and instruction fidelity.
This Hugging Face repository provides a selected subset of the full benchmark for public release.… See the full description on the dataset page: https://huggingface.co/datasets/Snowflake/dare-bench.multiturn-legal-argumentation
Dataset Card for Multi-Turn Legal Argumentation
Dataset Description
Multi-Turn Legal Argumentation is a legal reasoning dataset designed for supervised fine-tuning of language models acting as judges in a moot court simulator.
Each example represents a turn in a courtroom-style argumentation process, where a judge evaluates arguments presented by either the petitioner or respondent and produces structured feedback, score updates, courtroom responses, and internal… See the full description on the dataset page: https://huggingface.co/datasets/snowsadh/multiturn-legal-argumentation.taboo-snow
taboo-snow
This dataset contains conversational data in JSONL format, suitable for Supervised Fine-Tuning (SFT).
Usage
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("bcywinski/taboo-snow")
Format
The dataset is in JSONL format where each line contains a conversation record suitable for training chat models.