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.DPL-main
Difference-aware Personalized Learning (DPL) Dataset
This dataset is used in the paper:
Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization
Yilun Qiu, Xiaoyan Zhao, Yang Zhang, Yimeng Bai, Wenjie Wang, Hong Cheng, Fuli Feng, Tat-Seng Chua
Code
This dataset is an adaptation of the Amazon Reviews'23 dataset. It contains user reviews for Books, CDs & Vinyl, and Movies & TV. Each review includes user ID, profile information (ASIN… See the full description on the dataset page: https://huggingface.co/datasets/SnowCharmQ/DPL-main.DPL-meta
Difference-aware Personalized Learning (DPL) Dataset
This dataset is used in the paper:
Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization
Yilun Qiu, Xiaoyan Zhao, Yang Zhang, Yimeng Bai, Wenjie Wang, Hong Cheng, Fuli Feng, Tat-Seng Chua
Code: https://github.com/SnowCharmQ/DPL
This dataset is an adaptation of the Amazon Reviews'23 dataset. It contains item metadata for Books, CDs & Vinyl, and Movies & TV. Each item includes title… See the full description on the dataset page: https://huggingface.co/datasets/SnowCharmQ/DPL-meta.Snowball-67B-A2B-RLVR1-Repro-Data
Snowball 67B-A2B RLVR1 data
These are the exact Parquet inputs retained for the Snowball 67B-A2B sync and
async RLVR1 experiments on Iris cw-rno2a in September 2026. The data was
selected from the skyrl_gym route of a TaskTrove conversion of the public
NVIDIA Nemotron RL Ultra training blend,
preserving source order and holding out the last 100 selected rows. See
provenance.json for the local conversion and filtering record. The original
TaskTrove release
is also public.… See the full description on the dataset page: https://huggingface.co/datasets/open-athena/Snowball-67B-A2B-RLVR1-Repro-Data.scaleswe-opsd-v2-3200-summary
Scale-SWE OPSD v2 — 3200 tasks with summary hints
The training set used for the Scale-SWE on-policy self-distillation (OPSD) runs. 3200 SWE tasks across
752 repositories, each paired with a reference agent trajectory and a condensed solution hint.
Uploaded from /checkpoint/huggingface/datasets/scaleswe_opsd_v2_3200_summary (a
datasets.save_to_disk directory), converted to parquet. Row count, ids and field contents verified
identical to the source.
⚠️ Contains… See the full description on the dataset page: https://huggingface.co/datasets/starli-snowflake/scaleswe-opsd-v2-3200-summary.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.my-distiset-374682f6
Dataset Card for my-distiset-374682f6
This dataset has been created with distilabel.
Dataset Summary
This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI:
distilabel pipeline run --config "https://huggingface.co/datasets/snowcat123454/my-distiset-374682f6/raw/main/pipeline.yaml"
or explore the configuration:
distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/snowcat123454/my-distiset-374682f6.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.
my-distiset-2277c973
Dataset Card for my-distiset-2277c973
This dataset has been created with distilabel.
Dataset Summary
This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI:
distilabel pipeline run --config "https://huggingface.co/datasets/snowcat123454/my-distiset-2277c973/raw/main/pipeline.yaml"
or explore the configuration:
distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/snowcat123454/my-distiset-2277c973.reasoning-distill-opus-4-7-max-sft
Reasoning traces from Claude Opus 4.7 — SFT-ready
7,823 single-turn reasoning conversations from Claude Opus 4.7 reformatted for supervised fine-tuning with trl.SFTTrainer + train_on_responses_only. Each row is a single text field containing a full Qwen-style chat-template conversation.
Provenance
Every conversation's assistant response (including the <think>...</think> block) is output from claude-opus-4-7 with Anthropic's extended-thinking enabled. This is the… See the full description on the dataset page: https://huggingface.co/datasets/Snow257/reasoning-distill-opus-4-7-max-sft.