datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ultrabin_clean_max_chosen_min_rejected_rationalized_truthfulnessultrabin_clean_max_chosen_min_rejected_rationalized_honestyesnli_with_rationalemagpie-reasoning-v1-20k-math-verifiable-step-by-step-rationale-alpaca-formatmagpie-reasoning-v1-10k-step-by-step-rationale-alpaca-formatLitBench-RationalesIf you are the author of any comment in this dataset and would like it removed, please contact us and we will comply promptly.
RationalRewards-EvalData-GenAIBench-MMRB2-ERBenchTLDR: this is the RewardModel Evaluation dataset for text-to-image generation and image editing, from the following paper.
RationalRewards: Reasoning Rewards Scale Visual Generation Both Training and Test Time
Haozhe Wang1
Cong Wei2
Weiming Ren2
Jiaming Liu3
Fangzhen Lin1
Wenhu Chen2
1 HKUST
2 University of Waterloo
3 Alibaba
RationalRewards is a… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/RationalRewards-EvalData-GenAIBench-MMRB2-ERBench.ultrafeedback_rationale_Qwen2.5-3B-Instruct_cotMind2Web-HTML-cleaned-lite-with-desc_w_tao_value_rationalemagpie-reasoning-v1-20k-math-verifiable-step-by-step-rationaleRationaleVQAultrafeedback_rationale_Qwen2.5-3B-Instruct_ultra_sft_2e-5_thre-0.7_packing_42_cotSFT_PN_Rationales
SFT Dataset
generated from Qwen/Qwen3-VL-32B-Instruct
verified from OpenGVLab/InternVL3-78B
Domain Distribution of Positive/Negative Rationales
Per-Dataset Positive/Negative Rationale Counts by Domain
ultrafeedback_rationale_Llama-3.2-3B-Instruct_cotLitBench-new-rationales
Dataset Card for "LitBench-Rationales-GPT4-Complete"
More Information needed
magpie-reasoning-v1-10k-step-by-step-rationaleultrabin_clean_max_chosen_min_rejected_rationalized_instruction_followinglitbench-rationales-gpt4
LitBench Rationales - GPT-4 Rubric Evaluations
This dataset contains new rationales for story pair evaluations from the LitBench dataset, generated using GPT-4 with a structured rubric-based evaluation approach.
Evaluation Rubric
The rationales were generated using a 5-criterion rubric:
Creativity & Originality (25 points): Uniqueness of concept, innovative elements, fresh perspective
Writing Quality & Style (25 points): Prose quality, voice consistency, grammar and… See the full description on the dataset page: https://huggingface.co/datasets/SAA-Lab/litbench-rationales-gpt4.ultrafeedback_rationale_Qwen2.5-3B-Instruct_directfinetuning_datasetwebagent_policy_rationale_formattedwos_with_rationaleOpenVul_Rationalization_based_Vulnerability_Reasoning_Dataset_for_SFTThis dataset provides all training data's vulnerability reasoning CoTs (with one generation per sample) distilled from DeepSeek-R1-0528, collected using a rationalization-based data curation method.
google_1.1_rationalRationalization-AlpacaCoT-Collection-Rationalesletter_only_no_rationalefull_answer_rationalemovie_rationales_truncatedultrabin_clean_max_chosen_min_rejected_rationalized_helpfulness