datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
FiVE-Fine-Grained-Video-Editing-Benchmark
FiVE-Bench
FiVE-Bench: A Fine-Grained Video Editing Benchmark for Evaluating Diffusion and Rectified Flow Models
Minghan Li1*, Chenxi Xie2*, Yichen Wu13, Lei Zhang2, Mengyu Wang1†
1Harvard University 2The Hong Kong Polytechnic University 3City University of Hong Kong
*Equal contribution †Corresponding Author
💜 Leaderboard (coming soon) |
💻 GitHub |
🤗 Hugging Face
📝 Project Page |
📰 Paper |
🎥 Video Demo
FiVE is a benchmark comprising 100 videos for… See the full description on the dataset page: https://huggingface.co/datasets/LIMinghan/FiVE-Fine-Grained-Video-Editing-Benchmark.fine-grained-medical-reasoning
Dataset Card for Fine-Grained Medical Reasoning
Fine-grained medical reasoning QA dataset introduced in "Can LLMs Reason Like Doctors? Exploring the Limits of Large Language Models in Complex Medical Reasoning"
(Findings of EACL 2026). Manually annotated from the MedAgentsBench test_hard set,
it evaluates LLMs’ abduction, deduction, and induction capabilities, offering detailed insights into physician-like reasoning.
Dataset Details
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/expertailab/fine-grained-medical-reasoning.FiVE-Fine-Grained-Video-Editing-Benchmark
FiVE-Bench
FiVE-Bench: A Fine-Grained Video Editing Benchmark for Evaluating Diffusion and Rectified Flow Models
Minghan Li1*, Chenxi Xie2*, Yichen Wu13, Lei Zhang2, Mengyu Wang1†
1Harvard University 2The Hong Kong Polytechnic University 3City University of Hong Kong
*Equal contribution †Corresponding Author
💜 Leaderboard (coming soon) |
💻 GitHub |
🤗 Hugging Face
📝 Project Page |
📰 Paper |
🎥 Video Demo
FiVE is a benchmark comprising 100 videos for… See the full description on the dataset page: https://huggingface.co/datasets/CiaranCw/FiVE-Fine-Grained-Video-Editing-Benchmark.fine_grained_media_sentiments_annotationsTask: Aspect based sentiment recognition. Domain: Political news coverage. Named entities have been masked with [NEG], [NEU], or [POS] tokens. Dataset size: 1400 clippits.
An ABSA-BERT model successfully leveraged this dataset, with semi-supervised learning, to get very good results on fine grained sentiment recognition.
Creation: Five of my friends voluntarily used my browser plugin annotation tool I made to send me marked sentences of the news they were reading. How well they understood the… See the full description on the dataset page: https://huggingface.co/datasets/bitsinthesky/fine_grained_media_sentiments_annotations.finegrained-halu-tw