datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Fine_Grained_Fandom_Benchmark_Action_Sequences
Codified Decision Tree (CDT) Action Sequences
This dataset contains scene-action pairs derived from storylines, used to train and evaluate role-playing (RP) agents using the Codified Decision Trees (CDT) framework.
Paper: Deriving Character Logic from Storyline as Codified Decision Trees
Repository: https://github.com/KomeijiForce/Codified_Decision_Tree
Introduction
Role-playing (RP) agents rely on behavioral profiles to act consistently across diverse narrative… See the full description on the dataset page: https://huggingface.co/datasets/KomeijiForce/Fine_Grained_Fandom_Benchmark_Action_Sequences.formosa-vision-finegrained
Formosa Vision Fine-grained (Expanded)
Dataset Summary
此資料集以台灣在地文化與地景為核心，提供具細節的中文描述，並保留原始圖像。
擴充版本針對每張圖像生成更長、更密集的語義描述，以強化模型在細節理解上的表現。
Motivation
『資料合成』FLAIR 的核心在於訓練模型「聽得懂細節」。這意味著「長文本」越具體、包含越多方位詞 (左上角、紅色物體旁...)，模型學到的局部特徵就越好。因為在此階段會透過大型多模態模型生成豐富且長的中文描述夠「碎唸」(包含大量方位、顏色、材質等細節)。相較於網路爬蟲數據，此資料庫具備高品質的本土文化實體 (Entity) 標註，是訓練台灣在地化 AI 的最佳基石。
Source Data
原始資料集：twinkle-ai/Formosa-Vision（Hugging Face Datasets）
擴充流程：以本地 VLM 產生更細緻的中文長描述
Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/renhehuang/formosa-vision-finegrained.fine_grained_unlearning
Fine-Grained Knowledge Unlearning — Namesake Benchmark
A benchmark for fine-grained knowledge unlearning: can a method remove a fact
about entity X without damaging the same fact on entity Y, when X and Y
have (near-)identical names and share exactly that one attribute?
Each sample is a pair of real people who
share an identical or near-identical name,
share one career element (e.g. both are basketball players) — the fact to
unlearn on X and retain on Y,
differ on everything… See the full description on the dataset page: https://huggingface.co/datasets/ernlavr/fine_grained_unlearning.