datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
newyorker_caption_ranking
New Yorker Caption Ranking Dataset
Dataset Descriptions
Homepage: https://nextml.github.io/caption-contest-data/
Repository: https://github.com/yguooo/cartoon-caption-generation
Paper: Humor in AI: Massive Scale Crowd-Sourced Preferences and Benchmarks for Cartoon Captioning
Point of Contact: yguo@cs.wisc.edu
Dataset Summary
We present a novel multimodal preference dataset for creative tasks, consisting of over 250 million human ratings on more than 2.2… See the full description on the dataset page: https://huggingface.co/datasets/yguooo/newyorker_caption_ranking.PersonaGen-Enterprise
PersonaGen-Enterprise: B2B Buying Intelligence Dataset
5,000 enterprise buyer personas with full buying committee modeling across 15 industries, 3 company sizes, and 42 buying roles. Plus 47K real search queries, 7.5K competitive brand queries, and multi-model agreement scores.
Built by Rankfor.AI, the AI Visibility Intelligence platform. This dataset powers research into how enterprise buyers search for, evaluate, and select B2B technology vendors.
Enterprise… See the full description on the dataset page: https://huggingface.co/datasets/rankfor/PersonaGen-Enterprise.random-lossy-singlestep-5000
random-lossy-singlestep-5000
Single-step SFT dataset for graphic design editing, generated using the Random Perturbation baseline from the Perturb-and-Invert (P&I) framework.
What's in it
Each example trains a model to predict a single tool call given the current design state and the history of tool calls applied so far. Trajectories are generated by sampling random tools with valid parameters from the cyberagent/crello template dataset.
Lossy inverses:… See the full description on the dataset page: https://huggingface.co/datasets/monish-adobe/random-lossy-singlestep-5000.random-lossy-oneshot-5000
random-lossy-oneshot-5000
One-shot SFT dataset for graphic design editing, generated using the Random Perturbation baseline from the Perturb-and-Invert (P&I) framework.
What's in it
Each example trains a model to output the full inverse trajectory (all tool calls) in a single response given the initial design state and a natural-language edit request. Generated from cyberagent/crello templates using randomly sampled tools and parameters.
Lossy inverses:… See the full description on the dataset page: https://huggingface.co/datasets/monish-adobe/random-lossy-oneshot-5000.