datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
food-composition-matrix
Food Composition Nutrient Matrix — TKPI 2017 & USDA SR Legacy 2018
This repository contains two food composition datasets reformatted as wide-format nutrient matrices, suitable for a wide range of research tasks including nutrient prediction, food type classification, missing value imputation, dietary analysis, and other machine learning applications on food data. Both datasets share a harmonised set of 18 common nutrients, enabling cross-dataset generalization experiments.… See the full description on the dataset page: https://huggingface.co/datasets/ULM-DS-Lab/food-composition-matrix.Food-Composition
Ingredients CSV/Parquet File
Overview
The following data comes from the United States Department of Agriculture’s Food Composition Database. It contains data for various types of food ingredients including the amounts of different vitamins and minerals found in the foods as well as macronutrient percentages. The food covered spans a large variety of foods from butter to Campbell’s soup. Much of the supplementary documenation for each field comes directly from that pages’… See the full description on the dataset page: https://huggingface.co/datasets/hootan09/Food-Composition.tampa-bay-vacancy-composition-zcta-2020-2024
Tampa Bay vacancy composition by ZCTA, 2020-2024
Author: Richard (Ryszard) Cieplechowicz. DOI: https://doi.org/10.5281/zenodo.23114340 . Study page: https://richardcieplechowicz.com/ . Also on Figshare: https://doi.org/10.6084/m9.figshare.34062054.v1
Richard Cieplechowicz (also legally known as Ryszard Cieplechowicz) · September 29, 2026
A vacancy rate alone does not say why housing units are vacant. In the 132 Tampa Bay ZIP Code Tabulation Areas (ZCTAs) selected for the… See the full description on the dataset page: https://huggingface.co/datasets/richardcieplechowicz/tampa-bay-vacancy-composition-zcta-2020-2024.compositional-preference-modeling
Dataset Featurization: Compositional Preference Modeling
This repository contains the datasets used in our case study on compositional preference modeling from Dataset Featurization, demonstrating how our unsupervised featurization pipeline can produce features describing human preferences and match expert-level produced features. This case study is built on top of Compositional Preference Modeling (CPM).
HH-RLHF - Featurization
Utilizing HH-RLHF dataset, we provide… See the full description on the dataset page: https://huggingface.co/datasets/Bravansky/compositional-preference-modeling.Prediction-and-optimization-of-syngas-composition-from-hydrothermal-gasification