19,997 machine learning datasets
19,997 dataset results
This project contains instructions and codes to reconstruct a dataset for the development and evaluation of forensic tools for detecting machine-generated text in social media.
Recent evaluations of Large Language Models (LLMs) have centered around testing their zero-shot/few-shot capabilities for basic natural language tasks and their ability to translate instructions into tool APIs. However, the evaluation of LLMs utilizing complex tools to finish multi-turn, multi-modal instructions in a complex multi-modal environment has not been investigated. To address this gap, we introduce the PowerPoint Task Completion (PPTC) benchmark to assess LLMs' ability to create and edit PPT files based on user instructions. It contains 279 multi-turn sessions covering diverse topics and hundreds of instructions involving multi-modal operations.
The dataset is hosted on GitHub: https://github.com/ucsdsysnet/nsl-empirical-analysis and explained in our paper.
This repository contains BasqueParl, a bilingual corpus for political discourse analysis. It covers transcriptions from the Parliament of the Basque Autonomous Community for eight years and two legislative terms (2012-2020), and its main characteristic is the presence of Basque-Spanish code-switching speeches.
We introduce the novel task of multimodal puzzle solving, framed within the context of visual question-answering. We present a new dataset, AlgoPuzzleVQA designed to challenge and evaluate the capabilities of multimodal language models in solving algorithmic puzzles that necessitate both visual understanding, language understanding, and complex algorithmic reasoning. We create the puzzles to encompass a diverse array of mathematical and algorithmic topics such as boolean logic, combinatorics, graph theory, optimization, search, etc., aiming to evaluate the gap between visual data interpretation and algorithmic problem-solving skills. The dataset is generated automatically from code authored by humans. All our puzzles have exact solutions that can be found from the algorithm without tedious human calculations. It ensures that our dataset can be scaled up arbitrarily in terms of reasoning complexity and dataset size. Our investigation reveals that large language models (LLMs) such as GPT
This dataset is composed of 7,753 pairs of whole slide images and their corresponding diagnostic reports, extracted from the TCGA platform and refined with large language models. This dataset aims to boost the field of automated histopathology report generation by providing a new publicly available evaluation benchmark. See HistGen paper (see https://arxiv.org/pdf/2403.05396.pdf for reference) for a more detailed description of this dataset.
battery surface temperature from 16 sensors.
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
Read more about the dataset here: https://github.com/ServiceNow/seasonal-contrast
To effectively evaluate OmniCount across open-vocabulary, supervised, and few-shot counting tasks, a dataset catering to a broad spectrum of visual categories and instances featuring various visual categories with multiple instances and classes per image is essential. The current datasets, primarily designed for object counting focusing on singular object categories like humans and vehicles, fall short for multi-label object counting tasks. Despite the presence of multi-class datasets like MS COCO, their utility is limited for counting due to the sparse nature of object appearance. Addressing this gap, we created a new dataset with 30,230 images spanning 191 diverse categories, including kitchen utensils, office supplies, vehicles, and animals. This dataset, featuring a wide range of object instance counts per image ranging from 1 to 160 and an average count of 10, bridges the existing void and establishes a benchmark for assessing counting models in varied scenarios.
A set of over 4000 dark corner artifact / vignette masks that can be applied to ISIC skin lesion images to test for the effect of such artifacts in classification tasks.
The MPII Human Pose Descriptions dataset extends the widely-used MPII Human Pose Dataset with rich textual annotations. These annotations are generated by various state-of-the-art language models (LLMs) and include detailed descriptions of the activities being performed, the count of people present, and their specific poses.
Qubit energy relaxation versus frequency and time
From https://github.com/MMintLab/VIRDO/blob/master/data/dataset_readme.txt,
The HH Red Teaming dataset comprises two distinct types of data, each serving a unique purpose:
The MemoTrap dataset is a diagnostic dataset designed to assess whether language models (LMs) fall into memorization traps. These traps occur when LMs memorize specific examples from their training data instead of generalizing effectively. The dataset aims to probe the susceptibility of LMs to this phenomenon.
The MNumGLUESub dataset is a multi-task arithmetic reasoning benchmark. It is part of the NumGLUE collection, which focuses on evaluating mathematical reasoning abilities in various languages. Let's break down the details:
The World Flora Online (WFO) is a comprehensive initiative that aims to create an online flora of all known plants. Here are some key details about the WFO:
Data used in the work ”Unifying the design space and optimizing linear and nonlinear truss metamaterials by generative modeling”. This dataset contains 965,736 truss structures and their corresponding effective homogenized stiffness.
Semantic segmentation of drone images is critical for various aerial vision tasks as it provides essential semantic details to understand scenes on the ground. Ensuring high accuracy of semantic segmentation models for drones requires access to diverse, large-scale, and high-resolution datasets, which are often scarce in the field of aerial image processing. While existing datasets typically focus on urban scenes and are relatively small, our Varied Drone Dataset (VDD) addresses these limitations by offering a large-scale, densely labeled collection of 400 high-resolution images spanning 7 classes. This dataset features various scenes in urban, industrial, rural, and natural areas, captured from different camera angles and under diverse lighting conditions. We also make new annotations to UDD and UAVid, integrating them under VDD annotation standards, to create the Integrated Drone Dataset (IDD). It's expected that our dataset will generate considerable interest in drone image segmenta