19,997 machine learning datasets
19,997 dataset results
The AppealCase dataset is the first large-scale resource specifically designed to support LegalAI research in appellate judgment scenarios. While prior work in LegalAI has focused heavily on one-shot trials, the appellate procedure—critical to ensuring fairness and correcting judicial errors—remains largely underexplored.
The dataset contains dialogs of different LLMs from the discussion phase of a text-based Among Us-like game. The phrases in the dataset were annotated according to 25 selected persuasion techniques: Appeal to Logic, Appeal to Emotion, Appeal to Credibility, Shifting the Burden of Proof, Bandwagon Effect, Distraction, Gaslighting, Appeal to Urgency, Deception, Lying, Feigning Ignorance, Vagueness, Minimization, Self-Deprecation, Projection, Appeal to Relationship, Humor, Sarcasm, Withholding Information, Exaggeration, Denial without Evidence, Strategic Voting Suggestion, Appeal to Rules, Confirmation Bias Exploitation, Information Overload. The annotation was performed automatically by few-shot prompting a Gemini Flash 1.5 model with a temperature of 0. On a random sample of 11 games involving a total of 509 persuasion tags, Krippendorff's alpha inter-rater agreement between human annotations and the persuasion tagger was 0.56. For the definitions of the persuasion techniques, please re
KCIF is a benchmark for evaluating the instruction-following capabilities of Large Language Models (LLM). We adapt existing knowledge benchmarks and augment them with instructions that are a) conditional on correctly answering the knowledge task or b) use the space of candidate options in multiple-choice knowledge-answering tasks. KCIF allows us to study model characteristics, such as their change in performance on the knowledge tasks in the presence of answer-modifying instructions and distractor instructions.
The synRailObs contains following categories: - Person - Rocks - Vehicles - Moto-cars - Animals
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In this dataset we teleoperated UR5 arm to collect manipulation data for picking up a screwdriver in a cluttered tabletop environment.
MathEquiv dataset is accompanied to EquivPruner . It is specifically designed for mathematical statement equivalence , serving as a versatile resource applicable to a variety of mathematical tasks and scenarios. It consists of almost 100k math sentences pair with equivalence result and reasoning step generated by GPT-4O.
📊 Dataset Details
Description: The WEA dataset is derived from the WeatherBench repository and designed for medium-range weather forecasting at five geographically diverse cities: London (UK), New York (US), Hong Kong (China), Cape Town (South Africa), and Singapore. It spans the period from 1979 to 2018, with a temporal resolution of 6 hours and a spatial resolution of 5.625° in both latitude and longitude. Each city is matched to its nearest grid point on the WeatherBench grid using minimal absolute distance in both axes.
MVTec-AC is a curated refinement of the widely-used MVTec-AD dataset, specifically designed for anomaly classification—distinguishing between different types of anomalies rather than merely detecting if an image is anomalous. While MVTec-AD focuses on binary detection and suffers from mislabeled or ambiguous samples, MVTec-AC introduces manually corrected labels and reorganized anomaly categories to enable robust multi-class evaluation. Key improvements include the correction of 36 misclassified samples, merging of 4 overlapping classes, removal of 4 ambiguous ‘combined’ classes, and exclusion of the toothbrush category, which contains only a single trivial anomaly type. These changes support consistent, fine-grained assessment of classification models in industrial visual inspection contexts.
VisA-AC is a refined benchmark based on the VisA dataset, tailored for the task of anomaly classification—distinguishing between different types of anomalies rather than simply detecting whether an image is anomalous. While the original VisA provides anomaly type information in an Excel file, it includes numerous under-sampled and ambiguous classes. VisA-AC addresses these issues by removing classes with fewer than 10 samples, merging visually similar categories, and manually correcting mislabeled samples. Additionally, anomaly classes in VisA-AC are organized into separate folders—following the structure of MVTec-AC—for easier integration and usage. The resulting dataset ensures both statistical robustness and semantic clarity, supporting rigorous evaluation of multi-class anomaly classification methods in real-world industrial settings.
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Recognizing complex emotions linked to ambivalence and hesitancy (A/H) can play a critical role in the personalization and effectiveness of digital behaviour change interventions. These subtle and conflicting emotions are manifested by a discord between multiple modalities, such as facial and vocal expressions, and body language. Although experts can be trained to identify A/H, integrating them into digital interventions is costly and less effective. Automatic learning systems provide a cost-effective alternative that can adapt to individual users, and operate seamlessly within real-time, and resource-limited environments. However, there are currently no datasets available for the design of ML models to recognize A/H.
We create the first open-source large-scale S2V generation dataset OpenS2V-5M, which consists of five million high-quality 720P subject-text-video triples. To ensure subject-information diversity in our dataset by, we (1) segmenting subjects and building pairing information via cross-video associations and (2) prompting GPT-Image on raw frames to synthesize multi-view representations. The dataset supports both Subject-to-Video and Text-to-Video generation tasks.
The DAVIDE dataset consists of synchronized blurred, depth, and sharp videos. The dataset comprises 90 video sequences divided into 69 for training, 7 for validation, and 14 for testing. The test set includes annotations of seven content attributes categorized by: 1) environment (indoor/outdoor), 2) motion (camera motion/camera and object motion), and 3) scene proximity (close/mid/far). These annotations aim to facilitate further analysis into scenarios where depth information could be more beneficial.
SimpleStories is a dataset of >2 million model-generated short stories. It was made to train small, interpretable language models on it. The generation process is open-source: To see how the dataset was generated, or to generate some stories yourself, head over to https://github.com/lennart-finke/simple_stories_generate.
OOD split of the Mol-Instructions Dataset about Protein Annotation.
real-world doctor-patient question- answering dataset cleaned manually and automatically
real-world doctor-patient question- answering dataset
a 90 million token medical corpus crawled from medical websites