19,997 machine learning datasets
19,997 dataset results
MediBeng Dataset The MediBeng dataset contains synthetic code-switched dialogues in Bengali and English for training models in speech recognition (ASR), text-to-speech (TTS), and machine translation in clinical settings. The dataset is available under the CC-BY-4.0 license.
OpenING is a comprehensive benchmark comprising 5,400 high-quality human-annotated instances across 56 real-world tasks. OpenING covers diverse daily scenarios such as travel guide, design, and brainstorming, offering a robust platform for challenging interleaved image-text generation methods.
Filipino CrowS-Pairs and Filipino WinoQueer assess sexist and homophobic biases in language models handling Filipino.
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The TVPReid dataset contains 6559 pedestrian videos, each of which is annotated with two text descriptions, for a total of 13118 descriptions. The sentence descriptions are in a natural language style and contain rich details about the pedestrian's appearance, actions, and environmental elements that the pedestrian interacts with. The average sentence length of the TVPReid dataset is 30 words, and the longest sentence contains 83 words.
At KayifamilyTv, we introduce a powerful and scalable video-mining solution designed to enhance captioning capabilities by transferring supervision from image datasets to video and audio content. Using this innovative pipeline, we mine paired video clips and captions, starting with the Conceptual Captions 3M (CC3M) image dataset as our foundation. The result of this process is VideoCC3M—a large-scale collection of millions of video clips weakly paired with text captions, which we plan to make publicly available.
The StudyAbroadGPT-Dataset is a collection of conversational data focused on university application requirements for various programs, including MBA, MS in Computer Science, Data Science, and Bachelor of Medicine. The dataset includes interactions between humans asking questions about application processes (e.g., "How do I write a strong SOP for MS in Data Science at MIT?") and an assistant providing detailed responses. Covering prestigious institutions such as MIT, Oxford, Cambridge, and Stanford, this dataset serves as a valuable resource for understanding the informational needs of prospective students applying to study abroad.
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SemanticSugarBeets, a novel and high-quality dataset containing 953 monocular RGB images and 2920 annotations of sugar beets, enables a wide range of learning tasks including object detection, semantic segmentation, instance segmentation and mass estimation for post-harvest and post-storage analysis.
A policy knowledge graph can provide decision support for tasks such as project compliance, policy analysis, and intelligent question answering, and can also serve as an external knowledge base to assist the reasoning process of related large language models. Although there have been many related works on knowledge graphs, there is currently a lack of research on the construction methods of policy knowledge graphs. This paper, focusing on the forestry field, designs a complete policy knowledge graph construction framework, including: firstly, proposing a fine-grained forestry policy domain ontology; then, proposing an unsupervised policy information extraction method, and finally, constructing a complete forestry policy knowledge graph. The experimental results show that the proposed ontology has good expressiveness and extensibility, and the policy information extraction method proposed in this paper achieves better results than other unsupervised methods. Furthermore, by analyzing th
100 videos with varying danger levels (on a scale of 0-10) and different scenarios, annotated by 18 human annotators using our annotation pipeline to represent human perception and respective Vision Language model summaries for each of the videos as benchmarks for testing LLMs' danger perceptions.
PreRAID is a structured dataset designed to evaluate the diagnostic capabilities of Large Language Models (LLMs) in Rheumatoid Arthritis (RA) diagnosis. This dataset provides real-world patient data, offering insights into RA prediction and reasoning accuracy.
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This dataset includes four real-world sub-datasets about traffic demand. Each dataset spans 16 months, from January to April of the next year.
This repository contains documentation for the dataset that accompanies our ICPE 2025 paper, "Shaved Ice: Optimal Compute Resource Commitments for Dynamic Multi-Cloud Workloads". It also includes example R and Python notebooks to read and visualize the data, including scripts to reproduce the figures and analysis results in the paper.
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This dataset is associated with the paper published in Scientific Data, titled "SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting over a Large Turbine Array." You can access the paper: https://www.nature.com/articles/s41597-024-03427-5
TF1-EN-3M: Three Million Synthetic Moral Fables for Open Language Models TF1-EN-3M is a large-scale synthetic dataset of 3,000,000 English-language moral fables, generated by instruction-tuned language models with no more than 8 billion parameters. The stories are aimed at child-friendly educational and moral reasoning applications and follow a consistent six-part narrative scaffold: character → trait → setting → conflict → resolution → moral.
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