19,997 machine learning datasets
19,997 dataset results
Existing arithmetic benchmarks have a limited number of True-or-False questions. To address this gap, MathToF is created including 1,000 Chinese mathematical True-or-False questions with detailed explanations and focusing on math problems typically encountered in grades 4 to 6. It features a wide range of question types, including arithmetic, algebra, geometry, statistics, reasoning, and more, enhancing the diversity of current Chinese arithmetic datasets.
RaTE-NER dataset is a large-scale, radiological named entity recognition (NER) dataset, including 13,235 manually annotated sentences from 1,816 reports within the MIMIC-IV database, that spans 9 imaging modalities and 23 anatomical regions, ensuring comprehensive coverage.
To effectively measure the alignment between automatic evaluation metrics and radiologists' assessments in medical text generation tasks, we have established a comprehensive benchmark, RaTE-Eval, that encompasses three tasks, each with its official test set for fair comparison.
Climate change poses critical challenges globally, disproportionately affecting low-income countries that often lack resources and linguistic representation on the international stage. Despite Bangladesh's status as one of the most vulnerable nations to climate impacts, research gaps persist in Bengali-language studies related to climate change and NLP. To address this disparity, we introduce ধরণী (Dhoroni), a novel Bengali (Bangla) climate change and environmental news dataset, comprising a 2300 annotated Bangla news articles, offering multiple perspectives such as political influence, scientific/statistical data, authenticity, stance detection, and stakeholder involvement. Furthermore, we present an in-depth exploratory analysis of Dhoroni and introduce BanglaBERT-Dhoroni family, a novel baseline family for climate stance detection in Bangla, fine-tuned on our dataset. This research contributes significantly to enhancing accessibility and analysis of climate discourse in Bengali (Ban
Bangladesh's legal system struggles with major challenges like delays, complexity, high costs, and millions of unresolved cases, which deter many from pursuing legal action due to lack of knowledge or financial constraints. This research seeks to develop a specialized Large Language Model (LLM) to assist in the Bangladeshi legal system. We created UKIL-DB-EN, an English corpus of Bangladeshi legal documents, by collecting and scraping data on various legal acts. We fine-tuned the GPT-2 model on this dataset to develop GPT2-UKIL-EN, an LLM focused on providing legal assistance in English. The model was rigorously evaluated using semantic assessments, including case studies supported by expert opinions. The evaluation provided promising results, demonstrating the potential for the model to assist in legal matters within Bangladesh. Our work represents the first structured effort toward building an AI-based legal assistant for Bangladesh. While the results are encouraging, further refinem
CoCoPops is a meta-corpus of melodic and harmonic transcriptions of popular music. CoCoPops has been developed primarily by Nat Condit-Schultz and Claire Arthur in the Computational and Cognitive Musicology Lab, within the Georgia Tech Center for Music Technology. The goal of CoCoPops is to make a large ammount of comparable melodic/harmonic data available in a consistent, standardized format. All CoCoPops files are stored in humdrum format. CoCoPops currently includes of two main sub-corpora, the Billboard subset and the Rolling Stone subset. We plan on continuing to add more as additional datasets and corpora of popular music with melodic transcriptions become available.
PolyMATH, a challenging benchmark aimed at evaluating the general cognitive reasoning abilities of MLLMs. PolyMATH comprises 5,000 manually collected high-quality images of cognitive textual and visual challenges across 10 distinct categories, including pattern recognition, spatial reasoning, and relative reasoning
Dataset contains CS/Math articles abstracts (in Russian) obtained from two online sources. For each article publication year, journal name, authors, title, keyphrases and abstract are provided.
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
We study dynamic appearance models of both relightable (BRDF) and non-relightable (RGB). For both we introduce new pilot datasets, allowing, for the first time, to study such phenomena: For RGB we provide 22 dynamic textures acquired from free online sources; For BRDFs, we further acquire a dataset of 21 flash-lit videos of time-varying materials, enabled by a simple-to-construct setup.
Over 4000 single cycle waveforms, 600 samples
In the last years, the research interest in visual navigation towards objects in indoor environments has grown significantly. This growth can be attributed to the recent availability of large navigation datasets in photo-realistic simulated environments, like Gibson and Matterport3D. However, the navigation tasks supported by these datasets are often restricted to the objects present in the environment at acquisition time. Also, they fail to account for the realistic scenario in which the target object is a user-specific instance that can be easily confused with similar objects and may be found in multiple locations within the environment. To address these limitations, we propose a new task denominated Personalized Instance-based Navigation (PIN), in which an embodied agent is tasked with locating and reaching a specific personal object by distinguishing it among multiple instances of the same category. The task is accompanied by PInNED, a dedicated new dataset composed of photo-realis
We introduce misinfo-general, a benchmark dataset for evaluating misinformation models’ ability to perform out-of-distribution generalisation. Misinformation changes rapidly, much quicker than moderators can annotate at scale, resulting in a shift between the training and inference data distributions. As a result, misinformation models need to be able to perform out-of-distribution generalisation, an understudied problem in existing datasets.
OVT-B, a large-scale Open-Vocabulary multi-object Tracking Benchmark containing 1,973 videos and 637,608 annotated objects from 1,048 categories, surpassing the diversity of all current MOT datasets. OVT-B also includes some attributes especially for the MOT task, e.g., the scenarios of out-of-view, fast motion, mutual occlusion, and objects with various sizes, shapes, etc.
In this dataset we added [Company Name, Car Model, Car Type, Fuel Type, Transmission, Engine (cc), Mileage, Kms_driven, Buyers, Horsepower (kw), Year Price (Lakhs)]
To address the scarcity of high-quality safety datasets in the Chinese, we open-sourced the CCI (Chinese Corpora Internet) dataset on November 29, 2023. Building on this foundation, we continue to expand the data source, adopt stricter data cleaning methods, and complete the construction of the CCI 3.0 dataset. This dataset is composed of high-quality, reliable Internet data from trusted sources. And then with more stricter filtering, The CCI 3.0 HQ corpus released is about 500GB in size.
Our dataset, BSMDD, was collected from various open social media platforms and translated and annotated by native Bengali speakers with expertise in both language and mental health. It contains 21,910 cleaned samples, including 10,961 labeled as Depressed and 10,949 as Non-Depressed. The dataset is publicly accessible, providing a valuable resource for further research in depression detection in Bengali social media content. The expert annotation process, conducted by professionals, ensures high validity, making BSMDD particularly important for advancing mental health research through social media analysis. This dataset is also published on Mendeley.
Text present in images are not merely strings, they provide useful cues about the image. Despite their utility in better image understanding, scene texts are not used in traditional visual question answering (VQA) models. In this work, we present a VQA model which can read scene texts and perform reasoning on a knowledge graph to arrive at an accurate answer. Our proposed model has three mutually interacting modules: (i) proposal module to get word and visual content proposals from the image, (ii) fusion module to fuse these proposals, question and knowledge base to mine relevant facts, and represent these facts as multi-relational graph, (iii) reasoning module to perform a novel gated graph neural network based reasoning on this graph.
The Perfume Co-Preference Network dataset comprises comprehensive user reviews and ratings collected from the Persian retail platform Atrafshan. This dataset, central to our research on community detection in fragrance preferences, includes 36,434 comments from 7,387 unique users, providing insights into consumer sentiment towards various perfumes. It is designed to facilitate the analysis of user preferences through sentiment analysis, allowing for the clustering of perfumes based on shared attributes.
A tutorial for the improved Loewner Framework for modal analysis