19,997 machine learning datasets
19,997 dataset results
Dataset Description
Dataset description
Dataset description
BMI/OpenBMI dataset for MI.
BMI/OpenBMI dataset for MI.
Dataset description We recruited 15 healthy subjects aged between 22 and 40 years with a mean age of 27 years (standard deviation 5 years). Nine subjects were female, and all the subjects except s1 were right-handed.
This dataset is based on FB15k237 and a pre-trained language-model-based KGE. The main task is to correct the wrong knowledge stored in the pre-trained model and replace the incorrect entities with alternative entities. The model can be downloaded from here.
Dataset Description Our dataset contains questions from a well-known software testing book Introduction to Software Testing 2nd Edition by Ammann and Offutt. We use all the text-book questions in Chapters 1 to 5 that have solutions available on the book’s official website.
It is a new large-scale pulmonary nodule dataset named PN9, which contains 8,798 thoracic CT scans and a total of 40,439 annotated nodules.
This open-source dataset consists of 5.04 hours of transcribed English conversational speech beyond telephony, where 13 conversations were contained.
This is an image dataset for object detection of wildlife in the mixed coniferous broad-leaved forest.
SESYD "Systems Evaluation SYnthetic Documents" is a database of synthetical documents with groundtruth. This database targets two main research problems in the document image analysis field (i) symbol recognition and spotting in line drawing images (floorplans and electrical diagrams) (ii) character segmentation and recognition in geographical maps. The database is composed of eleven collections for performance evaluation containing 284k images, 190k symbols and 284k characters (k for thousand). SESYD is today a key database in the document image analysis field published in 2010 and referred by one hundred of citations into research papers.
This is a dataset of paraphrases created by ChatGPT.
ESP dataset (Evaluation for Styled Prompt dataset) is a new benchmark for zero-shot domain-conditional caption generation. The dataset aims to evaluate the capability to generate diverse domain-specific language conditioned on the same image. It comprises 4.8k captions from 1k images in the COCO Captions test set. We collected five text domains with everyday usage: blog, social media, instruction, story, and news using Amazon MTurk.
The Customer Support on Twitter dataset is a large, modern corpus of tweets and replies to aid innovation in natural language understanding and conversational models, and for study of modern customer support practices and impact.
We propose a new light field image database called “PINet” inheriting the hierarchical structure from WordNet. It consists of 7549 LIs captured by Lytro Illum, which is much larger than the existing databases. The images are manually annotated to 178 categories according to WordNet, such as cat, camel, bottle, fans, etc. The registered depth maps are also provided. Each image is generated by processing the raw LI from the camera by Light Field Toolbox v0.4 for demosaicing and devignetting.
Pathfinder and Pathfinder-X have proven to be instrumental in training and testing Large Language Models with long-range dependencies. Recently, Meta's Moving Average Equipped Gated Attention model scored a 97% on the Pathfinder-X dataset, indicating a need for a larger, more challenging dataset. Whereas Pathfinder-X only went up to 256 x 256 pixel images (or a sequence length of 65,536 tokens), Pathfinder-X2 introduces images of 512 x 512 pixels, or 262,144 tokens.
We provide a Mikolov-style word-analogy evaluation set specifically for Bangla, with a sample size of 16678, as well as a translated and curated version of the Mikolov dataset, which contains 10594 samples for cross-lingual research.
A Filipino multi-modal language dataset for text+visual tasks. Consists of 351,755 Filipino news articles gathered from Filipino news outlets.
Detecting Multi-labeled Emotion for 6 emotion categories, namely Love, Joy, Surprise, Anger, Sadness, Fear.