19,997 machine learning datasets
19,997 dataset results
Charlotte-ThermalFace is a thermal face dataset. The data is fully annotated with the facial landmarks, ambient temperature, relative humidity, the air speed of the room, distance to the camera, and subject thermal sensation at the time of capturing each image.
The ExaASC dataset is a dataset for Target-based Stance Detection in the Arabic Language that contains different types of targets like persons, entities and events. This corpus contains about 9500 tweets with replies and target specified in the source tweet. Each sample has at least two stance annotations provided by Exa Corporation annotators. The stance of each reply is annotated toward the target in the corresponding source tweet. Format of data is as follows: id, main (source tweet), reply, target, label of each annotator id and majority_label.
Contains 1,342,667 full text articles in English, together with the associated MeSH labels and metadata, authors, and publication venues that are collected from the MEDLINE database.
A corpus of 9k German and French user comments collected from migration-related news articles. It goes beyond the hate-neutral dichotomy and is instead annotated with 23 features, which in combination become descriptors of various types of speech, ranging from critical comments to implicit and explicit expressions of hate. The annotations are performed by 4 native speakers per language and achieve high (0.77) inter-annotator agreements.
W-Oops consists of 2,100 unintentional human action videos, with 44 goal-directed and 30 unintentional video-level activity labels collected through human annotations.
This dataset of medical misinformation was collected and is published by Kempelen Institute of Intelligent Technologies (KInIT). It consists of approx. 317k news articles and blog posts on medical topics published between January 1, 1998 and February 1, 2022 from a total of 207 reliable and unreliable sources. The dataset contains full-texts of the articles, their original source URL and other extracted metadata. If a source has a credibility score available (e.g., from Media Bias/Fact Check), it is also included in the form of annotation. Besides the articles, the dataset contains around 3.5k fact-checks and extracted verified medical claims with their unified veracity ratings published by fact-checking organisations such as Snopes or FullFact. Lastly and most importantly, the dataset contains 573 manually and more than 51k automatically labelled mappings between previously verified claims and the articles; mappings consist of two values: claim presence (i.e., whether a claim is conta
Two4Two is a library to create synthetic image data crafted for human evaluations of interpretable ML approaches (esp. image classification). The synthetic images show two abstract animals: Peaky (arms inwards) and Stretchy (arms outwards). They are similar-looking, abstract animals, made of eight blocks. The core functionality of this library is that one can correlate different parameters with an animal type to create bias in the data.
An open-source online generative dictionary that takes a word and context containing the word as input and automatically generates a definition as output. Incorporating state-of-the-art definition generation models, it supports not only Chinese and English, but also Chinese-English cross-lingual queries. Moreover, it has a user-friendly front-end design that can help users understand the query words quickly and easily.
Dataset for machine learning based performance prediction in online coding competitions.
Synthetic datasets have successfully been used to probe visual question-answering datasets for their reasoning abilities. CLEVR, for example, tests a range of visual reasoning abilities. The questions in CLEVR focus on comparisons of shapes, colors, and sizes, numerical reasoning, and existence claims. This paper introduces a minimally biased, diagnostic visual question-answering dataset, QLEVR, that goes beyond existential and numerical quantification and focus on more complex quantifiers and their combinations, e.g., asking whether there are more than two red balls that are smaller than at least three blue balls in an image. We describe how the dataset was created and present a first evaluation of state-of-the-art visual question-answering models, showing that QLEVR presents a formidable challenge to our current models.
CLUES is a benchmark for Classifier Learning Using natural language ExplanationS, consisting of a range of classification tasks over structured data along with natural language supervision in the form of explanations. CLUES consists of 36 real-world (CLUES-Real) and 144 synthetic (CLUES-Synthetic) classification tasks. It contains crowdsourced explanations describing real-world tasks from multiple teachers and programmatically generated explanations for the synthetic tasks.
Diacritized texts in Modern Hebrew, collected from eleven different sources. Diacritized using Ktiv Male conventions.
A dataset automatically generated using question generation neural models and alt-text video captions from the WebVid dataset, with 3M video-question-answer triplets.
Dynamic occupancy grids generated from NuScenes dataset. Dataset contains static environment and semantic labels, useful for long term prediction tasks.
A sentiment analysis job about the problems of each major U.S. airline. Twitter data was scraped from February of 2015 and contributors were asked to first classify positive, negative, and neutral tweets, followed by categorizing negative reasons (such as "late flight" or "rude service"). You can download the non-aggregated results (55,000 rows) here.
When arriving at each state, each observation token gets a coin toss to see whether it will appear in the output observation string. Numbers on the left are indices of observations, numbers on the right are indices of states.
The data is about 1.5 million English tweets annotated for part-of-speech using Ritter's extension of the PTB tagset. The tweets are from 2012 and 2013, tokenized using the GATE tokenizer and tagged jointly using the CMU ARK tagger and Ritter's T-POS tagger. Only when both these taggers' outputs are completely compatible over a whole tweet, is that tweet added to the dataset.
PTB-tagged English Tweets
This is a stance detection dataset in the Zulu language. The data is translated to Zulu by Zulu native speakers, from English source texts.
Automatic language identification is a challenging problem. Discriminating between closely related languages is especially difficult. This paper presents a machine-learning approach for automatic language identification for the Nordic languages, which often suffer miscategorization by existing state-of-the-art tools. Concretely we will focus on discrimination between six Nordic languages: Danish, Swedish, Norwegian (Nynorsk), Norwegian (Bokmål), Faroese, and Icelandic. This is the data for the tasks. Two variants are provided: 10K and 50K, withholding 10,000 and 50,000 examples for each language respectively.