19,997 machine learning datasets
19,997 dataset results
SI-SCORE is a synthetic dataset for the analysis of robustness to object location, rotation and size. It consists of images that vary only for factors like object size and object location.
RL Unplugged is suite of benchmarks for offline reinforcement learning. The RL Unplugged is designed around the following considerations: to facilitate ease of use, we provide the datasets with a unified API which makes it easy for the practitioner to work with all data in the suite once a general pipeline has been established. This is a dataset accompanying the paper RL Unplugged: Benchmarks for Offline Reinforcement Learning.
NorDial is the first step to creating a corpus of dialectal variation of written Norwegian. It consists of small corpus of tweets manually annotated as Bokmål, Nynorsk, any dialect, or a mix.
The data set contains 2500 manually-stance-labeled tweets, 1250 for each candidate (Joe Biden and Donald Trump). These tweets were sampled from the unlabeled set that our research team collected English tweets related to the 2020 US Presidential election. Through the Twitter Streaming API, the authors collected data using election-related hashtags and keywords. Between January 2020 and September 2020, over 5 million tweets were collected, not including quotes and retweets.
This is a discourse dataset with multiple and subjective interpretations of English conversation in the form of perceived conversation acts and intents. The dataset consists of witness testimonials in U.S. congressional hearings.
The Eedi dataset contains from two school years (September 2018 to May 2020) of students’ answers to mathematics questions from Eedi, a leading educational platform which millions of students interact with daily around the globe. Eedi offers diagnostic questions to students from primary to high school (roughly between 7 and 18 years old). Each diagnostic question is a multiple-choice question with 4 possible answer choices, exactly one of which is correct. Currently, the platform mainly focuses on mathematics questions.
The dataset consists of three files: a file with behaviour data (events.csv), a file with item properties (itemproperties.сsv) and a file, which describes category tree (categorytree.сsv). The data has been collected from a real-world ecommerce website. It is raw data, i.e. without any content transformations, however, all values are hashed due to confidential issues. The purpose of publishing is to motivate researches in the field of recommender systems with implicit feedback.
RTC is a benchmark corpus of social media comments sampled over three years. The corpus consists of 36.36m unlabelled comments for adaptation and evaluation on an upstream masked language modelling task as well as 0.9m labelled comments for finetuning and evaluation on a downstream document classification task. The Reddit Time Corpus (RTC) covers three years between March 2017 and February 2020 and is split into 36 evenly-sized monthly subsets based on comment timestamps. RTC is sampled from the Pushshift Reddit dataset.
Our trajectory dataset consists of camera-based images, LiDAR scanned point clouds, and manually annotated trajectories. It is collected under various lighting conditions and traffic densities in Beijing, China. More specifically, it contains highly complicated traffic flows mixed with vehicles, riders, and pedestrians.
BoostCLIR is a bilingual (Japanese-English) corpus of patent abstracts, extracted from the MAREC patent data, and the data from the NTCIR PatentMT workshop collections, accompanied with relevance judgements for the task of patent prior-art search.
DeCOCO is a bilingual (English-German) corpus of image descriptions, where the English part is extracted from the COCO dataset, and the German part are translations by a native German speaker.
The Large-Scale CLIR Dataset is a retrieval dataset built for Cross-Language Information Retrieval (CLIR). The dataset is derived from Wikipedia and contains more 2.8 million English single-sentence queries with relevant documents from 25 other selected languages.
SciGen is a challenge dataset for the task of reasoning-aware data-to-text generation consisting of tables from scientific articles and their corresponding descriptions. The unique properties of SciGen are that (1) tables mostly contain numerical values, and (2) the corresponding descriptions require arithmetic reasoning. SciGen is therefore the first dataset that assesses the arithmetic reasoning capabilities of generation models on complex input structures, i.e., tables from scientific articles. SciGen opens new avenues for future research in reasoning-aware text generation and evaluation.
WikiCaps is a large-scale multilingual but non-parallel data set for multimodal machine translation and retrieval. The image-caption data was extracted from Wikimedia Commons and is thus a representative of the collection of largely available non-descriptive image-caption pairs in the web. The current version of the dataset contains 3,816,940 images with 3,825,132 English captions and additional 1,000 image-caption pairs in German, French, and Russian together with their English counterparts.
Cable TV news is a data set of nearly 24/7 video, audio, and text captions from three U.S. cable TV networks (CNN, FOX, and MSNBC) from January 2010 to July 2019. Using machine learning tools, the authors detect faces in 244,038 hours of video, label each face's presented gender, identify prominent public figures, and align text captions to audio.
Event-Human3.6m is a challenging dataset for event-based human pose estimation by simulating events from the RGB Human3.6m dataset. It is built by converting the RGB recordings of Human3.6m into events and synchronising raw joints ground-truth with events frames through interpolation.
This is a Twitter dataset of 100,386 users along with up to 200 tweets from their timelines with a random-walk-based crawler on the retweet graph, with a subsample of 4,972 which is manually annotated as hateful or not through crowdsourcing. The dataset can be used to examine the difference between user activity patterns, the content disseminated between hateful and normal users, and network centrality measurements in the sampled graph.
The Rainforest Automation Energy (RAE) dataset was create to help smart grid researchers test their algorithms which make use of smart meter data. This initial release of RAE contains 1Hz data (mains and sub-meters) from two residential houses. In addition to power data, environmental and sensor data from the house's thermostat is included. Sub-meter data from one of the houses includes heat pump and rental suite captures which is of interest to power utilities.
UofTPed50 is an object detection and tracking dataset which uses GPS to ground truth the position and velocity of a pedestrian.
The NBA SportVU dataset contains player and ball trajectories for 631 games from the 2015-2016 NBA season. The raw tracking data is in the JSON format, and each moment includes information about the identities of the players on the court, the identities of the teams, the period, the game clock, and the shot clock.