19,997 machine learning datasets
19,997 dataset results
WikiSRS is a novel dataset of similarity and relatedness judgments of paired Wikipedia entities (people, places, and organizations), as assigned by Amazon Mechanical Turk workers.
The XL-R2R dataset is built upon the R2R dataset and extends it with Chinese instructions. XL-R2R preserves the same splits as in R2R and thus consists of train, val-seen, and val-unseen splits with both English and Chinese instructions, and test split with English instructions only.
YASO is a crowd-sourced TSA evaluation dataset, collected using a new annotation scheme for labeling targets and their sentiments. The dataset contains 2,215 English sentences from movie, business and product reviews, and 7,415 terms and their corresponding sentiments annotated within these sentences.
This dataset contains 94 movie summary videos from various YouTube channels.
The Wikidata-Disamb dataset is intended to allow a clean and scalable evaluation of NED with Wikidata entries, and to be used as a reference in future research.
Visual Beliefs is a dataset of abstract scenes to study visual beliefs. The dataset consists of 8-frame scenes, and in each scene a person has a mistaken belief. The dataset can be used for two tasks: predicting who is mistaken and predicting when are they mistaken.
Classifiers are function words that are used to express quantities in Chinese and are especially difficult for language learners. This dataset of Chinese Classifiers can be used to predict Chinese classifiers from context. The dataset contains a large collection of example sentences for Chinese classifier usage derived from three language corpora (Lancaster Corpus of Mandarin Chinese, UCLA Corpus of Written Chinese and Leiden Weibo Corpus). The data was cleaned and processed for a context-based classifier prediction task.
A new benchmark dataset for simple question answering over knowledge graphs that was created by mapping SimpleQuestions entities and predicates from Freebase to DBpedia.
The QTUNA dataset is the result of a series of elicitation experiments in which human speakers were asked to perform a linguistic task that invites the use of quantified expressions in order to inform possible Natural Language Generation algorithms that mimic humans' use of quantified expressions.
Used to investigate common crowdsourced paraphrasing issues and for detecting the quality issues.
The Metaphorical Connections dataset is a poetry dataset that contains annotations between metaphorical prompts and short poems. Each poem is annotated whether or not it successfully communicates the idea of the metaphorical prompt.
PICO is a framework to formulate a well-defined focused clinical question. This framework identifies the sentences in a given medical text that belong to the four components: Participants/Problem (P), Intervention (I), Comparison (C) and Outcome (O). The PubMed PICO Element Detection dataset is a dataset for evaluating models that automatically detect PICO elements.
The JNC data provides common supervision data for headline generation.
A curated and 3-D pose-annotated subset of RGB videos sourced from Kinetics-700, a large-scale action dataset.
FarsBase-KBP contains 22015 sentences, in which the entities and relation types are linked to the FarsBase ontology. This gold dataset can be reused for benchmarking KBP systems in the Persian language.
Almawave-SLU is the first Italian dataset for Spoken Language Understanding (SLU). It is derived through a semi-automatic procedure and is used as a benchmark of various open source and commercial systems.
The Flickr 8k Audio Caption Corpus contains 40,000 spoken captions of 8,000 natural images. It was collected in 2015 to investigate multimodal learning schemes for unsupervised speech pattern discovery. For a description of the corpus, see:
Minecraft House is a crowd sourced dataset that collects examples of humans building houses in Minecraft. Each user is asked to build a CraftAssist: A Framework for Dialogue-enabled Interactive Agents house on a fixed time budget (30 minutes), without any additional guidance or instructions. Every action of the user is recorded using the Cuberite server.
RSOC is a large-scale object counting dataset with remote sensing images, which contains four important geographic objects: buildings, crowded ships in harbors, large-vehicles and small-vehicles in parking lots.
ObjectNet3D is a large scale database for 3D object recognition, named, that consists of 100 categories, 90,127 images, 201,888 objects in these images and 44,147 3D shapes. Objects in the images in the database are aligned with the 3D shapes, and the alignment provides both accurate 3D pose annotation and the closest 3D shape annotation for each 2D object. Consequently, the database is useful for recognizing the 3D pose and 3D shape of objects from 2D images. Authors also provide baseline experiments on four tasks: region proposal generation, 2D object detection, joint 2D detection and 3D object pose estimation, and image-based 3D shape retrieval, which can serve as baselines for future research.