Omkar2118/datasets huggingface dataset:
Snips-SmartLights

The SmartLights benchmark from Snipstests the capability of controlling lights in different rooms.
It consists of 1660 requests which are split into five partitions for a 5-fold evaluation.
 A sample command could be: “please change the [bedroom] lights to [red]” or “i’d like the [living room] lights to be at [twelve] percent”

Introducing paper: Spoken Language Understanding on the Edge
Snips-SmartSpeaker

The SmartSpeaker benchmark tests the performance of reacting to music player commands in English as well as in French.
It has the difficulty of containing many artist or music tracks with uncommon names in the commands, like
“play music by [a boogie wit da hoodie]” or “I’d like to listen to [Kinokoteikoku]”.

Introducing paper: Spoken Language Understanding on the Edge
SVRT
Full name: Synthetic Visual Reasoning Task

The Synthetic Visual Reasoning Test (SVRT) is a series of 23 classification problems involving images of randomly generated shapes.

https://www.pnas.org/doi/10.1073/pnas.1109168108

Comparing machines and humans on a visual categorization test
AOM-CTC
Full name: AOM-CTC

This is the Current Video sequence set from the AOM-CTC.
DaNewsroom
Full name: DaNewsroom: A Large-scale Danish Summarisation Dataset

The first large-scale non-English language dataset specifically curated for automatic summarisation. The document-summary pairs are news articles and manually written summaries in the Danish language.

Introducing paper: DaNewsroom: A Large-scale Danish Summarisation Dataset
Mechanical MNIST – Distribution Shift

The Mechanical MNIST – Distribution Shift dataset contains the results of finite element simulation of heterogeneous material subject to large deformation due to equibiaxial extension at a fixed boundary displacement of d = 7.0. The result provided in this dataset is the change in strain energy after this equibiaxial extension. The Mechanical MNIST dataset is generated by converting the MNIST bitmap images (28x28 pixels) with range 0 - 255 to 2D heterogeneous blocks of material (28x28 unit square) with varying modulus in range 1- s. The original bitmap images are sourced from the MNIST Digits dataset, (http://www.pymvpa.org/datadb/mnist.html) which corresponds to Mechanical MNIST – MNIST, and the EMNIST Letters dataset (https://www.nist.gov/itl/products-and-services/emnist-dataset) which correspond to Mechanical MNIST – EMNIST Letters. The Mechanical MNIST – Distribution Shift dataset is specifically designed to demonstrate three types of data distribution shift: (1) covariate shift, (2) mechanism shift, and (3) sampling bias, for all of which the training and testing environments are drawn from different distributions. For each type of data distribution shift, we have one dataset generated from the Mechanical MNIST bitmaps and one from the Mechanical MNIST – EMNIST Letters bitmaps. For the covariate shift dataset, the training dataset is collected from two environments (2500 samples from s = 100, and 2500 samples from s = 90), and the test data is collected from two additional environments (2000 samples from s = 75, and 2000 samples from s = 50). For the mechanism shift dataset, the training data is identical to the training data in the covariate shift dataset (i.e., 2500 samples from s = 100, and 2500 samples from s = 90), and the test datasets are from two additional environments (2000 samples from s = 25, and 2000 samples from s = 10).  For the sampling bias dataset, datasets are collected such that each datapoint is selected from the broader MNIST and EMNIST inputs bitmap selection by a probability which is controlled by a parameter r. The training data is collected from two environments (9800 from r = 15, and 200 from r = -2), and the test data is collected from three different environments (2000 from r = -5, 2000 from r = -10, and 2000 from r = 1).  Thus, in the end we have 6 benchmark datasets with multiple training and testing environments in each. The enclosed document “folder_description.pdf'” shows the organization of each zipped folder provided on this page. The code to reproduce these simulations is available on GitHub (https://github.com/elejeune11/Mechanical-MNIST/blob/master/generate_dataset/Equibiaxial_Extension_FEA_test_FEniCS.py).
Astock

(1) provide financial news for each specific stock. (2) provide various stock technical factors and fundamental factors for each stock.
VNDS
Full name: VNDS: A Vietnamese Dataset for Summarization

A single-document Vietnamese summarization dataset
Mapping Topics in 100,000 Real-Life Moral Dilemmas
Full name: Tuan Dung nguyen

This dataset accompanies the ICWSM 2022 paper "Mapping Topics in 100,000 Real-Life Moral Dilemmas".

Introducing paper: Mapping Topics in 100,000 Real-life Moral Dilemmas
ScribbleKITTI

ScribbleKITTI is a scribble-annotated dataset for LiDAR semantic segmentation.

Introducing paper: Scribble-Supervised LiDAR Semantic Segmentation