Omkar2118/datasets huggingface dataset:
HUME-VB
Full name: The Hume Vocal Bursts Dataset

The Hume Vocal Burst Database (H-VB) includes all train, validation, and test recordings and corresponding emotion ratings for the train and validation recordings.

This dataset contains 59,201 audio recordings of vocal bursts from 1,702 speakers, from 4 cultures—the U.S, South Africa, China, and Venezuela—ranging in age from 20 to 39.5 years old. The duration of data in this version of H-VB is 36 Hours  (Mean: 02.23  sec). The emotion ratings correspond to ten emotion concepts, listed below, and averaged 0-100 intensities for each emotion concept, with each sample having been rated by an average of 85.2 raters.

Emotion Labels:  Awe, Excitement, Amusement, Awkwardness, Fear, Horror, Distress, Triumph, Sadness, Surprise.
MIMIC II
Full name: Multi-parameter Intelligent Monitoring for Critical Care  II database

The data used in this research is a subset of the Multi-parameter Intelligent Monitoring for Critical
Care (MIMIC) II database. It contains minute-by-minute time series of Heart Rate (HR), Systolic
Blood Pressure (SBP), Diastolic Blood Pressure (DBP), and Mean Arterial blood Pressure (MAP)
arranged into records, each of which corresponds to an adult patient’s ICU stay.

Source: [Vitor Cerqueira, Luis Torgo, and Carlos Soares. Early anomaly detection in time series: a hierarchical
approach for predicting critical health episodes. arXiv preprint arXiv:2010.11595, 2020.] (https://github.com/vcerqueira/layered_
learning_time_series/tree/master/data_sample)
WebLI
Full name: Web Language Image

**WebLI** (Web Language Image) is a web-scale multilingual image-text dataset, designed to support Google’s vision-language research, such as the large-scale pre-training for image understanding, image captioning, visual question answering, object detection etc.

The dataset is built from the public web, including image bytes, image-associated texts (alt-text, OCR, page title), 109 languages and many other features. The dataset is deduplicated on 68 common vision/vision-language tasks, and has no user or personally identifiable data with careful RAI considerations.

source: [PaLI: A Jointly-Scaled Multilingual Language-Image Model](https://arxiv.org/abs/2209.06794)
ArNLI

Natural Language Inference processes pairs of sentences to extract their semantic relations.
Pair sentences are annotated with three classes (Contradictions, Entailment, Neutral). 

1- Entailment: A and B intersect (A ∩ B ≠ ∅, A ∪ B ≠ Universe) or one may contain another ((A ∁ B) OR (B ∁ A)).
2- Contradiction: A, and B cannot be together, if A is True then B is False (A ∩ B = ∅, and A ∪ B = Universe). 
3- Neutral: A, and B have no semantic relations. Each one of them is a different set in the universe. No intersection and their union is a subset of the universe, not all universe (A ∩ B = ∅ and A ∪ B ≠Universe)

Introducing paper: ArNLI: Arabic Natural Language Inference for Entailment and Contradiction Detection
Artie Bias Corpus

**Artie Bias Corpus** is an open dataset for detecting demographic bias in speech applications.

Introducing paper: Artie Bias Corpus: An Open Dataset for Detecting Demographic Bias in Speech Applications
EHR Dataset for Patient Treatment Classification

The dataset is Electronic Health Record Predicting collected from a private Hospital in Indonesia. It contains the patients laboratory test results used to determine next patient treatment whether in care or out care patient. The task embedded to the dataset is classification prediction.
Code2Seq (Java)

Java-Small, Java-Med, Java-Large

Introducing paper: code2seq: Generating Sequences from Structured Representations of Code
Sig53
Full name: Sig53

A dataset of 53 complex-valued signal modulation classes.

Introducing paper: Large Scale Radio Frequency Signal Classification