Omkar2118/datasets huggingface dataset:
SPIRS

A  first-of-its-kind large dataset of sarcastic/non-sarcastic tweets with high-quality labels and extra features: (1) sarcasm perspective labels (2) new contextual features. The dataset is expected to advance sarcasm detection research. 

Source: [Reactive Supervision: A New Method for Collecting Sarcasm Data](/paper/reactive-supervision-a-new-method-for)

Introducing paper: Reactive Supervision: A New Method for Collecting Sarcasm Data
SPLASH

A dataset of utterances, incorrect SQL interpretations and the corresponding natural language feedback.

Source: [Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback](/paper/speak-to-your-parser-interactive-text-to-sql)

Introducing paper: Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback
Spoken-SQuAD

In SpokenSQuAD, the document is in spoken form, the input question is in the form of text and the answer to each question is always a span in the document. The following procedures were used to generate spoken documents from the original SQuAD dataset. First, the Google text-to-speech system was used to generate the spoken version of the articles in SQuAD. Then CMU Sphinx was sued to generate the corresponding ASR transcriptions. The SQuAD training set was used to generate the training set of Spoken SQuAD, and SQuAD development set was used to generate the testing set for Spoken SQuAD. If the answer of a question did not exist in the ASR transcriptions of the associated article, the question-answer pair was removed from the dataset because these examples are too difficult for listening comprehension machine at this stage.

Source: [https://github.com/chiahsuan156/Spoken-SQuAD](https://github.com/chiahsuan156/Spoken-SQuAD)
Image Source: [https://github.com/chiahsuan156/Spoken-SQuAD](https://github.com/chiahsuan156/Spoken-SQuAD)

Introducing paper: Spoken SQuAD: A Study of Mitigating the Impact of Speech Recognition Errors on Listening Comprehension
Spotify Podcast

A set of approximately 100K podcast episodes comprised of raw audio files along with accompanying ASR transcripts. This represents over 47,000 hours of transcribed audio, and is an order of magnitude larger than previous speech-to-text corpora. 

Source: [The Spotify Podcast Dataset](/paper/the-spotify-podcasts-dataset)

Introducing paper: The Spotify Podcast Dataset
SQuAD-es

Stanford Question Answering Dataset (SQuAD) into Spanish.

Source: [SQuAD-es](https://github.com/ccasimiro88/TranslateAlignRetrieve)
SQuAD-it

SQuAD-it is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset into Italian. It represents a large-scale dataset for open question answering processes on factoid questions in Italian. The dataset contains more than 60,000 question/answer pairs derived from the original English dataset.

Source: [SQuAD-it](https://github.com/crux82/squad-it)
SQuADShifts

Provides four new test sets for the Stanford Question Answering Dataset (SQuAD) and evaluate the ability of question-answering systems to generalize to new data. 

Source: [The Effect of Natural Distribution Shift on Question Answering Models](/paper/the-effect-of-natural-distribution-shift-on)

Introducing paper: The Effect of Natural Distribution Shift on Question Answering Models
SQUID
Full name: Stereo Quantitative Underwater Image Dataset

A dataset of images taken in different locations with varying water properties, showing color charts in the scenes. Moreover, to obtain ground truth, the 3D structure of the scene was calculated based on stereo imaging. This dataset enables a quantitative evaluation of restoration algorithms on natural images.

Source: [Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset](/paper/underwater-single-image-color-restoration)

Introducing paper: Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset
STAIR Actions Captions

A large-scale Japanese video caption dataset consisting of 79,822 videos and 399,233 captions. Each caption in the dataset describes a video in the form of "who does what and where."

Source: [Video Caption Dataset for Describing Human Actions in Japanese](/paper/video-caption-dataset-for-describing-human)

Introducing paper: Video Caption Dataset for Describing Human Actions in Japanese
Standardized Project Gutenberg Corpus

The **Standardized Project Gutenberg Corpus** (SPGC) is an open science approach to a curated version of the complete PG data containing more than 50,000 books and more than 3×109 word-tokens.

Source: [https://arxiv.org/abs/1812.08092](https://arxiv.org/abs/1812.08092)

Introducing paper: A standardized Project Gutenberg corpus for statistical analysis of natural language and quantitative linguistics
StanfordExtra

An 'in the wild' dataset of 20,580 dog images for which 2D joint and silhouette annotations were collected.

Source: [Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop](/paper/who-left-the-dogs-out-3d-animal)

Introducing paper: Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop