Omkar2118/datasets huggingface dataset:
Arabic Dataset for Commonsense Validation¬†

A benchmark Arabic dataset for commonsense understanding and validation as well as a baseline research and models trained using the same dataset. 

Source: [Is this sentence valid? An Arabic Dataset for Commonsense Validation](/paper/is-this-sentence-valid-an-arabic-dataset-for)

Introducing paper: Is this sentence valid? An Arabic Dataset for Commonsense Validation
Arabic Handwritten Digits Dataset

Contain Arabic handwritten digits images (60000 training and 10000 testing images).

Source: [Arabic Handwritten Digits Dataset](https://mloey.github.io/research/research1.html)
Arabic Text Diacritization

Extracted from the Tashkeela Corpus, the dataset consists of 55K lines containing about 2.3M words.

Source: [https://github.com/AliOsm/arabic-text-diacritization](https://github.com/AliOsm/arabic-text-diacritization)

Introducing paper: Arabic Text Diacritization Using Deep Neural Networks
ArabicWeb16

It includes 150M (150,211,934) Arabic Web pages.

Web pages in ArabicWeb16 are collected into files that conform to the WARC ISO 28500 version 0.18 standard ("WARC files").

Source: [ArabicWeb16](https://sites.google.com/view/arabicweb16/download/arabicweb16?authuser=0)
ARCD

Composed of 1,395 questions posed by crowdworkers on Wikipedia articles, and a machine translation of the Stanford Question Answering Dataset (Arabic-SQuAD). 

Source: [Neural Arabic Question Answering](/paper/neural-arabic-question-answering)

Introducing paper: Neural Arabic Question Answering
ArCOV-19

ArCOV-19 is an Arabic COVID-19 Twitter dataset that covers the period from 27th of January till 30th of April 2020. ArCOV-19 is the first publicly-available Arabic Twitter dataset covering COVID-19 pandemic that includes over 1M tweets alongside the propagation networks of the most-popular subset of them (i.e., most-retweeted and -liked).

Source: [ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks](/paper/texttt-arcov-19-the-first-arabic-covid-19)

Introducing paper: ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks
ArCOV19-Rumors

ArCOV19-Rumors is an Arabic COVID-19 Twitter dataset for misinformation detection composed of tweets containing claims from 27th January till the end of April 2020. 

Source: [ArCOV19-Rumors: Arabic COVID-19 Twitter Dataset for Misinformation Detection](/paper/arcov19-rumors-arabic-covid-19-twitter)

Introducing paper: ArCOV19-Rumors: Arabic COVID-19 Twitter Dataset for Misinformation Detection
ARDIS

This is a new image-based handwritten historical digit dataset named ARDIS (Arkiv Digital Sweden). The images in ARDIS dataset are extracted from 15.000 Swedish church records which were written by different priests with various handwriting styles in the nineteenth and twentieth centuries. The constructed dataset consists of three single digit datasets and one digit strings dataset. The digit strings dataset includes 10.000 samples in Red-Green-Blue (RGB) color space, whereas, the other datasets contain 7.600 single digit images in different color spaces.

Source: [ARDIS](https://ardisdataset.github.io/ARDIS/)
Armenian Paraphrase Detection Corpus

This dataset contains 2,360 paraphrases in Armenian that can be used for paraphrase detection. The dataset is constructed by back-translating sentences from Armenian to English twice, and manually filtering the result.

Source: [https://github.com/ivannikov-lab/arpa-paraphrase-corpus](https://github.com/ivannikov-lab/arpa-paraphrase-corpus)

Introducing paper: ARPA: Armenian Paraphrase Detection Corpus and Models
ArraMon

A dataset (in English; and also extended to Hindi) with human-written navigation and assembling instructions, and the corresponding ground truth trajectories. 

Source: [ArraMon: A Joint Navigation-Assembly Instruction Interpretation Task in Dynamic Environments](/paper/arramon-a-joint-navigation-assembly)

Introducing paper: ArraMon: A Joint Navigation-Assembly Instruction Interpretation Task in Dynamic Environments
ArSentD-LEV

The Arabic Sentiment Twitter Dataset for the Levantine dialect (ArSenTD-LEV) is a dataset of 4,000 tweets with the following annotations: the overall sentiment of the tweet, the target to which the sentiment was expressed, how the sentiment was expressed, and the topic of the tweet. 

Source: [ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets](/paper/190601830)

Introducing paper: ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets
ART Dataset
Full name: Abductive Reasoning in narrative Text

ART consists of over 20k commonsense narrative contexts and 200k explanations.

Introducing paper: Abductive Commonsense Reasoning
Arxiv Academic Paper Dataset

A dataset to enable automatic academic paper rating.

Source: [Automatic Academic Paper Rating Based on Modularized Hierarchical Convolutional Neural Network](/paper/automatic-academic-paper-rating-based-on)

Introducing paper: Automatic Academic Paper Rating Based on Modularized Hierarchical Convolutional Neural Network
arXiv Summarization Dataset

This is a dataset for evaluating summarisation methods for research papers.

Source: [A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents](/paper/a-discourse-aware-attention-model-for)

Introducing paper: A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents
ASAYAR

The first public dataset dedicated for Latin (French) and Arabic Scene Text Detection in Highway panels. It comprises more than 1800 well-annotated images. The dataset was colleted from Moroccan Highway and it has been manually annotated. ASAYAR data can be used to develop and evaluate traffic signs detection and French or Arabic text detection in different languages.

Source: [ASAYAR](https://vcar.github.io/ASAYAR/)
AskParents

**AskParents** is a dataset for advice classification extracted from Reddit. In this dataset, posts are annotated for whether they contain advice or not. It contains 8,701 samples for training, 802 for validation and 1,091 for testing.

Source: [https://github.com/venkatasg/Advice-EMNLP2020](https://github.com/venkatasg/Advice-EMNLP2020)

Introducing paper: Help! Need Advice on Identifying Advice
ASNQ
Full name: Answer Sentence Natural Questions

A large scale dataset to enable the transfer step, exploiting the Natural Questions dataset. 

Source: [TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection](/paper/tanda-transfer-and-adapt-pre-trained)

Introducing paper: TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection
ASSET Corpus

A crowdsourced multi-reference corpus where each simplification was produced by executing several rewriting transformations.

Source: [ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations](/paper/asset-a-dataset-for-tuning-and-evaluation-of)

Introducing paper: ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations
ASSIN

ASSIN (Avaliação de Similaridade Semântica e INferência textual) is a dataset with semantic similarity score and entailment annotations. It was used in a shared task in the PROPOR 2016 conference.

The full dataset has 10,000 sentence pairs, half of which in Brazilian Portuguese and half in European Portuguese. Either language variant has 2,500 pairs for training, 500 for validation and 2,000 for testing. This is different from the split used in the shared task, in which the training set had 3,000 pairs and there was no validation set. The shared task training set can be reconstructed by simply merging both sets.

Source: [ASSIN](http://nilc.icmc.usp.br/assin/)
ASSIN2

ASSIN2 (Avaliação de Similaridade Semântica e Inferência Textual) is the second edition of a workshop that evaluates Semantic Textual Similarity (STS) and Textual Entailment Recognition (RTE). It was held in conjunction with STIL 2019. Let’s break it down:

- Semantic Textual Similarity (STS): This task involves quantifying the level of semantic equivalence between two sentences. In other words, it assesses how similar or related two sentences are in terms of meaning.

- Textual Entailment Recognition (RTE): Here, the goal is to classify whether one sentence entails or logically implies another. Given a pair of sentences, the system determines if the first sentence can be inferred from the second.

Check https://link.springer.com/chapter/10.1007/978-3-030-41505-1_39
Astyx HiRes2019

A radar-centric automotive dataset based on radar, lidar and camera data for the purpose of 3D object detection.

Source: [Automotive Radar Dataset for Deep Learning Based 3D Object Detection](https://www.astyx.com/fileadmin/redakteur/dokumente/Automotive_Radar_Dataset_for_Deep_learning_Based_3D_Object_Detection.PDF)
Atlas

**Atlas** is a dataset for e-commerce clothing product categorization. The Atlas dataset consists of a high-quality product taxonomy dataset focusing on clothing products which contain 186,150 images under clothing category with 3 levels and 52 leaf nodes in the taxonomy.

Source: [https://github.com/vumaasha/atlas](https://github.com/vumaasha/atlas)
Image Source: [Umaashankaret et al](https://arxiv.org/pdf/1908.08984.pdf)

Introducing paper: Atlas: A Dataset and Benchmark for E-commerce Clothing Product Categorization
ATRW
Full name: Amur Tiger Re-identification in the Wild

The **ATRW Dataset** contains over 8,000 video clips from 92 Amur tigers, with bounding box, pose keypoint, and tiger identity annotations. 

Source: [ATRW: A Benchmark for Amur Tiger Re-identification in the Wild](/paper/amur-tiger-re-identification-in-the-wild)

Introducing paper: ATRW: A Benchmark for Amur Tiger Re-identification in the Wild
AU-AIR

The **AU-AIR** is a multi-modal aerial dataset captured by a UAV. Having visual data, object annotations, and flight data (time, GPS, altitude, IMU sensor data, velocities), AU-AIR meets vision and robotics for UAVs.

Source: [https://github.com/bozcani/auairdataset](https://github.com/bozcani/auairdataset)
Image Source: [https://github.com/bozcani/auairdataset](https://github.com/bozcani/auairdataset)

Introducing paper: Integration of the 3D Environment for UAV Onboard Visual Object Tracking
Automatic Keyphrase Extraction Dataset

Dataset for automatic keyphrase extraction task.

Source: [Automatic Keyphrase Extraction Dataset](https://github.com/snkim/AutomaticKeyphraseExtraction)
Automating Dynamic Consent

This dataset is used to evaluate a predictive consent model for users’ information shared in social media. In this task, the goal is to predict whether the users will give their consent to share that data with different hypothetical audiences within a medical context. The dataset is built from information the users posted on Facebook and their consent answers about each piece of information.

Source: [https://github.com/cnorval/automating-dynamic-consent-dataset](https://github.com/cnorval/automating-dynamic-consent-dataset)

Introducing paper: Automating dynamic consent decisions for the processing of social media data in health research
AuxAD

**AuxAD** is a a distantly supervised dataset for acronym disambiguation.

Source: [https://github.com/PrimerAI/sdu-data](https://github.com/PrimerAI/sdu-data)

Introducing paper: Primer AI's Systems for Acronym Identification and Disambiguation
AVA-ActiveSpeaker

Contains temporally labeled face tracks in video, where each face instance is labeled as speaking or not, and whether the speech is audible. This dataset contains about 3.65 million human labeled frames or about 38.5 hours of face tracks, and the corresponding audio. 

Source: [AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection](/paper/ava-activespeaker-an-audio-visual-dataset-for)

Introducing paper: AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection
AVA-LAEO

Dataset to address the problem of detecting people Looking At Each Other (LAEO) in video sequences.

Source: [LAEO-Net: revisiting people Looking At Each Other in videos](/paper/laeo-net-revisiting-people-looking-at-each-1)

Introducing paper: LAEO-Net: revisiting people Looking At Each Other in videos
AVA-Speech

Contains densely labeled speech activity in YouTube videos, with the goal of creating a shared, available dataset for this task. 

Source: [AVA-Speech: A Densely Labeled Dataset of Speech Activity in Movies](/paper/ava-speech-a-densely-labeled-dataset-of)

Introducing paper: AVA-Speech: A Densely Labeled Dataset of Speech Activity in Movies
AVE
Full name: Audio-Visual Event Localization

To investigate three temporal localization tasks: supervised and weakly-supervised audio-visual event localization, and cross-modality localization.

Source: [Audio-Visual Event Localization in Unconstrained Videos](/paper/audio-visual-event-localization-in)

Introducing paper: Audio-Visual Event Localization in Unconstrained Videos
BanFakeNews

An annotated dataset of ~50K news that can be used for building automated fake news detection systems for a low resource language like Bangla. 

Source: [BanFakeNews: A Dataset for Detecting Fake News in Bangla](/paper/banfakenews-a-dataset-for-detecting-fake-news)

Introducing paper: BanFakeNews: A Dataset for Detecting Fake News in Bangla
BanglaLekha-Isolated

This dataset contains Bangla handwritten numerals, basic characters and compound characters. This dataset was collected from multiple geographical location within Bangladesh and includes sample collected from a variety of aged groups. This dataset can also be used for other classification problems i.e: gender, age, district. 

Source: [BanglaLekha-Isolated: A Comprehensive Bangla Handwritten Character Dataset](/paper/banglalekha-isolated-a-comprehensive-bangla)

Introducing paper: BanglaLekha-Isolated: A Comprehensive Bangla Handwritten Character Dataset
BanglaWriting

The **BanglaWriting** dataset contains single-page handwritings of 260 individuals of different personalities and ages. Each page includes bounding-boxes that bounds each word, along with the unicode representation of the writing. This dataset contains 21,234 words and 32,787 characters in total. Moreover, this dataset includes 5,470 unique words of Bangla vocabulary. Apart from the usual words, the dataset comprises 261 comprehensible overwriting and 450 incomprehensible overwriting. All of the bounding boxes and word labels are manually-generated. The dataset can be used for complex optical character/word recognition, writer identification, and handwritten word segmentation. Furthermore, this dataset is suitable for extracting age-based and gender-based variation of handwriting.

Source: [https://github.com/QuwsarOhi/BanglaWriting](https://github.com/QuwsarOhi/BanglaWriting)
Image Source: [https://github.com/QuwsarOhi/BanglaWriting](https://github.com/QuwsarOhi/BanglaWriting)

Introducing paper: BanglaWriting: A multi-purpose offline Bangla handwriting dataset
BAR
Full name: Biased Action Recognition

**Biased Action Recognition** (**BAR**) dataset is a real-world image dataset categorized as six action classes which are biased to distinct places. The authors settle these six action classes by inspecting imSitu, which provides still action images from Google Image Search with action and place labels. In detail, the authors choose action classes where images for each of these candidate actions share common place characteristics. At the same time, the place characteristics of action class candidates should be distinct in order to classify the action only from place attributes. The select pairs are six typical action-place pairs: (Climbing, RockWall), (Diving, Underwater), (Fishing, WaterSurface), (Racing, APavedTrack), (Throwing, PlayingField),and (Vaulting, Sky).

Source: [https://github.com/alinlab/BAR](https://github.com/alinlab/BAR)
Image Source: [https://github.com/alinlab/BAR](https://github.com/alinlab/BAR)

Introducing paper: Learning from Failure: Training Debiased Classifier from Biased Classifier
BASIL

300 news articles annotated with 1,727 bias spans and find evidence that informational bias appears in news articles more frequently than lexical bias.

Source: [In Plain Sight: Media Bias Through the Lens of Factual Reporting](/paper/in-plain-sight-media-bias-through-the-lens-of)

Introducing paper: In Plain Sight: Media Bias Through the Lens of Factual Reporting
BBDB

A new large-scale baseball video dataset which is produced semi-automatically by using play-by-play texts available online. The BBDB contains 4200 hours of baseball game videos with 400k temporally annotated activity segments.

Source: [BBDB](https://sites.google.com/site/eccv2018bbdb/)
BCWS
Full name: Bilingual Contextual Word Similarity

Dataset for evaluating English-Chinese Bilingual Contextual Word Similarity. The dataset consists of 2,091 English-Chinese word pairs with the corresponding sentential contexts and their similarity scores annotated by the human. 

Source: [BCWS: Bilingual Contextual Word Similarity](/paper/bcws-bilingual-contextual-word-similarity)

Introducing paper: BCWS: Bilingual Contextual Word Similarity
BD-4SK-ASR
Full name: Basic Dataset for Sorani Kurdish Automatic Speech Recognition

The **Basic Dataset for Sorani Kurdish Automatic Speech Recognition** (**BD-4SK-ASR**) is a dataset for automatic speech recognition for Sorani Kurdish.

Source: [https://arxiv.org/abs/1911.13087](https://arxiv.org/abs/1911.13087)

Introducing paper: Kurdish (Sorani) Speech to Text: Presenting an Experimental Dataset
BdSLImset
Full name: Bangladeshi Sign Language Image Dataset

Bangladeshi Sign Language Image Dataset (BdSLImset) is a dataset that contains images of different Bangladeshi sign letters.

Source: [https://github.com/imruljubair/BdSLImset](https://github.com/imruljubair/BdSLImset)
Image Source: [https://arxiv.org/pdf/1811.12813v1.pdf](https://arxiv.org/pdf/1811.12813v1.pdf)

Introducing paper: Real Time Bangladeshi Sign Language Detection using Faster R-CNN
Bengali Hate Speech

Introduces three datasets of expressing hate, commonly used topics, and opinions for hate speech detection, document classification, and sentiment analysis, respectively. 

Source: [Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network](/paper/classification-benchmarks-for-under-resourced)

Introducing paper: Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network
Berkeley DeepDrive Video

A dataset comprised of real driving videos and GPS/IMU data. The BDDV dataset contains diverse driving scenarios including cities, highways, towns, and rural areas in several major cities in US.

Source: [End-to-end Learning of Driving Models from Large-scale Video Datasets](/paper/end-to-end-learning-of-driving-models-from)

Introducing paper: End-to-end Learning of Driving Models from Large-scale Video Datasets
Bianet

Bianet is a parallel news corpus in Turkish, Kurdish and English
It contains 3,214 Turkish articles with their sentence-aligned Kurdish or English translations from the Bianet online newspaper.

Source: [Bianet: A Parallel News Corpus in Turkish, Kurdish and English](/paper/bianet-a-parallel-news-corpus-in-turkish)

Introducing paper: Bianet: A Parallel News Corpus in Turkish, Kurdish and English