Omkar2118/datasets huggingface dataset:
ALGAD
Full name: Andy Lomas Generative Art Dataset

Repository of a generative art dataset by computer artist Andy Lomas.

Source: [https://github.com/SensiLab/Andy-Lomas-Generative-Art-Dataset](https://github.com/SensiLab/Andy-Lomas-Generative-Art-Dataset)
Image Source: [https://github.com/SensiLab/Andy-Lomas-Generative-Art-Dataset](https://github.com/SensiLab/Andy-Lomas-Generative-Art-Dataset)

Introducing paper: Deep Learning of Individual Aesthetics
Allegro Reviews

A comprehensive multi-task benchmark for the Polish language understanding, accompanied by an online leaderboard. It consists of a diverse set of tasks, adopted from existing datasets for named entity recognition, question-answering, textual entailment, and others. 

Source: [KLEJ: Comprehensive Benchmark for Polish Language Understanding](/paper/klej-comprehensive-benchmark-for-polish)

Introducing paper: KLEJ: Comprehensive Benchmark for Polish Language Understanding
AlloCine

A new dataset for sentiment analysis, scraped from Allociné.fr user reviews. It contains 100k positive and 100k negative reviews divided into 3 balanced splits: train (160k reviews), val (20k) and test (20k). 

Source: [AlloCine](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert)
ALT
Full name: Asian Language Treebank

The ALT project aims to advance the state-of-the-art Asian natural language processing (NLP) techniques through the open collaboration for developing and using ALT. It was first conducted by NICT and UCSY as described in Ye Kyaw Thu, Win Pa Pa, Masao Utiyama, Andrew Finch and Eiichiro Sumita (2016). Then, it was developed under ASEAN IVO as described in this Web page. The process of building ALT began with sampling about 20,000 sentences from English Wikinews, and then these sentences were translated into the other languages. ALT now has 13 languages: Bengali, English, Filipino, Hindi, Bahasa Indonesia, Japanese, Khmer, Lao, Malay, Myanmar (Burmese), Thai, Vietnamese, Chinese (Simplified Chinese).

Source: [ALT](https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/)
Amazon Product Data
Full name: rithik

This dataset contains product reviews and metadata from Amazon, including 142.8 million reviews spanning May 1996 - July 2014.

This dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs).

Introducing paper: Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering
AmbigQA

Is a new open-domain question answering task which involves predicting a set of question-answer pairs, where every plausible answer is paired with a disambiguated rewrite of the original question. A dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark.

Source: [AmbigQA](https://nlp.cs.washington.edu/ambigqa/)
AML Robot Cutting Dataset

The AML Robot Cutting Dataset consists of approximately 1500 seconds of real data collected on Kinova Jaco 2 robot retrofitted with a custom end-effector fixture and dremel performing cutting tasks on wood specimens for 5 materials and 5 thicknesses.

Source: [AML Robot Cutting Dataset](https://github.com/sahandrez/aml_robot_cutting_dataset)

Introducing paper: Learning the Latent Space of Robot Dynamics for Cutting Interaction Inference
ANETAC
Full name: Arabic Named Entity Transliteration and Classification

An English-Arabic named entity transliteration and classification dataset built from freely available parallel translation corpora. The dataset contains 79,924 instances, each instance is a triplet (e, a, c), where e is the English named entity, a is its Arabic transliteration and c is its class that can be either a Person, a Location, or an Organization. The ANETAC dataset is mainly aimed for the researchers that are working on Arabic named entity transliteration, but it can also be used for named entity classification purposes.

Source: [ANETAC: Arabic Named Entity Transliteration and Classification Dataset](/paper/anetac-arabic-named-entity-transliteration)

Introducing paper: ANETAC: Arabic Named Entity Transliteration and Classification Dataset
Animal-Pose Dataset

**Animal-Pose Dataset** is an animal pose dataset to facilitate training and evaluation. This dataset provides animal pose annotations on five categories are provided: dog, cat, cow, horse, sheep, with in total 6,000+ instances in 4,000+ images. Besides, the dataset also contains bounding box annotations for other 7 animal categories.

Source: [Cross-Domain Adaptation for Animal Pose Estimation](/paper/cross-domain-adaptation-for-animal-pose)
Image Source: [https://sites.google.com/view/animal-pose/](https://sites.google.com/view/animal-pose/)

Introducing paper: Cross-Domain Adaptation for Animal Pose Estimation
AnimalWeb

A large-scale, hierarchical annotated dataset of animal faces, featuring 21.9K faces from 334 diverse species and 21 animal orders across biological taxonomy. These faces are captured `in-the-wild' conditions and are consistently annotated with 9 landmarks on key facial features. The proposed dataset is structured and scalable by design; its development underwent four systematic stages involving rigorous, manual annotation effort of over 6K man-hours.

Source: [AnimalWeb: A Large-Scale Hierarchical Dataset of Annotated Animal Faces](/paper/animalweb-a-large-scale-hierarchical-dataset)

Introducing paper: AnimalWeb: A Large-Scale Hierarchical Dataset of Annotated Animal Faces
ANTIQUE

ANTIQUE is a collection of 2,626 open-domain non-factoid questions from a diverse set of categories. The dataset  contains 34,011 manual relevance annotations. The questions were asked by real users in a community question answering service, i.e., Yahoo! Answers. Relevance judgments for all the answers to each question were collected through crowdsourcing.

Introducing paper: ANTIQUE: A Non-Factoid Question Answering Benchmark
AO-CLEVr

**AO-CLEVr** is a new synthetic-images dataset containing images of "easy" Attribute-Object categories, based on the CLEVr. AO-CLEVr has attribute-object pairs created from 8 attributes: { red, purple, yellow, blue, green, cyan, gray, brown } and 3 object shapes {sphere, cube, cylinder}, yielding 24 attribute-object pairs. Each pair consists of 7500 images. Each image has a single object that consists of the attribute-object pair. The object is randomly assigned one of two sizes (small/large), one of two materials (rubber/metallic), a random position, and random lightning according to CLEVr defaults.

Source: [https://github.com/nv-research-israel/causal_comp](https://github.com/nv-research-israel/causal_comp)
Image Source: [https://github.com/nv-research-israel/causal_comp](https://github.com/nv-research-israel/causal_comp)

Introducing paper: A causal view of compositional zero-shot recognition
ApartmenTour

Contains a large number of online videos and subtitles. 

Source: [Watch and Learn: Mapping Language and Noisy Real-world Videos with Self-supervision](/paper/watch-and-learn-mapping-language-and-noisy)

Introducing paper: Watch and Learn: Mapping Language and Noisy Real-world Videos with Self-supervision
APE
Full name: Automatic Post-Editing

APE is useful to evaluate Machine Translation automatic post-editing (**APE**), which is the task of improving the output of a blackbox MT system by automatically fixing its mistakes. The act of post-editing text can be fully specified as a sequence of delete and insert actions in given positions.

Source: [https://github.com/antoniogois/keystrokes_ape](https://github.com/antoniogois/keystrokes_ape)
Image Source: [Gois et al](https://arxiv.org/pdf/2004.14120v1.pdf)

Introducing paper: Learning Non-Monotonic Automatic Post-Editing of Translations from Human Orderings
APRICOT

APRICOT is a collection of over 1,000 annotated photographs of printed adversarial patches in public locations. The patches target several object categories for three COCO-trained detection models, and the photos represent natural variation in position, distance, lighting conditions, and viewing angle. 

Source: [APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection](/paper/apricot-a-dataset-of-physical-adversarial)
Image Source: [https://apricot.mitre.org/](https://apricot.mitre.org/)

Introducing paper: APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection
APT-Malware

The APT Malware dataset is used to train classifiers to predict if a given malware belongs to the “Advanced Persistent Threat” (APT) type or not. It contains 3131 samples spread over 24 different unique malware classes.

Source: [https://arxiv.org/pdf/1810.07321.pdf](https://arxiv.org/pdf/1810.07321.pdf)

Introducing paper: Malware triage for early identification of Advanced Persistent Threat activities
AQUA

The question-answer (QA) pairs are automatically generated using state-of-the-art question generation methods based on paintings and comments provided in an existing art understanding dataset. The QA pairs are cleansed by crowdsourcing workers with respect to their grammatical correctness, answerability, and answers' correctness. The dataset inherently consists of visual (painting-based) and knowledge (comment-based) questions. 

Source: [A Dataset and Baselines for Visual Question Answering on Art](/paper/a-dataset-and-baselines-for-visual-question)

Introducing paper: A Dataset and Baselines for Visual Question Answering on Art
Aqualoc

A new underwater dataset that has been recorded in an harbor and provides several sequences with synchronized measurements from a monocular camera, a MEMS-IMU and a pressure sensor.

Source: [The Aqualoc Dataset: Towards Real-Time Underwater Localization from a Visual-Inertial-Pressure Acquisition System](/paper/the-aqualoc-dataset-towards-real-time)

Introducing paper: The Aqualoc Dataset: Towards Real-Time Underwater Localization from a Visual-Inertial-Pressure Acquisition System
aquamuse

5,519 query-based summaries, each associated with an average of 6 input documents selected from an index of 355M documents from Common Crawl.

Source: [AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization](/paper/aquamuse-automatically-generating-datasets)

Introducing paper: AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization