Omkar2118/datasets huggingface dataset:
OASum

**OASum** is a large-scale open-domain aspect-based summarization dataset which contains more than 3.7 million instances with around 1 million different aspects on 2 million Wikipedia pages.

Source: [OASum: Large-Scale Open Domain Aspect-based Summarization](https://arxiv.org/pdf/2212.09233v1.pdf)

Image Source: [https://arxiv.org/pdf/2212.09233v1.pdf](https://arxiv.org/pdf/2212.09233v1.pdf)

Introducing paper: OASum: Large-Scale Open Domain Aspect-based Summarization
SimpEvalASSET

**SimpEvalASSET** is a dataset for learning learnable metrics using modern language models. It comprises of 12K human ratings on 2.4K simplifications of 24 systems, and SIMPEVAL_2022, a challenging simplification benchmark consisting of over 1K human ratings of 360 simplifications including generations from GPT-3.5.

Source: [LENS: A Learnable Evaluation Metric for Text Simplification](https://arxiv.org/pdf/2212.09739v1.pdf)

Image Source: [https://arxiv.org/pdf/2212.09739v1.pdf](https://arxiv.org/pdf/2212.09739v1.pdf)

Introducing paper: LENS: A Learnable Evaluation Metric for Text Simplification
ChartQA

Charts are very popular for analyzing data. When exploring charts, people often ask a variety of complex reasoning questions that involve several logical and arithmetic operations. They also commonly refer to visual features of a chart in their questions. However, most existing datasets do not focus on such complex reasoning questions as their questions are template-based and answers come from a fixed-vocabulary. In this work, we present a large-scale benchmark covering 9.6K human-written questions as well as 23.1K questions generated from human-written chart summaries. To address the unique challenges in our benchmark involving visual and logical reasoning over charts, we present two transformer-based models that combine visual features and the data table of the chart in a unified way to answer questions. While our models achieve the state-of-the-art results on the previous datasets as well as on our benchmark, the evaluation also reveals several challenges in answering complex reasoning questions.

Introducing paper: ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning
FedTADBench

**FedTADBench** is a federated time series anomaly detection benchmark. It covers 5 time series anomaly detection algorithms, 4 federated learning frameworks, and 3 time series anomaly detection datasets.

Source: [FedTADBench: Federated Time-Series Anomaly Detection Benchmark (https://arxiv.org/pdf/2212.09518v1.pdf)

Introducing paper: FedTADBench: Federated Time-Series Anomaly Detection Benchmark
CAP-DATA

**CAP-DATA** is a large-scale benchmark consisting of 11,727 in-the-wild accident videos with over 2.19 million frames together with labeled fact-effect-reason-introspection description and temporal accident frame label. It can support many useful tasks for accident inference, such as accident detection and prediction (AccidentDet/Pre), causal inference of accident (Accident-Causal), accident classification (Accident-Cla), text-video based accident retrieval (Accident-Retri), and question answering in an accident (Accident-QA) of the driving scene.

Source: [Cognitive Accident Prediction in Driving Scenes: A Multimodality Benchmark](https://arxiv.org/pdf/2212.09381v1.pdf)

Image Source: [https://arxiv.org/pdf/2212.09381v1.pdf](https://arxiv.org/pdf/2212.09381v1.pdf)

Introducing paper: Cognitive Accident Prediction in Driving Scenes: A Multimodality Benchmark
JEMMA

**JEMMA** is an Extensible Java Dataset for ML4Code Applications, which is a large-scale dataset targeted at ML4 code. JEMMA comes with a considerable amount of pre-processed information such as metadata, representations (e.g., code tokens, ASTs, graphs), and several properties (e.g., metrics, static analysis results) for 50,000 Java projects from the 50KC dataset, with over 1.2 million classes and over 8 million methods.

Source: [JEMMA: An Extensible Java Dataset for ML4Code Applications](https://arxiv.org/pdf/2212.09132v1.pdf)

Image Source: [https://arxiv.org/pdf/2212.09132v1.pdf](https://arxiv.org/pdf/2212.09132v1.pdf)

Introducing paper: JEMMA: An Extensible Java Dataset for ML4Code Applications
MPV
Full name: Multi-Pose Virtual try on

Consists of 37,723/14,360 person/clothes images, with the resolution of 256x192. Each person has different poses. We split them into the train/test set 52,236/10,544 three-tuples, respectively. You can download the dataset at [MPV(Google Drive)](https://drive.google.com/file/d/1Vmc-n8I4jh3wppSbaisoeiZDtoWEUY27/view?usp=sharing)
SPARF

**SPARF** is a large-scale ShapeNet-based synthetic dataset for novel view synthesis consisting of ~17 million images rendered from nearly 40,000 shapes at high resolution (400×400 pixels).

Source: [SPARF: Large-Scale Learning of 3D Sparse Radiance Fields from Few Input Images](https://arxiv.org/pdf/2212.09100v1.pdf)

Image Source: [https://github.com/ajhamdi/sparf_pytorch](https://github.com/ajhamdi/sparf_pytorch)

Introducing paper: SPARF: Large-Scale Learning of 3D Sparse Radiance Fields from Few Input Images
Cards Against Humanity

A dataset of games played in the card game "Cards Against Humanity" (CAH), by human players, derived from the online CAH labs. 
Each round includes the cards presented to users - a "black" prompt with a blank or question and 10 "white" punchlines as possible responses, and which punchline was picked by a player each round, along with text and metadata.

An example prompt is “TSA guidelines now prohibit ___ on airplanes”. Candidate punchlines are “Goblins”, “BATMAN!!!”, “Poor people”, and “The
right amount of cocaine”. Importantly, many cards are offensive or politically incorrect.

Used to explore human humor preferences.

Available upon request from CAH labs: mail@cardsagainsthumanity.com
Train/test splits and data processing available in the paper/code: "Cards Against AI: Predicting Humor in a Fill-in-the-blank Party Game": https://github.com/ddofer/CAH

Introducing paper: Cards Against AI: Predicting Humor in a Fill-in-the-blank Party Game