Omkar2118/datasets huggingface dataset:
PMPC
Full name: Persona Match on Persona-Chat

**PMPC** (Persona Match on Persona-Chat) is a dataset for Speaker Persona Detection (SPD) which aims to detect speaker personas based on the plain conversational text.

Introducing paper: Detecting Speaker Personas from Conversational Texts
MultiEURLEX

**MultiEURLEX** is a multilingual dataset for topic classification of legal documents. The dataset comprises 65k European Union (EU) laws, officially translated in 23 languages, annotated with multiple labels from the EUROVOC taxonomy. The dataset covers 23 official EU languages from 7 language families.

Introducing paper: MultiEURLEX -- A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer
M-PCCD
Full name: MPEG Point Cloud Compression Dataset

The emerging MPEG point cloud codecs (V-PCC and G-PCC variants) are assessed, and best practices for rate allocation are investigated [1]. For this purpose, three experiments are conducted. In the first experiment, a rigorous evaluation of the codecs is performed, adopting test conditions dictated by experts of the group on a carefully selected set of models, using both subjective and objective quality assessment methodologies. In the other two experiments, different rate allocation schemes for geometry-only and geometry-plus-color encoding are subjectively evaluated, in order to draw conclusions on the best-performing approaches in terms of perceived quality for a given bit rate.

In this webpage, we make publicly available quality scores associated with the stimuli under assessment for each experiment. For purposes of reproducibility, a content that is used while not being part of established point cloud repositories adopted by standardisation bodies, is re-distributed. Moreover, scripts are provided in order to generate the reference models and the rendering-related meta-data that are used in this study.
FunKPoint

**FunKPoint** is a dataset for finding correspondences in visual data that has ground truth correspondences for 10 tasks and 20 object categories.

Introducing paper: The Functional Correspondence Problem
MFH

The **MFH** dataset is a multi-viewpoint fine-grained hand hygiene dataset. It contains 73,1147 samples in total, which are collected by 6 camera views in 6 different locations. All samples are split into 7 classes in total. MFH dataset is distinguished from existing datasets in three aspects: the large intra-class difference, the subtle inter-class difference, and the data mismatch in distribution between the training phase and the inference phase. This dataset thus provides a more realistic benchmark.

Introducing paper: Fine-grained Hand Gesture Recognition in Multi-viewpoint Hand Hygiene
Hummingbird

**Hummingbird** is a dataset to examine stylistic lexical cues from human perception and BERT used to characterize their discrepancy. In HUMMINGBIRD crowd-workers relabeled benchmarking datasets for style classification tasks.

Introducing paper: Does BERT Learn as Humans Perceive? Understanding Linguistic Styles through Lexica
WhyAct

**WhyAct** is a dataset for identifying human action reasons in online videos, consisting of 1,077 visual actions manually annotated with their reasons.

Introducing paper: WhyAct: Identifying Action Reasons in Lifestyle Vlogs
Frustrated Legislators: Replication data and code

Description: Replication data and code for
Aref, S., and Neal, Z.P., "Identifying hidden coalitions in the US House of Representatives by optimally partitioning signed networks based on generalized balance" (2021) Scientific Reports. http://dx.doi.org/10.1038/s41598-021-98139-w
TREx-2p

**TREx-2p** is a dataset to probe whether a pretrained LM possesses “indirect” 2-hop knowledge. It is a 2-hop variant of the T-REx dataset. It has been built by manually examining the 2-hop link existing in the knowledge graph of TREx-1p, and select eight 2- hop relation types that make sense to humans

Introducing paper: An Empirical Study on Few-shot Knowledge Probing for Pretrained Language Models
VoicePrivacy 2020

**VoicePrivacy 2020** is a dataset for developing anonymization solutions for speech technology. It is built from subsets of existing datasets such as: [LibriSpeech](librispeech-1), [LibriTTS](libritts), [VoxCeleb1](voxceleb1), [VoxCeleb2](voxceleb1) and [VCTK](vctk).

Introducing paper: The VoicePrivacy 2020 Challenge: Results and findings
ImageTBAD

A dataset of A 3D Computed Tomography (CT) image dataset, ImageTBAD, for segmentation of Type-B Aortic Dissection is published. ImageTBAD contains 100 3D Computed Tomography (CT) images, which is of decent size compared with existing medical imaging datasets.

ImageTBAD contains a total of 100 3D CTA images gathered from Guangdong Peoples' Hospital Data from January 1,2013 to April 23, 2019. Images are acquired from a variety of scanners (GE Medical Systems, Siemens, Philips), resulting in large variance in voxel size, resolution and imaging quality. All the images are pre-operative TBAD CTA images whose top and bottom are around the neck and the brachiocephalic vessels, respectively, in the axial view. The segmentation labeling is performed by a team of two cardiovascular radiologists who have extensive experience with TBAD. The segmentation labeling of each patient is fulfilled by one radiologist and checked by the other. The segmentation

Introducing paper: ImageTBAD: A 3D Computed Tomography Angiography Image Dataset for Automatic Segmentation of Type-B Aortic Dissection
WebQA

WebQA, is a new benchmark for multimodal multihop reasoning in which systems are presented with the same style of data as humans when searching the web: Snippets and Images. The system must then identify which information is relevant across modalities and combine it with reasoning to answer the query. Systems will be evaluated on both the correctness of their answers and their sources.

Introducing paper: WebQA: Multihop and Multimodal QA
MiniF2F

**MiniF2F** is a dataset of formal Olympiad-level mathematics problems statements intended to provide a unified cross-system benchmark for neural theorem proving. The miniF2F benchmark currently targets Metamath, Lean, and Isabelle and consists of 488 problem statements drawn from the AIME, AMC, and the International Mathematical Olympiad (IMO), as well as material from high-school and undergraduate mathematics courses.

Introducing paper: MiniF2F: a cross-system benchmark for formal Olympiad-level mathematics
mMARCO

**mMARCO** is a multilingual version of the MS MARCO passage ranking dataset comprising 8 languages that was created using machine translation.

Introducing paper: mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset
AwA Pose

**AwA Pose** is a large scale animal keypoint dataset with ground truth annotations for keypoint detection of quadruped animals from images.

Introducing paper: A Novel Dataset for Keypoint Detection of quadruped Animals from Images
TREK-150

**TREK-150** is a benchmark dataset for object tracking in First Person Vision (FPV) videos composed of 150 densely annotated video sequences.

Introducing paper: Is First Person Vision Challenging for Object Tracking?
OAK
Full name: Objects Around Krishna

**OAK** is a dataset for online continual object detection benchmark with an egocentric video dataset. OAK adopts the KrishnaCam videos, an ego-centric video stream collected over nine months by a graduate student. OAK provides exhaustive bounding box annotations of 80 video snippets (~17.5 hours) for 105 object categories in outdoor scenes.

Introducing paper: Wanderlust: Online Continual Object Detection in the Real World
Depth in the Wild

**Depth in the Wild** is a dataset for single-image depth perception in the wild, i.e., recovering depth from a single image taken in unconstrained settings. It consists of images in the wild annotated with relative depth between pairs of random points.

Introducing paper: Single-Image Depth Perception in the Wild
Fashion-MMT

**Fashion-MNT** is large-scale bilingual product description dataset called Fashion-MMT, which contains over 114k noisy and 40k manually cleaned description translations with multiple product images.

Introducing paper: Product-oriented Machine Translation with Cross-modal Cross-lingual Pre-training
ComSum

ComSum is a data set of 7 million commit messages for text summarization. When documenting commits, software code changes, both a message and its summary are posted. These messages are gathered and filtered to curate developers' work summarization data set.

Introducing paper: ComSum: Commit Messages Summarization and Meaning Preservation
Indiscapes2

Indiscapes2, a new large-scale diverse dataset of Indic manuscripts with semantic layout annotations. Indiscapes2 contains documents from four different historical collections and is 150% larger than its predecessor, Indiscapes.

Introducing paper: Palmira: A Deep Deformable Network for Instance Segmentation of Dense and Uneven Layouts in Handwritten Manuscripts
BnB

BnB is a large-scale and diverse in-domain VLN (Vision and Language Navigation) dataset.

Introducing paper: Airbert: In-domain Pretraining for Vision-and-Language Navigation
VIL-100

**VIL-100** is a  video instance lane detection dataset, which contains 100 videos with in total 10,000 frames, acquired from different real traffic scenarios. All the frames in each video are manually annotated to a high-quality instance-level lane annotation, and a set of frame-level and video-level metrics are included for quantitative performance evaluation.

Introducing paper: VIL-100: A New Dataset and A Baseline Model for Video Instance Lane Detection
Automated Evolution of Feature Logging Statement Levels Using Git Histories and Degree of Interest

Logging—used for system events and security breaches to more informational yet essential aspects of software features—is pervasive. Given the high transactionality of today's software, logging effectiveness can be reduced by information overload. Log levels help alleviate this problem by correlating a priority to logs that can be later filtered. As software evolves, however, levels of logs documenting surrounding feature implementations may also require modification as features once deemed important may have decreased in urgency and vice-versa. We present an automated approach that assists developers in evolving levels of such (feature) logs. The approach, based on mining Git histories and manipulating a degree of interest (DOI) model, transforms source code to revitalize feature log levels based on the "interestingness" of the surrounding code. Built upon JGit and Mylyn, the approach is implemented as an Eclipse IDE plug-in and evaluated on 18 Java projects with ~3 million lines of code and ~4K log statements. Our tool successfully analyzes 99.26% of logging statements, increases log level distributions by ~20%, identifies logs manually modified with a recall of ~80% and a level-direction match rate of ~87%, and increases the focus of logs in bug fix contexts ~83% of the time. Moreover, pull (patch) requests were integrated into large and popular open-source projects. The results indicate that the approach is promising in assisting developers in evolving feature log levels.

Introducing paper: Automated Evolution of Feature Logging Statement Levels Using Git Histories and Degree of Interest
COCO 10% labeled data

Semi-Supervised Object Detection on COCO 10% labeled data
VQA-CE
Full name: VQA Counterexamples

This dataset provides a new split of VQA v2 (similarly to VQA-CP v2), which is built of questions that are hard to answer for biased models.

This dataset is designed to penalize biases, and encourage the learning of models that generalize well.

Introducing paper: Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question Answering
Medical Wiki Paralell Corpus for Medical Text Simplification

A medical Wiki paralell corpus for medical text simplification.

Introducing paper: AutoMeTS: The Autocomplete for Medical Text Simplification
LiDAR-MOS
Full name: LiDAR-based Moving Object Segmentation

# Tasks.
In moving object segmentation of point cloud sequences, one has to provide motion labels for each point of the test sequences 11-21. Therefore, the input to all evaluated methods is a list of coordinates of the three-dimensional points along with their remission, i.e., the strength of the reflected laser beam which depends on the properties of the surface that was hit. Each method should then output a label for each point of a scan, i.e., one full turn of the rotating LiDAR sensor. Here, we only distinguish between static and moving object classes.

# Metric
To assess the labeling performance, we rely on the commonly applied Jaccard Index or intersection-over-union (mIoU) metric over moving parts of the environment. We map all moving-x classes of the original SemanticKITTI semantic segmentation benchmark to a single moving object class.

# Citation
Citation. More information on the task and the metric, you can find in our publication related to the task:
@article{chen2021ral,
  title={{Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data}},
  author={X. Chen and S. Li and B. Mersch and L. Wiesmann and J. Gall and J. Behley and C. Stachniss},
  year={2021},
  journal={IEEE Robotics and Automation Letters(RA-L)},
  doi = {10.1109/LRA.2021.3093567}
}

Introducing paper: Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data
Spider-Realistic

Spider dataset is used for evaluation in the paper "Structure-Grounded Pretraining for Text-to-SQL". The dataset is created based on the dev split of the Spider dataset (2020-06-07 version from https://yale-lily.github.io/spider). We manually modified the original questions to remove the explicit mention of column names while keeping the SQL queries unchanged to better evaluate the model's capability in aligning the NL utterance and the DB schema. For more details, please check our paper at https://arxiv.org/abs/2010.12773.

Introducing paper: Structure-Grounded Pretraining for Text-to-SQL
Infologic sql queries

Sql queries

Introducing paper: "What makes my queries slow?": Subgroup Discovery for SQL Workload Analysis
ProcGen

Procgen Benchmark includes 16 simple-to-use procedurally-generated environments which provide a direct measure of how quickly a reinforcement learning agent learns generalizable skills.

Introducing paper: Leveraging Procedural Generation to Benchmark Reinforcement Learning