Omkar2118/datasets huggingface dataset:
BIOSSES
Full name: Biomedical Semantic Similarity Estimation System

The BIOSSES data set comprises total 100 sentence pairs all of which were selected from the "[TAC2 Biomedical Summarization Track Training Data Set](https://tac.nist.gov/2014/BiomedSumm/)" .

The sentence pairs were evaluated by five different human experts that judged their similarity and gave scores in a range [0-4]. Our guideline was prepared based on SemEval 2012 Task 6 Guideline.

Image source: [BIOSSES](https://tabilab.cmpe.boun.edu.tr/BIOSSES/DataSet.html)

Introducing paper: BIOSSES: A Semantic Sentence Similarity Estimation System for the Biomedical Domain
MedNLI
Full name: Medical Natural Language Inference

The **MedNLI** dataset consists of the sentence pairs developed by Physicians from the Past Medical History section of MIMIC-III clinical notes annotated for Definitely True, Maybe True and Definitely False. The dataset contains 11,232 training, 1,395 development and 1,422 test instances. This provides a natural language inference task (NLI) grounded in the medical history of patients.

Source: [MT-Clinical BERT: Scaling Clinical Information Extraction with Multitask Learning](https://arxiv.org/abs/2004.10220)
Image Source: [https://arxiv.org/abs/1904.02181](https://arxiv.org/abs/1904.02181)
WiderPerson

WiderPerson contains a total of 13,382 images with 399,786 annotations, i.e., 29.87 annotations per image, which means this dataset contains dense pedestrians with various kinds of occlusions. Hence, pedestrians in the proposed dataset are extremely challenging due to large variations in the scenario and occlusion, which is suitable to evaluate pedestrian detectors in the wild.

Source: [WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild](/paper/widerperson-a-diverse-dataset-for-dense)
Image Source: [http://www.cbsr.ia.ac.cn/users/sfzhang/WiderPerson/](http://www.cbsr.ia.ac.cn/users/sfzhang/WiderPerson/)

Introducing paper: WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild
CID
Full name: Campus Image Dataset

The **CID** (**Campus Image Dataset**) is a dataset captured in low-light env with the help of Android programming. Its basic unit is group, which is named by capture time and contains 8 exposure-time-varying raw image shot in a burst.

Source: [https://github.com/505030475/ExtremeLowLight](https://github.com/505030475/ExtremeLowLight)

Introducing paper: Learning an Adaptive Model for Extreme Low-light Raw Image Processing
LeNER-Br

LeNER-Br is a dataset for named entity recognition (NER) in Brazilian Legal Text.

Introducing paper: LeNER-Br: a Dataset for Named Entity Recognition in Brazilian Legal Text
OIE2016

OIE2016 is the first large-scale OpenIE benchmark. It is created by automatic conversion from QA-SRL [He et al., 2015], a semantic role labeling dataset. The sentences are from news (e.g., WSJ) and encyclopedia (e.g., WIKI) domains. Since there are no restrictions on the elements of OpenIE extractions, partial-matching criteria instead of exact-matching is typically used. Hence, the evaluation script can tolerate the extractions that are slightly different from the gold annotation. 

Source: https://arxiv.org/pdf/2205.11725.pdf (section 3.1)

Introducing paper: Creating a Large Benchmark for Open Information Extraction
ToTTo
Full name: ToTTo

ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description.

During the dataset creation process, tables from English Wikipedia are matched with (noisy) descriptions. Each table cell mentioned in the description is highlighted and the descriptions are iteratively cleaned and corrected to faithfully reflect the content of the highlighted cells.

Source: [Google Research Datasets](https://github.com/google-research-datasets/totto)

Introducing paper: ToTTo: A Controlled Table-To-Text Generation Dataset