Omkar2118/datasets huggingface dataset:
GD-NLI
Full name: Generated Debiased NLI Datasets

This is a set of *debiased* Natural Language Inference (NLI) datasets produced by the paper  [Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets](https://arxiv.org/abs/2203.12942). The datasets are constructed by augmenting SNLI or MNLI with data samples that are *generated to mitigate the spurious correlations* in the original datasets. Please visit [this repository](https://github.com/jimmycode/gen-debiased-nli) for more details.

Citation:
```
@inproceedings{gen-debiased-nli-2022,
    title = "Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets",
    author = "Wu, Yuxiang  and
      Gardner, Matt  and
      Stenetorp, Pontus  and
      Dasigi, Pradeep",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics",
    month = may,
    year = "2022",
    publisher = "Association for Computational Linguistics",
}
```

Introducing paper: Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets
V3C1
Full name: the Vimeo Creative Commons Collection 1

The dataset has been designed to represent true web videos in the wild, with good visual quality and diverse content characteristics, and will serve as evaluation basis for the Video Browser Showdown 2019-2021 and TREC Video Retrieval (TRECVID) Ad-Hoc Video Search tasks 2019-2021. The dataset comes with a shot segmentation (around 1 million shots) for which we analyze content specifics and statistics. Our research shows that the content of V3C1 is very diverse, has no predominant characteristics and provides a low self-similarity. Thus it is very well suited for video retrieval evaluations as well as for participants of TRECVID AVS or the VBS.
IACC.3
Full name: Internet Archive videos (IACC.3) under Creative Commons licenses.

The IACC.3 dataset is approximately 4600 Internet Archive videos (144 GB, 600 h) with Creative Commons licenses in MPEG-4/H.264 format with duration ranging from 6.5 min to 9.5 min and a mean duration of almost 7.8 min. Most videos will have some metadata provided by the donor available e.g., title, keywords, and description.
Rope3D

**Roadside Perception 3D** (**Rope3D**) is a dataset for autonomous driving and monocular 3D object detection task consisting of 50k images and over 1.5M 3D objects in various scenes, which are captured under different settings including various cameras with ambiguous mounting positions, camera specifications, viewpoints, and different environmental conditions.

Introducing paper: Rope3D: TheRoadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Task
UDE-Office-Home
Full name: UDE-Office-Home

Different from the setting of domain adaptation which uses all labeled source and unlabeled target domain examples for training,  domain examples should be divided into two disjoint parts: training and test.  UDE-Office-Home is built from Office-Home, so the performance of domain-adapted or domain-expanded models on the source and target domain can be evaluated.

Introducing paper: Unsupervised Domain Expansion for Visual Categorization
UDE-DomainNet
Full name: UDE-DomainNet

Different from the setting of domain adaptation which uses all labeled source and unlabeled target domain examples for training,  domain examples should be divided into two disjoint parts: training and test.  UDE-DomainNet is built from DomainNet, so the performance of domain-adapted or domain-expanded models on the source and target domain can be evaluated.

Introducing paper: Unsupervised Domain Expansion for Visual Categorization
IAM Dataset
Full name: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks

We introduce a large and comprehensive dataset to facilitate the study of several essential AM tasks in the debating system. In our work, we first review the existing subtasks (claim extraction, stance classification, evidence extraction), and then propose two integrated argument mining tasks: claim extraction with stance classification (CESC) and claim-evidence pair extraction (CEPE).

Introducing paper: IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks