Omkar2118/datasets huggingface dataset:
Wikipedia Title

**Wikipedia Title** is a dataset for learning character-level compositionality from the character visual characteristics. It consists of a collection of Wikipedia titles in Chinese, Japanese or Korean labelled with the category to which the article belongs.

Source: [https://arxiv.org/abs/1704.04859](https://arxiv.org/abs/1704.04859)

Introducing paper: Learning Character-level Compositionality with Visual Features
WikiQAar
Full name: English-Arabic Wikipedia Question-Answering

A publicly available set of question and sentence pairs, collected and annotated for research on open-domain question answering.

Source: [WikiQA: A Challenge Dataset for Open-Domain Question Answering](/paper/wikiqa-a-challenge-dataset-for-open-domain)

Introducing paper: WikiQA: A Challenge Dataset for Open-Domain Question Answering
WikiReading Recycled

A newly developed public dataset and the task of multiple property extraction. It uses the same data as WikiReading but does not inherit its predecessor's identified disadvantages.

Source: [From Dataset Recycling to Multi-Property Extraction and Beyond](/paper/from-dataset-recycling-to-multi-property)

Introducing paper: From Dataset Recycling to Multi-Property Extraction and Beyond
WikiSection

A publicly available dataset with 242k labeled sections in English and German from two distinct domains: diseases and cities.

Source: [SECTOR: A Neural Model for Coherent Topic Segmentation and Classification](/paper/sector-a-neural-model-for-coherent-topic)

Introducing paper: SECTOR: A Neural Model for Coherent Topic Segmentation and Classification
WikiSem500

The **WikiSem500** dataset contains around 500 per-language cluster groups for English, Spanish, German, Chinese, and Japanese (a total of 13,314 test cases).

Source: [https://arxiv.org/abs/1611.01547](https://arxiv.org/abs/1611.01547)

Introducing paper: Automated Generation of Multilingual Clusters for the Evaluation of Distributed Representations
WikiSplit

Contains one million naturally occurring sentence rewrites, providing sixty times more distinct split examples and a ninety times larger vocabulary than the WebSplit corpus introduced by Narayan et al. (2017) as a benchmark for this task.

Source: [Learning To Split and Rephrase From Wikipedia Edit History](/paper/learning-to-split-and-rephrase-from-wikipedia)

Introducing paper: Learning To Split and Rephrase From Wikipedia Edit History
WikiSRS

**WikiSRS** is a novel dataset of similarity and relatedness judgments of paired Wikipedia entities (people, places, and organizations), as assigned by Amazon Mechanical Turk workers.

Source: [https://github.com/OSU-slatelab/WikiSRS](https://github.com/OSU-slatelab/WikiSRS)

Introducing paper: Jointly Embedding Entities and Text with Distant Supervision
WikiText-TL-39

WikiText-TL-39 is a benchmark language modeling dataset in Filipino that has 39 million tokens in the training set.

Source: [Evaluating Language Model Finetuning Techniques for Low-resource Languages](/paper/evaluating-language-model-finetuning)

Introducing paper: Evaluating Language Model Finetuning Techniques for Low-resource Languages
Wilds

Builds on top of recent data collection efforts by domain experts in these applications and provides a unified collection of datasets with evaluation metrics and train/test splits that are representative of real-world distribution shifts.

The v2.0 update adds unlabeled data to 8 datasets. The labeled data and evaluation metrics are exactly the same, so all previous results are directly comparable.

Source: [WILDS: A Benchmark of in-the-Wild Distribution Shifts](/paper/wilds-a-benchmark-of-in-the-wild-distribution)

Introducing paper: WILDS: A Benchmark of in-the-Wild Distribution Shifts
WildDeepfake

**WildDeepfake** is a dataset for real-world deepfakes detection which consists of 7,314 face sequences extracted from 707 deepfake videos that are collected completely from the internet. WildDeepfake is a small dataset that can be used, in addition to existing datasets, to develop more effective detectors against real-world deepfakes.

Source: [https://github.com/deepfakeinthewild/deepfake-in-the-wild](https://github.com/deepfakeinthewild/deepfake-in-the-wild)
Image Source: [https://github.com/deepfakeinthewild/deepfake-in-the-wild](https://github.com/deepfakeinthewild/deepfake-in-the-wild)

Introducing paper: WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection
Winogender Schemas

Winogender Schemas is a novel, Winograd schema-style set of minimal pair sentences that differ only by pronoun gender.

Source: [Gender Bias in Coreference Resolution](/paper/gender-bias-in-coreference-resolution)

Introducing paper: Gender Bias in Coreference Resolution