Omkar2118/datasets huggingface dataset:
YorkTag

YorkTag provides pairs of sharp/blurred images containing fiducial markers and is proposed to train and qualitatively and quantitatively evaluate our model.

Introducing paper: Application of Ghost-DeblurGAN to Fiducial Marker Detection
GMEG-yahoo

Grammatical error correction dataset for text from Yahoo! Answers

Introducing paper: Enabling Robust Grammatical Error Correction in New Domains: Data Sets, Metrics, and Analyses
GMEG-wiki

Grammatical error correction dataset for text from Wikipedia.

Introducing paper: Enabling Robust Grammatical Error Correction in New Domains: Data Sets, Metrics, and Analyses
SituatedQA

**SituatedQA** is an open-retrieval QA dataset where systems must produce the correct answer to a question given the temporal or geographical context. Answers to the same question may change depending on the extralinguistic contexts (when and where the question was asked).

Introducing paper: SituatedQA: Incorporating Extra-Linguistic Contexts into QA
E-Manual Corpus

**E-Manual Corpus** is a corpus of 307,957 E-manuals, used for pre-training models for Question Answering on e-manuals.

Introducing paper: Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework
CelebA-Dialog

The CelebA-Dialog dataset has the following properties: 1) Facial images are annotated with rich fine-grained labels, which classify one attribute into multiple degrees according to its semantic meaning; 2) Accompanied with each image, there are captions describing the attributes and a user request sample.

Image source: [https://arxiv.org/pdf/2109.04425v1.pdf](https://arxiv.org/pdf/2109.04425v1.pdf)

Introducing paper: Talk-to-Edit: Fine-Grained Facial Editing via Dialog
VGaokao

**VGaokao** is a verification style reading comprehension dataset designed for native speakers' evaluation.

Introducing paper: Extract, Integrate, Compete: Towards Verification Style Reading Comprehension
Implicit Hate

The **Implicit Hate** corpus is a dataset for hate speech detection with fine-grained labels for each message and its implication. This dataset contains 22,056 tweets from the most prominent extremist groups in the United States; 6,346 of these tweets contain implicit hate speech.

Introducing paper: Latent Hatred: A Benchmark for Understanding Implicit Hate Speech