The main aim for this dataset is to cover a wide variety of social biases that are implied in text, both subtle and overt, and make the biases representative of real world discrimination that people experience RWJF 2017.

PathVQA consists of 32,799 open-ended questions from 4,998 pathology images where each question is manually checked to ensure correctness.

Cifar-10 is an important resource in the field of image recognition and classification, used to train and test machine learning models and neural networks.

TAL-SCQ5K are high-quality mathematical competition datasets created by TAL Education Group.

Korean Language Understanding Evaluation (KLUE) benchmark is a series of datasets to evaluate natural language understanding capability of Korean language models.

The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified Good and Featured articles on Wikipedia.

FashionMNIST is a perfect alternative dataset to MNIST in evaluating machine learning algorithms.

This dataset is a great resource for researchers who want to evaluate cross-lingual question answering performance.