Datasets

The ARC dataset consists of 7,787 science exam questions drawn from a variety of sources, including science questions provided under license by a research partner affiliated with AI2.

MathVista: Diverse benchmark for mathematical reasoning in visual contexts. Includes 6,141 examples from 31 datasets.

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.

LongBench is a comprehensive benchmark for multilingual and multi-task purposes, with the goal to fully measure and evaluate the ability of pre-trained language models to understand long text

MINDS-14 is a dataset designed for the intent detection task with spoken data. It encompasses 14 distinct intents extracted from a commercial system in the e-banking domain.

This is the repository for PLOD Dataset subset being used for CW in NLP module 2023-2024 at University of Surrey.

MMLU is a new benchmark designed to measure knowledge acquired during pretraining by evaluating models exclusively in zero-shot and few-shot settings.

The Beyond the Imitation Game Benchmark (BIG-bench) is a collaborative benchmark intended to probe large language models and extrapolate their future capabilities. Big-bench include more than 200 tasks.

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

NIH Chest X-Ray is a large dataset containing chest X-ray images of patients collected by the National Institutes of Health (NIH) of the United States.

Cifar-100 is used to train and evaluate image classification models in complex tasks.

Mnist is used to train and evaluate image classification models in complex tasks.

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.