This is a repository of dad jokes meant to be used to fine-tune an LLM to have a sense of humor.

A benchmark collection for sentence-based image description and search, consisting of 8,000 images that are each paired with five different captions which provide clear descriptions of the salient entities and events. … The images were chosen from six different Flickr groups, and tend not to contain any well-known people or locations, but were manually selected to depict a variety of scenes and situations.

This repository contains code and data used in Interpreting and Explaining Deep Neural Networks for Classifying Audio Signals. The dataset consists of 30,000 audio samples of spoken digits (0–9) from 60 different speakers. Additionally, it holds the audioMNIST_meta.txt, which provides meta information such as the gender or age of each speaker.

This dataset contains 404,290 questions pairs from Quora, and if they are duplicates of eachother.

TriviaQA is a reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K question-answer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on average, that provide high quality distant supervision for answering the questions.

AGIEval is a human-centric benchmark specifically designed to evaluate the general abilities of foundation models in tasks pertinent to human cognition and problem-solving. T

MMLU (Massive Multitask Language Understanding) is a new benchmark designed to measure knowledge acquired during pre-training by evaluating models exclusively in zero-shot and few-shot settings. This makes the benchmark more challenging and more similar to how we evaluate humans. The benchmark covers 57 subjects across STEM, the humanities, the social sciences, and more. It ranges in difficulty from an elementary level to an advanced professional level, and it tests both world knowledge and problem solving ability. Subjects range from traditional areas, such as mathematics and history, to more specialized areas like law and ethics. The granularity and breadth of the subjects makes the benchmark ideal for identifying a model’s blind spots.