Omkar2118/datasets huggingface dataset:
REBUS
Full name: A Robust Evaluation Benchmark of Understanding Symbols

Recent advances in large language models have led to the development of multimodal LLMs
(MLLMs), which take both image data and text as an input. Virtually all of these models
have been announced within the past year, leading to a significant need for benchmarks
evaluating the abilities of these models to reason truthfully and accurately on a diverse set
of tasks. When Google announced Gemini (Gemini Team et al., 2023), they showcased its
ability to solve rebuses—wordplay puzzles which involve creatively adding and subtracting
letters from words derived from text and images. The diversity of rebuses allows for a
broad evaluation of multimodal reasoning capabilities, including image recognition, multi-
step reasoning, and understanding the human creator’s intent.
We present REBUS: a collection of 333 hand-crafted rebuses spanning 13 diverse cate-
gories, including hand-drawn and digital images created by nine contributors. Samples are
presented in Table 1. Notably, GPT-4V, the most powerful model we evaluated, answered
only 24% of puzzles correctly, highlighting the poor capabilities of MLLMs in new and unex-
pected domains to which human reasoning generalizes with comparative ease. Open-source
models perform even worse, with a median accuracy below 1%. We notice that models
often give faithless explanations, fail to change their minds after an initial approach doesn’t
work, and remain highly uncalibrated on their own abilities.
ASAP
Full name: Aligned Scores and Performances

**ASAP** is a dataset of 222 digital musical scores aligned with 1068 performances (more than 92 hours) of Western classical piano music.

The scores are provided as paired MusicXML files and quantized MIDI files, and the performances as paired MIDI files and partially as audio recordings. Scores and performances are aligned with downbeat, beat, time signature, and key signature annotations.

Source: [ASAP: a dataset of aligned scores and performances for piano transcription](https://archives.ismir.net/ismir2020/paper/000127.pdf)
global-weather-repository

This dataset provides daily weather information for capital cities around the world. Unlike forecast data, this dataset offers a comprehensive set of features that reflect the current weather conditions around the world.
Starting from August 29, 2023.
It provides over 40+ features , including temperature, wind, pressure, precipitation, humidity, visibility, air quality measurements and more. The dataset is valuable for analyzing Global weather patterns, exploring climate trends, and understanding the relationships between different weather parameters.