Omkar2118/datasets huggingface dataset:
Deepmatcher

https://github.com/anhaidgroup/deepmatcher/blob/master/Datasets.md
Twitter-HyDrug-UR
Full name: Twitter Hypergraph Drug for User Roles

This benchmark hypergraph dataset, Twitter-HyDrug-UR,  is derived from Twitter-HyDrug by [HyGCL-DC](https://github.com/GraphResearcher/HyGCL-DC). Twitter-HyDrug-UR is a real-world hypergraph data that describes the drug trafficking on Twitter. Unlike HyGCL-DC, which targets a drug trafficking community detection task (a multi-label node classification), we aim to identify drug user roles in drug trafficking activities on social media. To this end, we categorize node labels into four distinct roles: drug seller, drug buyer, drug user, and drug discussant, and each node is assigned to one and only one label. Consequently, we frame the problem for Twitter-HyDrug-UR as a multi-class node classification task.
EGSet12
Full name: Twelve real & original solo electric guitar performances with diverse playing styles to evaluate guitar tablature transcription

EGSet12 is a small dataset with twelve original and real solo electric guitar performances (31.65 seconds avg. duration, totaling 379.8 seconds). These pieces were composed by a professional musician and guitar player, showcasing the full tonal range of the electric guitar across diverse melodies and chord complexities. EGSet12 encompasses a broad spectrum of styles, including pop, funk, jazz and twelve-tone, reflecting varied tonalities, keys, rhythms, and modes.
EGSet12 was performed by a single professional guitarist using a Sire T7 Telecaster guitar and a Yamaha B15 amplifier. The performance setup allowed the performer to freely select the guitar’s volume and tone knobs, also allowing techniques like alternate picking, hybrid picking, and palm mute. We captured the performance using an ECM8000 microphone positioned 15 centimeters from the amplifier and connected to a UMC202 HD audio interface (original sampling rate of 48000Hz; each file's stereo channels are a copy of each other; in reality the signal is mono). This recording setup differs significantly from those used in datasets meant to train guitar tablature transcription models. Other than the amplifier, no additional audio effects were used in EGSet12.

EGSet12’s realism, tone diversity, and playing styles make it useful to assess methods for guitar tablature transcription. 

Introduced in the paper: LEVERAGING ELECTRIC GUITAR TONES AND EFFECTS TO IMPROVE ROBUSTNESS IN GUITAR TABLATURE TRANSCRIPTION MODELING

The weights for the best performing model (TabCNN trained with "GuitarProFX") in the paper are also provided.
Mono3DRefer

We sample 2025 frames of images from the original KITTI for Mono3DRefer, containing 41,140 expressions in total and a vocabulary of 5,271 words.

Introducing paper: Mono3DVG: 3D Visual Grounding in Monocular Images
Linguistic Benchmark

The Linguistic Benchmark (JSON), consisting of 30 questions was developed to be easy for human adults to answer but challenging for LLMs. It is designed to assess the well-documented limitations of LLMs across domains such as spatial reasoning, linguistic understanding, relational thinking, mathematical reasoning, knowledge of basic scientific concepts, and common sense. This benchmark is a useful tool to gauge the current capabilities capabilities of LLMs. The questions serve as a linguistic benchmark to examine model performance in several key domains where they have known limitations.

Introducing paper: Easy Problems That LLMs Get Wrong
MoToMQA
Full name: Multi-Order Theory of Mind Question & Answer

The **MoToMQA (Multi-Order Theory of Mind Question & Answer)** benchmark is a test suite introduced to examine the extent to which large language models (LLMs) have developed higher-order theory of mind (ToM); the human ability to reason about multiple mental and emotional states in a recursive manner¹.

The benchmark is based on a ToM test designed for human adults and involves answering true/false questions about characters in short-form stories¹. The test examines LLM ToM from orders 2-6, where the 'order of intentionality' is the number of mental states involved in a ToM reasoning process¹. For example, a third-order statement is "I think you believe that she knows"¹.

The MoToMQA benchmark is used to compare the performance of LLMs to a newly gathered adult human benchmark¹. It assesses how ToM order affects LLM performance, how LLM performance compares to human performance, and how LLM performance on ToM tasks compares to performance on factual tasks of equivalent syntactic complexity¹.

(1) LLMs achieve adult human performance on higher-order theory of mind tasks. https://arxiv.org/pdf/2405.18870.
(2) UserBenchmark: PC Speed Test Tool - Compare Your PC. https://www.userbenchmark.com/Software.
(3) MotionMark - browser bench. https://browserbench.org/MotionMark/.

Introducing paper: LLMs achieve adult human performance on higher-order theory of mind tasks
UTSD
Full name: Unified Time Series Dataset

Unified Time Series Dataset (UTSD) includes 7 domains with up to 1 billion time points with hierarchical capacities to facilitate research of large models in the field of time series. It is meticulously assembled from a blend of publicly accessible online data repositories and empirical data derived from real-world machine operations. We analyze each dataset within the collection, examining the time series through the lenses of stationarity and forecastability to allows us to characterize the level of complexity inherent to each dataset.

All datasets are classified into seven distinct domains by their source: Energy, Environment, Health, Internet of Things (IoT), Nature, Transportation, and Web with diverse sampling frequencies. UTSD is constructed with hierarchical capacities, namely UTSD-1G, UTSD-2G, UTSD-4G, and UTSD-12G, where each smaller dataset is a subset of the larger ones. A larger subset means greater data difficulty and diversity, allowing you to conduct detailed scaling experiments.

See the [paper](https://arxiv.org/pdf/2402.02368) and [codebase](https://github.com/thuml/Large-Time-Series-Model) for more information.

Introducing paper: Timer: Generative Pre-trained Transformers Are Large Time Series Models