Omkar2118/datasets huggingface dataset:
GEMRec-18K

GEMRec-18K is a dense prompt-model interaction dataset that consists of 18K images generated by pairing 200 generative models with 90 prompts collected from real-world usages. This dataset builds the cornerstone for exploring generative model recommendation and can be useful for understanding generative models.

Introducing paper: GEMRec: Towards Generative Model Recommendation
GuardRails Dataset
Full name: GuardRails Dataset of Problems with Known Ambiguities

For each problem, we provide 4 variants of prompts:

1. S (function signature)
2. SP (S + purpose statement)
3. SP1 (SP + 1 functional example)
4. SPx (SP1 + more functional examples)

Note that the purpose statements are ambiguous and the functional examples do not correspond to any of the ambiguous input classes (AICs).

Introducing paper: GuardRails: Automated Suggestions for Clarifying Ambiguous Purpose Statements
Ray-tracing data in Herald Square

Data obtained by ray-tracing simulation in Herald Square. 
Data includes all the channel parameter information such as pathloss, delay, and angles.
BOTH57M

BOTH57M is a body-hand dataset with body-level text prompts and finger-level text prompts.
CORE-MM

CORE-MM is an Open-ended VQA benchmark dataset specifically designed for MLLMs, with a focus on complex reasoning tasks. CORE-MM benchmark consists of 279 manually curated reasoning questions, associated with a total of 342 images. The questions are divided into 3 reasoning categories--Deductive, Abductive and Analogical. 49 questions pertain to abductive reasoning, 181 require deductive reasoning, and 49 involve analogicalreasoning. Furthermore, the dataset is divided into two folds based on reasoning complexity, with 108 classified as “High” reasoning complexity and 171 as “Moderate” reasoning complexity.
CMNLI
Full name: Chinese Multi-Genre NLI

The CMNLI dataset is part of the Chinese Language Understanding Evaluation (CLUE) benchmark. It consists of two parts: XNLI and MNLI. The data comes from various sources such as fiction, telephone, travel, government, slate, etc. The original MNLI data and XNLI data were translated into Chinese and English. The original training set was retained, and the dev and test sets were created by merging and shuffling the dev set from XNLI and the matched set from MNLI, and the test set from XNLI and the mismatched set from MNLI, respectively.

Introducing paper: CLUE: A Chinese Language Understanding Evaluation Benchmark
EPRSTMT
Full name: E-commerce Product Review Dataset for Sentiment Analysis

The EPRSTMT dataset, also known as EPR-sentiment, is a binary sentiment analysis dataset based on product reviews on an e-commerce platform. Each sample in the dataset is labeled as either Positive or Negative. It was collected by the ICIP Lab of Beijing Normal University and has been re-organized to make it suitable for sentiment analysis tasks.

Introducing paper: FewCLUE: A Chinese Few-shot Learning Evaluation Benchmark
LEVIR-CC

A Large Dataset for Remote Sensing Image Change Captioning. The LEVIR-CC dataset contains 10,077 pairs of bi-temporal remote sensing images and 50,385 sentences describing the differences between images.