Omkar2118/datasets huggingface dataset:
HumanEval-ET

Extension test cases of HumanEval, as well as generated code.

Introducing paper: CodeScore: Evaluating Code Generation by Learning Code Execution
MBPP-ET

Extension test cases of MBPP, as well as generated code.

Introducing paper: CodeScore: Evaluating Code Generation by Learning Code Execution
APPS-ET

Extension test cases of APPS, as well as generated code.

Introducing paper: CodeScore: Evaluating Code Generation by Learning Code Execution
Uncorrelated Corrupted Dataset
Full name: UCD

Uncorrelated Corrupted Dataset is an evaluation set that consists of realistic visible-infrared (V-I) corruptions allowing for models' corruption robustness evaluation. Initially proposed for multimodal person re-identification, our dataset can also be used for the evaluation of V-I cross-modal approaches. Corruptions of the visible modality are the twenty corruptions proposed by Chen & al. in the "Benchmarks for Corruption Invariant Person Re-identification" paper. Corruptions of the infrared modalities have been proposed in our paper, introducing 19 corruptions that respect the infrared modality encoding.  In practice, the corruptions are applied randomly and independently to the visible and the infrared cameras, making it more suited to a not co-located camera setting.

Introducing paper: Fusion for Visual-Infrared Person ReID in Real-World Surveillance Using Corrupted Multimodal Data
Correlated Corrupted Dataset
Full name: CCD

Correlated Corrupted Dataset is an evaluation set that consists of realistic visible-infrared (V-I) corruptions allowing for models' corruption robustness evaluation. Initially proposed for multimodal person re-identification, our dataset can also be used for the evaluation of V-I cross-modal approaches. Corruptions of the visible modality are the twenty corruptions proposed by Chen & al. in the "Benchmarks for Corruption Invariant Person Re-identification" paper. Corruptions of the infrared modalities have been proposed in our paper, introducing 19 corruptions that respect the infrared modality encoding. In practice, for co-located visible-infrared cameras, weather-related corruptions should, for example, affect each camera. Also, blur-related corruption would likely occur in both visible and infrared cameras. This dataset tackles this aspect by considering the eventual correlations that may occur from one modality camera to another.

Introducing paper: Fusion for Visual-Infrared Person ReID in Real-World Surveillance Using Corrupted Multimodal Data
FaMoS
Full name: Facial Motion across Subjects

FaMoS is a dynamic 3D head dataset from 95 subjects, each performing 28 motion sequences. The sequences comprise of six prototypical expressions (i.e., Anger, Disgust, Fear, Happiness, Sadness, and Surprise), two head rotations (left/right and up/down), and diverse facial motions, including extreme and asymmetric expressions. Each sequence is recorded at 60 fps. In total,  FaMoS contains around 600K 3D head meshes (i.e., ~225 frames per sequence). For each frame, registrations in FLAME meshes are publicly available.
Demande Dataset
Full name: Demande Dataset

**Demande Dataset** contains the features and probabilites of ten different functions.

Introducing paper: Fast Kernel Density Estimation with Density Matrices and Random Fourier Features
FICLE
Full name: Factual Inconsistency CLassification with Explanation

The FICLE dataset is a derivative of the FEVER dataset, which is a collection of 185,445 claims generated by modifying sentences obtained from Wikipedia. These claims were then verified without knowledge of the original sentences they were derived from. Each sample in the FEVER dataset consists of a claim sentence, a context sentence extracted from a Wikipedia URL as evidence, and a type label indicating whether the claim is supported, refuted, or lacks sufficient information.

Introducing paper: Neural models for Factual Inconsistency Classification with Explanations