Omkar2118/datasets huggingface dataset:
FM2
Full name: FoolMeTwice

FoolMeTwice (FM2 for short) is a large dataset of challenging entailment pairs collected through a fun multi-player game. Gamification encourages adversarial examples, drastically lowering the number of examples that can be solved using "shortcuts" compared to other popular entailment datasets. Players are presented with two tasks. The first task asks the player to write a plausible claim based on the evidence from a Wikipedia page. The second one shows two plausible claims written by other players, one of which is false, and the goal is to identify it before the time runs out. Players "pay" to see clues retrieved from the evidence pool: the more evidence the player needs, the harder the claim. Game-play between motivated players leads to diverse strategies for crafting claims, such as temporal inference and diverting to unrelated evidence, and results in higher quality data for the entailment and evidence retrieval tasks.

Introducing paper: Fool Me Twice: Entailment from Wikipedia Gamification
ContraCAT
Full name: Contrastive Coreference Analytical Templates (for Machine Translation)

Current approaches to context-aware MT rely on a set of surface heuristics to translate pronouns, which break down when translations require real reasoning.  We create a new template test set ContraCAT to assess the ability of Machine Translation to handle the specific steps necessary for successful pronoun translation.
MindReader

MindReader is a novel dataset providing explicit user ratings over a knowledge graph within the movie domain. The latest stable version of the dataset contains 218,794 ratings from 2,316 users over 12,206 entities entities, and an associated knowledge graph consisting of 18,133 movie-related entities. The dataset is collected from an online movie recommendation game, MindReader, where users are pseudo-randomly asked to provide preferences for both movie- and non-movie entities (e.g., genres, actors, and directors). For each entity, users can either like it, dislike it, or state that they do not know it.
SI-Score

**SI-SCORE** is a synthetic dataset for the analysis of robustness to object location, rotation and size. It consists of images that vary only for factors like object size and object location.

SI-SCORE was built by taking objects and backgrounds and systematically varying object size, location and rotation angle so that the effect of changing these factors on model performance can be studied.

Introducing paper: SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size
RLU
Full name: RL Unplugged

RL Unplugged is suite of benchmarks for offline reinforcement learning. The RL Unplugged is designed around the following considerations: to facilitate ease of use, we provide the datasets with a unified API which makes it easy for the practitioner to work with all data in the suite once a general pipeline has been established. This is a dataset accompanying the paper RL Unplugged: Benchmarks for Offline Reinforcement Learning.

In this suite of benchmarks, we try to focus on the following problems:

High dimensional action spaces, for example the locomotion humanoid domains, we have 56 dimensional actions.

High dimensional observations.

Partial observability, observations have egocentric vision.

Difficulty of exploration, using states of the art algorithms and imitation to generate data for difficult environments.

Real world challenges.

Introducing paper: RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning
Multifog KITTI dataset

we propose the augmented KITTI dataset with fog for both camera and LiDAR sensors with different visibility ranges from 20 to 80 meters to best match realistic fog environment.