Omkar2118/datasets huggingface dataset:
RiddleSense

Question: I have five fingers but I am not alive. What am I? Answer: a glove.

Answering such a riddle-style question is a challenging cognitive process, in that it requires complex commonsense reasoning abilities, an understanding of figurative language, and counterfactual reasoning skills, which are all important abilities for advanced natural language understanding (NLU). However, there is currently no dedicated datasets aiming to test these abilities. Herein, we present RiddleSense, a new multiple-choice question answering task, which comes with the first large dataset (5.7k examples) for answering riddle-style commonsense questions. We systematically evaluate a wide range of models over the challenge, and point out that there is a large gap between the best-supervised model and human performance — suggesting intriguing future research in the direction of higher-order commonsense reasoning and linguistic creativity towards building advanced NLU systems.

Introducing paper: RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge
Diamante

Diamante is a novel and efficient framework consisting of a data collection strategy and a learning method to boost the performance of pre-trained dialogue models. Two kinds of human feedback are collected and leveraged in Diamante, including explicit demonstration and implicit preference. The Diamante dataset is publicly available at the [LUGE platform](https://www.luge.ai/#/luge/dataDetail?id=52).

Introducing paper: Towards Boosting the Open-Domain Chatbot with Human Feedback
GD
Full name: Gaze-Detection

These images were generated using UnityEyes simulator, after including essential eyeball physiology elements and modeling binocular vision dynamics. The images are annotated with head pose and gaze direction information, besides 2D and 3D landmarks of eye's most important features. Additionally, the images are distributed into eight classes denoting the gaze direction of a driver's eyes (TopLeft, TopRight, TopCenter, MiddleLeft, MiddleRight, BottomLeft, BottomRight, BottomCenter). This dataset was used to train a DNN model for estimating the gaze direction. The dataset contains 61,063 training images, 132,630 testing images and additional 72,000 images for improvement.

Introducing paper: Supporting DNN Safety Analysis and Retraining through Heatmap-based Unsupervised Learning
eLife
Full name: Scientific Lay Summarization

This dataset contains 4,828 full biomedical articles paired with non-technical lay summaries derived from the eLife scientific journal.

Introducing paper: Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature
PLOS
Full name: Scientific Lay Summarization

This dataset contains 27,525 full biomedical articles paired with non-technical lay summaries derived from various journals published by the Public Library of Science (PLOS).

Introducing paper: Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature
Mint
Full name: Multilingual Intimacy analysis

**Mint** is a new Multilingual intimacy analysis dataset covering 13,384 tweets in 10 languages including English, French, Spanish, Italian, Portuguese, Korean, Dutch, Chinese, Hindi, and Arabic. The dataset is released along with the SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis.

Introducing paper: SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis
DA$^2$
Full name: Dual-Arm Dexterity-Aware

**DA2** is a large-scale Dual-Arm Dexterity-Aware (DA2) grasp dataset, including a total of about 9M parallel-jaw grasp pairs for more than 6000 different meshes. The grasp pairs are labeled with multiple grasp dexterity measures by fully analyzing the grasp matrix. The dataset constitutes a standardized data source filling the gap in vision-guided dual-arm grasping of arbitrary objects.

Introducing paper: DA$^2$ Dataset: Toward Dexterity-Aware Dual-Arm Grasping