Omkar2118/datasets huggingface dataset:
SWSR
Full name: Sina Weibo Sexism Review

The Sina Weibo Sexism Review (SWSR) dataset is a dataset to research online sexism in Chinese. The SWSR dataset provides labels at different levels of granularity including (i) sexism or non-sexism, (ii) sexism category and (iii) target type, which can be exploited, among others, for building computational methods to identify and investigate finer-grained gender-related abusive language.

Introducing paper: SWSR: A Chinese Dataset and Lexicon for Online Sexism Detection
iGibson 2.0

**iGibson 2.0** is an open-source simulation environment that supports the simulation of a more diverse set of household tasks through three key innovations. First, iGibson 2.0 supports object states, including temperature, wetness level, cleanliness level, and toggled and sliced states, necessary to cover a wider range of tasks. Second, iGibson 2.0 implements a set of predicate logic functions that map the simulator states to logic states like Cooked or Soaked. Additionally, given a logic state, iGibson 2.0 can sample valid physical states that satisfy it. This functionality can generate potentially infinite instances of tasks with minimal effort from the users. The sampling mechanism allows our scenes to be more densely populated with small objects in semantically meaningful locations. Third, iGibson 2.0 includes a virtual reality (VR) interface to immerse humans in its scenes to collect demonstrations.

Introducing paper: iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks
WebFG-496

**WebFG-496** is a dataset for fine-grained recognition that contains 200 subcategories of the "Bird" (Web-bird), 100 subcategories of the Aircraft" (Web-aircraft), and 196 subcategories of the "Car" (Web-car). It has a total number of 53339 web training images.

Introducing paper: Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach
WDC-Dialogue

**WDC-Dialogue** is a dataset built from the Chinese social media to train EVA. Specifically, conversations from various sources are gathered and a rigorous data cleaning pipeline is designed to enforce the quality of WDC-Dialogue. 

The dataset mainly focuses on three categories of textual interaction data, i.e., repost on social media, comment / reply on various online forums and online question and answer (Q&A) exchanges. Each round of these textual interactions yields a dialogue session via well-designed parsing rules.

Introducing paper: EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Training
InferWiki

**InferWiki** is a Knowledge Graph Completion (KGC) dataset that improves upon existing benchmarks in inferential ability, assumptions, and patterns. First, each testing sample is predictable with supportive data in the training set. Second, InferWiki initiates the evaluation following the open-world assumption and improves the inferential difficulty of the closed-world assumption, by providing manually annotated negative and unknown triples. Third, the dataset includes various inference patterns (e.g., reasoning path length and types) for comprehensive evaluation.

Introducing paper: Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion
Marine Debris Turntable

**Marine Debris Turntable** is a dataset for sonar perception.

Introducing paper: Pre-trained Models for Sonar Images
RareDis corpus

The **RareDis** corpus contains more than 5,000 rare diseases and almost 6,000 clinical manifestations are annotated. Moreover, the Inter Annotator Agreement evaluation shows a relatively high agreement (F1-measure equal to 83.5% under exact match criteria for the entities and equal to 81.3% for the relations). Based on these results, this corpus is of high quality, supposing a significant step for the field since there is a scarcity of available corpus annotated with rare diseases.

Introducing paper: The RareDis corpus: a corpus annotated with rare diseases, their signs and symptoms