Omkar2118/datasets huggingface dataset:
EBHI-Seg

**EBHI-Seg** is a dataset containing 5,170 images of six types of tumor differentiation stages and the corresponding ground truth images. The dataset can provide researchers with new segmentation algorithms for medical diagnosis of colorectal cancer.

Source: [EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Tasks](https://arxiv.org/pdf/2212.00532v1.pdf)

Image Source: [https://arxiv.org/pdf/2212.00532v1.pdf](https://arxiv.org/pdf/2212.00532v1.pdf)

Introducing paper: EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Tasks
Perseus

**Perseus** is a dataset for Cross-Lingual Summarization (CLS) which collects about 94K Chinese scientific documents paired with English summaries. The average length of documents in Perseus is more than two thousand tokens.

Source: [Long-Document Cross-Lingual Summarization](https://arxiv.org/pdf/2212.00586v1.pdf)

Image Source: [https://arxiv.org/pdf/2212.00586v1.pdf](https://arxiv.org/pdf/2212.00586v1.pdf)

Introducing paper: Long-Document Cross-Lingual Summarization
Super-CLEVR

**Super-CLEVR** is a dataset for Visual Question Answering (VQA) where different factors in VQA domain shifts can be isolated in order that their effects can be studied independently. It contains 21 vehicle models belonging to 5 categories, with controllable attributes. Four factors are considered: visual complexity, question redundancy, concept distribution and concept compositionality.

Source: [Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning](https://arxiv.org/pdf/2212.00259v1.pdf)

Image Source: [https://arxiv.org/pdf/2212.00259v1.pdf](https://arxiv.org/pdf/2212.00259v1.pdf)

Introducing paper: Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning
ArzEn
Full name: Corpus of Egyptian Arabic-English Code-switching

**Corpus of Egyptian Arabic-English Code-switching (ArzEn)** is a spontaneous conversational speech corpus, obtained through informal interviews held at the German University in Cairo. The participants discussed broad topics, including education, hobbies, work, and life experiences. The corpus currently contains 12 hours of speech, having 6,216 utterances. The recordings were transcribed and translated into monolingual Egyptian Arabic and monolingual English.

Source: [https://arxiv.org/pdf/2211.16319v1.pdf](https://arxiv.org/pdf/2211.16319v1.pdf)

Introducing paper: Benchmarking Evaluation Metrics for Code-Switching Automatic Speech Recognition
LEVEN
Full name: Legal Event Detection Dataset

## Overview
LEVEN is the **largest Legal Event Detection** dataset as well as the **largest Chinese Event Detection** dataset.

### Large Scale
LEVEN contains 8,116 legal documents, 63,616 sentences, and 150,977 event mentions.

### High Coverage
LEVEN contains 108 event types in total, including 64 charge-oriented events and 44 general events. 

## Miscs
LEVEN is adopted for the [Event Detection Track of CAIL 2022](http://cail.cipsc.org.cn/task1.html?raceID=1&cail_tag=2022).
Check out the LEVEN [repo](https://github.com/thunlp/LEVEN/) for more details.

Introducing paper: LEVEN: A Large-Scale Chinese Legal Event Detection Dataset