Omkar2118/datasets huggingface dataset:
WNUT 2020
Full name: WNUT-2020 Task 1 Overview: Extracting Entities and Relations from Wet Lab Protocols

The training and development dataset for our task was taken from previous work on wet lab corpus (Kulkarni et al., 2018) that consists of from the 623 protocols. We excluded the eight duplicate protocols from this dataset and then re-annotated the 615 unique protocols in BRAT (Stenetorp et al., 2012).

Introducing paper: WNUT-2020 Task 1 Overview: Extracting Entities and Relations from Wet Lab Protocols
selfie2anime

The selfie dataset contains 46,836 selfie images annotated with 36 different attributes. We only use photos of females as training data and test data. The size of the training dataset is 3400, and that of the test dataset is 100, with the image size of 256 x 256. For the anime dataset, we have firstly retrieved 69,926 animation character images from Anime-Planet1. Among those images, 27,023 face images are extracted by using an anime-face detector2. After selecting only female character images and removing monochrome images manually, we have collected two datasets of female anime face images, with the sizes of 3400 and 100 for training and test data respectively, which is the same numbers as the selfie dataset. Finally, all anime face images are resized to 256 x 256 by applying a CNN-based image super-resolution algorithm.

Source: [U-GAT-IT: UNSUPERVISED GENERATIVE ATTENTIONAL NETWORKS WITH ADAPTIVE LAYER-INSTANCE NORMALIZATION FOR IMAGE-TO-IMAGE TRANSLATION](https://arxiv.org/pdf/1907.10830v4.pdf)
.

Introducing paper: U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation
WNUT-2020 Task 2
Full name: WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets

Briefly describe the dataset. Provide:

* a high-level explanation of the dataset characteristics
* explain motivations and summary of its content
* potential use cases of the dataset

If the description or image is from a different paper, please refer to it as follows:
Source: [title](url)
Image Source: [title](url)

Introducing paper: WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets
DIR-LAB COPDgene
Full name: The Deformable Image Registration Laboratory

Inspiratory and exipratory breath-hold CT image pairs acquired from the National Heart Lung Blood Institute COPDgene study archive.
TCC

The largest and most realistic dataset available for TCC. It consists of 600 real-world videos recorded with a high-resolution mobile phone camera shooting 1824 x 1368 sized pictures. The length of these videos ranges from 3 to 17 frames (7.3 on average, the median is 7.0 and mode is 8.5). Ground truth information is present only for the last frame in each video (i.e., the shot frame), and was collected using a gray surface calibration target.

Introducing paper: A Benchmark for Temporal Color Constancy
USPTO-50k

Subset and preprocessed version of Chemical reactions from US patents (1976-Sep2016) by Daniel Lowe.
It includes 50K randomly selected reactions that was later classified into 10 reaction classes by Nadine Schneider et al.

Introducing paper: What’s What: The (Nearly) Definitive Guide to Reaction Role Assignment
Eduge
Full name: Eduge news classification dataset

Eduge news classification dataset provided by Bolorsoft LLC. Used to train the Eduge.mn production news classifier
75K news articles in 9 categories: урлаг соёл, эдийн засаг, эрүүл мэнд, хууль, улс төр, спорт, технологи, боловсрол and байгал орчин

Download train/test data via:

[train](https://storage.googleapis.com/eduge_dataset/eduge_train.csv)

[test](https://storage.googleapis.com/eduge_dataset/eduge_test.csv)