Omkar2118/datasets huggingface dataset:
CommitChronicle

**CommitChronicle** is a dataset for commit message generation (and/or completion).

Its key features:

* *large-scale and multilingual*: contains 10.7M commits from 11.9k GitHub repositories in 20 programming languages;
* *diverse*: avoids restrictive filtering on commit messages or commit diffs structure;
* *suitable for experiments with commit history*: provides metadata about commit authors and dates and uses split-by-project.

Available on 🤗 : [`JetBrains-Research/commit-chronicle`](https://huggingface.co/datasets/JetBrains-Research/commit-chronicle)

Introducing paper: From Commit Message Generation to History-Aware Commit Message Completion
MEIS
Full name: M-mode Echocardiograms for Instance Segmentation

MEIS comprises a total of 2,639 images in the size of 1024 × 768 toward two recording views (Aortic Valve (AV) and
Left Ventricle (LV)) with 1,521 (747 in AV + 774 in LV) images for training and 1,118 (559 in AV + 559 in LV) for
testing, respectively. Each view must be detected with two objects to calculate the measurement indicators. That is in
total with four object classes (two objects in each view): aortic root (AoR) and left atrium (LA) in AV; interventricular
septum (IVS) and left ventricular posterior wall (LVPW) in LV. The medical meaning and purpose of each indicator are
listed in the following:
• AV: LA-Dimension and AoR-Dimension can be measured for calculating different indicators, such as AoR/LA
ratio, to examine the state of the aortic valve.
• LV: 6 measurements include IVSs, IVSd, LVIDs, LVIDd, LVPWs, and LVPWd. These concerned thicknesses
and dimensions in LV recording are used to estimate other cardiac functions through specific medical formulas,
including LV mass, LV ejection fraction, end-diastolic volume, end-systolic volume, and more.

Introducing paper: Real-time Automatic M-mode Echocardiography Measurement with Panel Attention from Local-to-Global Pixels
ImageNet-Atr
Full name: ImageNet with Adversarial Text Regions

We build a new evaluation set by adding spotting words to the images of ImageNet 2012 evaluation sets. There are 1,000 categories in ImageNet. For each category c, we find its most confusing category c*and spot the category name to every evaluation image. 

This evaluation set is challenging for many CLIP models. For example, OpenAI CLIP B-16 got a top-1 accuracy of as low as 32%, which is much lower than the original ImageNet evaluation set.

Introducing paper: Less is More: Removing Text-regions Improves CLIP Training Efficiency and Robustness