|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Fashion Mnist||9,856||2 years ago||24||mit||Python|
|A MNIST-like fashion product database. Benchmark :point_down:|
|Nlp_chinese_corpus||8,344||6 months ago||20||mit|
|大规模中文自然语言处理语料 Large Scale Chinese Corpus for NLP|
|Clue||3,345||6 months ago||73||Python|
|中文语言理解测评基准 Chinese Language Understanding Evaluation Benchmark: datasets, baselines, pre-trained models, corpus and leaderboard|
|Benchmarking Gnns||2,196||5 months ago||6||mit||Jupyter Notebook|
|Repository for benchmarking graph neural networks|
|Deepmoji||1,462||3 months ago||10||mit||Python|
|State-of-the-art deep learning model for analyzing sentiment, emotion, sarcasm etc.|
|Codexglue||1,191||4 months ago||23||mit||C#|
|Beir||1,120||8||4 months ago||29||July 21, 2023||57||apache-2.0||Python|
|A Heterogeneous Benchmark for Information Retrieval. Easy to use, evaluate your models across 15+ diverse IR datasets.|
|Torchmoji||882||5 months ago||21||mit||Python|
|😇A pyTorch implementation of the DeepMoji model: state-of-the-art deep learning model for analyzing sentiment, emotion, sarcasm etc|
|Tdc||877||4||a month ago||32||January 27, 2023||32||mit||Jupyter Notebook|
|Therapeutics Data Commons: Artificial Intelligence Foundation for Therapeutic Science|
|Matterport||834||22 days ago||48||mit||C++|
|Matterport3D is a pretty awesome dataset for RGB-D machine learning tasks :)|
This benchmark library is curated and maintained by the IEEE PES Task Force on Benchmarks for Validation of Emerging Power System Algorithms and is designed to evaluate a well established version of the the AC Optimal Power Flow problem. This introductory video and detailed report present the motivations and goals of this benchmark library. In particular, these cases are designed for benchmarking algorithms that solve the following Non-Convex Nonlinear Program,
A detailed description of this mathematical model is available here. All of the cases files are curated in the MATPOWER data format. Open-source reference implementations are available in MATPOWER and PowerModels.jl and baseline results are reported in BASELINE.md.
These cases may also be useful for benchmarking the following variants of the Optimal Power Flow problem,
That said, these cases are curated with the AC Optimal Power Flow problem in mind. Application to other domains and problem variants should be done with discretion.
A forthcoming technical report will detail the sources, motivations, and procedures for curating these case files.
In this repository the network data files are organized into the following three broad groups:
All case files are provided under a Creative Commons Attribution License, which allows anyone to share or adapt these cases as long as they give appropriate credit to the orginal author, provide a link to the license, and indicate if changes were made.
Community-based recommendations and contributions are welcome and encouraged in all PGLib repositories. Please feel free to submit comments and questions in the issue tracker. Corrections and new network contributions are welcome via pull requests. All data contributions are subject to a quality assurance review by the repository curator(s).
This repository is not static. Consequently, it is critically important to indicate the version number when referencing this repository in scholarly work.
Users of this these cases are encouraged to cite the original source documents that are indicated in the file headers and the achrive report.