Popular GitHub Repositories for Match

搜索所有中文NLP数据集，附常用英文NLP数据集

推荐/广告/搜索领域工业界经典以及最前沿论文集合。A collection of industry classics and cutting-edge papers in the field of recommendation/advertising/search.

FreeAnchor: Learning to Match Anchors for Visual Object Detection (NeurIPS 2019)

ProphitBet is a Machine Learning Soccer Bet prediction application. It analyzes the form of teams, computes match statistics and predicts the outcomes of a match using Advanced Machine Learning (ML) methods. The supported algorithms in this application are Neural Networks, Random Forests & Ensembl Models.

ccf 2020 qa match competition top1

The easiest way to use Machine Learning. Mix and match underlying ML libraries and data set sources. Generate new datasets or modify existing ones with ease.

A blazing-fast semantic search React component. Match by meaning, not just by letters. Search as you type without waiting (no debounce needed). Rank by cosine similarity.

NLP research：基于tensorflow的nlp深度学习项目，支持文本分类/句子匹配/序列标注/文本生成 四大任务

Master Thesis: Limit order placement with Reinforcement Learning

Machine Learning + Financial Markets + Astrology = A Match made in Heaven

?? Perfect Match is a simple method for learning representations for counterfactual inference with neural networks.

高性能足球数据分析工具包：抓取数据、模拟比赛、排名球队，并更明智地下注 | pena.lt/y/blog 提供支持

Apply computer vision to table tennis for match / training analysis

Job search Agent (AI) searches and applies to jobs on your behalf, creating tailored applications for positions that match your skills, making your entire job search hassle-free.

Nucleic Acids Research 2024：RNA-MSM model is an unsupervised RNA language model based on multiple sequences that outputs both embedding and attention map to match different types of downstream tasks.

English to RegEx converter, powered by gpt-3.5-turbo and Vercel Edge Functions.

An easy python package to run quick basic QA evaluations. This package includes standardized QA evaluation metrics and semantic evaluation metrics: Black-box and Open-Source large language model prompting and evaluation, exact match, F1 Score, PEDANT semantic match, transformer match. Our package also supports prompting OPENAI and Anthropic API.

Win probability predictions for League of Legends matches using neural networks

GAN encoders in PyTorch that could match PGGAN, StyleGAN v1/v2, and BigGAN. Code also integrates the implementation of these GANs.

In this group project carried out with @Anannyap7, the aim is to take a professional badminton match video as an input and predict the most probable space on the court where the shot will be hit by the player on the near side of the court.