SOTA是state of the art的缩写，指某特定时间背景下的最高水准。 例如，SOTA model 最先进的模型。 benchmark通常指的是一个（标准的）衡量规定或是评估标准。

This repository provides state of the art (SoTA) results for all machine learning problems. We do our best to keep this repository up to date. If you do find a problem's SoTA result is out of date or missing, please raise this as an issue or submit Google form (with this information: research paper name, dataset, metric, source code and year). W…

High-quality implementations of standard and SOTA methods on a variety of tasks. - google/uncertainty-baselines

2025年1月24日 · Open-source industrial-grade ASR models supporting Mandarin, Chinese dialects and English, achieving a new SOTA on public Mandarin ASR benchmarks, while also offering outstanding singing lyrics recognition capability.

先回答问题，不一定需要sota效果。其实前面几个回答都提到了讲故事的能力了。就我个人的观点而言，恰好最近读了论文《Concatenated Power Mean Word Embeddings as Universal Cross-Lingual Sentence Representations》（sentence embedding相关），传统的句子embedding的简单做法是直接对句子中所有单词的embedding进行相加平均 ...

一句话回答：现有SOTA Embedding方法一般都结合了LLM. 本文将介绍我们的LLM&Embedding综述的内容，将从主要思想、数据增强、模型设计和任务类型，未来挑战多个部分对LLM如何结合Embedding进行讲解，更多细节欢迎阅读Arxiv论文！

This repository offers a growing collection of computer vision tutorials. Learn to use SOTA models like YOLOv11, SAM 2, Florence-2, PaliGemma 2, and Qwen2.5-VL for tasks ranging from object detection, segmentation, and pose estimation to data extraction and OCR. Dive in and explore the exciting world of computer vision!

1. SOTA(State-of-the-art): - MuZero:Google DeepMind在2020年提出的基于蒙特卡洛树搜索和神经网络的强化学习算法,在多个棋盘游戏和电子游戏中都取得了极强的超人水平表现。 - AlphaStar:DeepMind开发的星际争霸II AI算法,通过自我对弈进行持续学习,战胜了顶级玩家。

2025年1月22日 · A Non-Autoregressive Transformer based Text-to-Speech, supporting a family of SOTA transformers with supervised and unsupervised duration modelings. This project grows with the research community, aiming to achieve the ultimate TTS

Easy integration with SOTA backbone models (with tutorials) Tutorial for custom dataset; Distributed training; Current features to be discarded: Amount of datasets provided will be reduced. But instead, representative ones will be remained with a tutorial for custom dataset. Amount of models provided will be reducted.