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machine-learning icon machine-learning

:zap:机器学习实战(Python3):kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归

marktool icon marktool

DoTAT 是一款基于web、面向领域的通用文本标注工具,支持大规模实体标注、关系标注、事件标注、文本分类、基于字典匹配和正则匹配的自动标注以及用于实现归一化的标准名标注,同时也支持迭代标注、嵌套实体标注和嵌套事件标注。标注规范可自定义且同类型任务中可“一次创建多次复用”。通过分级实体集合扩大了实体类型的规模,并设计了全新高效的标注方式,提升了用户体验和标注效率。此外,本工具增加了审核环节,可对多人的标注结果进行一致性检验、自动合并和手动调整,提高了标注结果的准确率。

mathmodel icon mathmodel

研究生数学建模,本科生数学建模、数学建模竞赛优秀论文,数学建模算法,LaTeX论文模板,算法思维导图,参考书籍,Matlab软件教程,PPT

ml-visuals icon ml-visuals

🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.

muse icon muse

A library for Multilingual Unsupervised or Supervised word Embeddings

mysupervisor_save icon mysupervisor_save

收集“导师评价”相关资源,及原“导师评价网”存档数据

nlp-progress icon nlp-progress

Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.

nlp_paper_study icon nlp_paper_study

该仓库主要记录 NLP 算法工程师相关的顶会论文研读笔记

nlper-arsenal icon nlper-arsenal

收录NLP竞赛策略实现、各任务baseline、相关竞赛经验贴(当前赛事、往期赛事、训练赛)、NLP会议时间、常用自媒体、GPU推荐等,持续更新中

nlpwebsite icon nlpwebsite

自然语言处理网站标注系统,采用Django框架写的Python Web

paddlenlp icon paddlenlp

Easy-to-use and powerful NLP library with awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications. Including Neural Search, Question Answering, Information Extraction and Sentiment Analysis end-to-end system.

pclue icon pclue

pCLUE: 1000000+多任务提示学习数据集

promptclue icon promptclue

PromptCLUE:大规模多任务Prompt预训练中文开源模型,支持全中文任务零样本学习

pytorch-nlu icon pytorch-nlu

Pytorch-NLU,一个中文文本分类、序列标注工具包,支持中文长文本、短文本的多类、多标签分类任务,支持中文命名实体识别、词性标注、分词等序列标注任务。 Ptorch NLU, a Chinese text classification and sequence annotation toolkit, supports multi class and multi label classification tasks of Chinese long text and short text, and supports sequence annotation tasks such as Chinese named entity recognition, part of speech tagging and word segmentation.

qa-survey-cn icon qa-survey-cn

北京航空航天大学大数据高精尖中心自然语言处理研究团队开展了智能问答的研究与应用总结。包括基于知识图谱的问答(KBQA),基于文本的问答系统(TextQA),基于表格的问答系统(TableQA)、基于视觉的问答系统(VisualQA)和机器阅读理解(MRC)等,每类任务分别对学术界和工业界进行了相关总结。

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