Golang RESTful Client for HanLP.中文分词 词性标注 命名实体识别 依存句法分析 语义依存分析 新词发现 关键词短语提取 自动摘要 文本分类聚类 拼音简繁转换 自然语言处理

Overview

Build Status Go Report Card GoDoc

gohanlp

中文分词 词性标注 命名实体识别 依存句法分析 语义依存分析 新词发现 关键词短语提取 自动摘要 文本分类聚类 拼音简繁转换 自然语言处理

HanLP 的golang 接口

  • 在线轻量级RESTful API
  • 仅数KB,适合敏捷开发、移动APP等场景。服务器算力有限,匿名用户配额较少
  • 支持基于等宽字体的可视化,能够直接将语言学结构在控制台内可视化出来

使用方式

安装

go get -u github.com/xxjwxc/[email protected]

使用

申请auth认证

https://bbs.hanlp.com/t/hanlp2-1-restful-api/53

文本形式

client := hanlp.HanLPClient(hanlp.WithAuth("你申请到的auth")) // auth不填则匿名
s, _ := client.Parse("2021年HanLPv2.1为生产环境带来次世代最先进的多语种NLP技术。阿婆主来到北京立方庭参观自然语义科技公司。",hanlp.WithLanguage("zh"))
fmt.Println(s)

对象形式

client := hanlp.HanLPClient(hanlp.WithAuth("你申请到的auth")) // auth不填则匿名
resp, _ := client.ParseObj("2021年HanLPv2.1为生产环境带来次世代最先进的多语种NLP技术。阿婆主来到北京立方庭参观自然语义科技公司。",hanlp.WithLanguage("zh"))
fmt.Println(resp)

更多调用API 请查看

options

更多信息

关于标注集含义,请参考《语言学标注规范》《格式规范》。我们购买、标注或采用了世界上量级最大、种类最多的语料库用于联合多语种多任务学习,所以HanLP的标注集也是覆盖面最广的。

训练你自己的领域模型

写深度学习模型一点都不难,难的是复现较高的准确率。下列代码展示了如何在sighan2005 PKU语料库上花6分钟训练一个超越学术界state-of-the-art的中文分词模型。

English | 文档 | 1.x版 | 论坛 | docker | ▶️ 在线运行

面向生产环境的多语种自然语言处理工具包,基于PyTorch和TensorFlow 2.x双引擎,目标是普及落地最前沿的NLP技术。HanLP具备功能完善、性能高效、架构清晰、语料时新、可自定义的特点。

借助世界上最大的多语种语料库,HanLP2.1支持包括简繁中英日俄法德在内的104种语言上的10种联合任务:分词(粗分、细分2个标准,强制、合并、校正3种词典模式)、词性标注(PKU、863、CTB、UD四套词性规范)、命名实体识别(PKU、MSRA、OntoNotes三套规范)、依存句法分析(SD、UD规范)、成分句法分析语义依存分析(SemEval16、DM、PAS、PSD四套规范)、语义角色标注词干提取词法语法特征提取抽象意义表示(AMR)。

量体裁衣,HanLP提供RESTfulnative两种API,分别面向轻量级和海量级两种场景。无论何种API何种语言,HanLP接口在语义上保持一致,在代码上坚持开源。

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