C# Sdcb.PaddleInference 中文分词、词性标注

C# Sdcb.PaddleInference 中文分词、词性标注

目录

效果

项目

代码

下载

参考


效果

项目

代码

using Sdcb.PaddleNLP.Lac;

using System;

using System.Collections.Generic;

using System.Data;

using System.Linq;

using System.Windows.Forms;

namespace C__Sdcb.PaddleInference_中文分词_词性标注

{

public partial class Form1 : Form

{

public Form1()

{

InitializeComponent();

}

ChineseSegmenter segmenter;

private void button1_Click(object sender, EventArgs e)

{

string input = "我是中国人,我爱我的祖国。";

textBox1.Text = input;

string\[\] result = segmenter.Segment(input);

textBox2.Text = string.Join(",", result);

}

private void Form1_Load(object sender, EventArgs e)

{

segmenter = new ChineseSegmenter();

}

private void button2_Click(object sender, EventArgs e)

{

string input = "我爱北京天安门";

textBox1.Text = input;

textBox2.Text = "";

WordAndTag\[\] result = segmenter.Tagging(input);

string labels = string.Join(",", result.Select(x => x.Label));

string words = string.Join(",", result.Select(x => x.Word));

string tags = string.Join(",", result.Select(x => x.Tag));

textBox2.Text += "words:" + words + "\r\n";

textBox2.Text += "labels:" + labels + "\r\n";

textBox2.Text += "tags" + tags + "\r\n";

}

private void button3_Click(object sender, EventArgs e)

{

string input = "我爱北京天安门";

textBox1.Text = input;

textBox2.Text = "";

Dictionary<string, WordTag?> customizedWords = new Dictionary<string, WordTag?>();

customizedWords.Add("北京天安门", WordTag.LocationName);

LacOptions lacOptions = new LacOptions(customizedWords);

ChineseSegmenter segmenter_custom = new ChineseSegmenter(lacOptions);

WordAndTag\[\] result = segmenter_custom.Tagging(input);

string labels = string.Join(",", result.Select(x => x.Label));

string words = string.Join(",", result.Select(x => x.Word));

string tags = string.Join(",", result.Select(x => x.Tag));

textBox2.Text += "words:" + words + "\r\n";

textBox2.Text += "labels:" + labels + "\r\n";

textBox2.Text += "tags" + tags + "\r\n";

}

}

}

复制代码
using Sdcb.PaddleNLP.Lac;
using System;
using System.Collections.Generic;
using System.Data;
using System.Linq;
using System.Windows.Forms;

namespace C__Sdcb.PaddleInference_中文分词_词性标注
{
    public partial class Form1 : Form
    {
        public Form1()
        {
            InitializeComponent();
        }

        ChineseSegmenter segmenter;

        private void button1_Click(object sender, EventArgs e)
        {
            string input = "我是中国人,我爱我的祖国。";
            textBox1.Text = input;
            string[] result = segmenter.Segment(input);
            textBox2.Text = string.Join(",", result);

        }

        private void Form1_Load(object sender, EventArgs e)
        {
            segmenter = new ChineseSegmenter();
        }

        private void button2_Click(object sender, EventArgs e)
        {
            string input = "我爱北京天安门";
            textBox1.Text = input;
            textBox2.Text = "";
            WordAndTag[] result = segmenter.Tagging(input);
            string labels = string.Join(",", result.Select(x => x.Label));
            string words = string.Join(",", result.Select(x => x.Word));
            string tags = string.Join(",", result.Select(x => x.Tag));

            textBox2.Text += "words:" + words + "\r\n";
            textBox2.Text += "labels:" + labels + "\r\n";
            textBox2.Text += "tags" + tags + "\r\n";
        }

        private void button3_Click(object sender, EventArgs e)
        {
            string input = "我爱北京天安门";
            textBox1.Text = input;
            textBox2.Text = "";

            Dictionary<string, WordTag?> customizedWords = new Dictionary<string, WordTag?>();
            customizedWords.Add("北京天安门", WordTag.LocationName);

            LacOptions lacOptions = new LacOptions(customizedWords);

            ChineseSegmenter segmenter_custom = new ChineseSegmenter(lacOptions);

            WordAndTag[] result = segmenter_custom.Tagging(input);
            string labels = string.Join(",", result.Select(x => x.Label));
            string words = string.Join(",", result.Select(x => x.Word));
            string tags = string.Join(",", result.Select(x => x.Tag));

            textBox2.Text += "words:" + words + "\r\n";
            textBox2.Text += "labels:" + labels + "\r\n";
            textBox2.Text += "tags" + tags + "\r\n";
        }
    }
}

下载

源码下载

参考

https://github.com/sdcb/PaddleSharp/blob/master/docs/paddlenlp-lac.md

相关推荐
zhenaibo52113 小时前
摘要、研究背景、文献综述AI风险高,怎么优化?
人工智能·深度学习·自然语言处理
男孩李2 天前
浅谈JiuwenSwarm安装
人工智能·语言模型·自然语言处理
寥落半伤感2 天前
codex接入deepseek+VLM视觉语言模型教程
人工智能·语言模型·自然语言处理·codex·deepseek
测开小菜鸟3 天前
从AI概念到LLM评估:全面解析大型语言模型的能力与评价
人工智能·语言模型·自然语言处理
网络工程小王3 天前
【HCIE-AI】4.NLP 核心任务与技术演进学习笔记
人工智能·深度学习·自然语言处理·nlp·transformer
小王不叫小王叭4 天前
Blip2:使用冻结图像编码器和大型语言模型的Bootstrapping图像预训练-2301
人工智能·语言模型·自然语言处理
AI人工智能+4 天前
通用表格识别:实现从非结构化图像到高保真数据的转换,提升业务流程自动化水平
深度学习·自然语言处理·ocr·表格识别
怦怦蓝4 天前
给AI装上“眼睛”:一文讲透视觉语言模型(VLM)
人工智能·语言模型·自然语言处理·vlm
manyingAi5 天前
AI漫剧制作全流程技术拆解:从NLP剧本解析到一致性角色生成
人工智能·自然语言处理
AI人工智能+5 天前
智能文档抽取系统通过“视觉感知+大模型认知“双引擎架构,实现非结构化文档的自动化处理
深度学习·计算机视觉·语言模型·自然语言处理·ocr·文档抽取