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huolongshe d1a803c284 | 2 months ago | |
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run_model_server.py | 9 months ago |
中文分词任务就是把连续的汉字分隔成具有语言语义学意义的词汇。中文的书写方式不像英文等日耳曼语系语言词与词之前显式的用空格分隔。为了让计算机理解中文文本,通常来说中文信息处理的第一步就是进行文本分词。
本方法采用char-BiLSTM-CRF模型,word-embedding使用Chinese-Word-Vectors。序列标注标签体系(B、I、E、S),四个标签分别表示单字处理单词的起始、中间、终止位置或者该单字独立成词。char-BiLSTM-CRF模型具体结构可以参考论文 Neural Architectures for Named Entity Recognition
电商领域的分词训练数据基于电商搜索Query和标题数据标注得到, 对比通用领域分词模型, 主要提升对电商领域特有的品牌、品类、商品修饰等词汇的切分准确率.
模型来源: https://www.modelscope.cn/models/damo/nlp_lstmcrf_word-segmentation_chinese-ecommerce/summary
本模型基于 ServiceBoot微服务引擎 进行服务化封装,参见: 《CubeAI模型开发指南》
$ sh pip-install-reqs.sh
$ serviceboot start
或
$ python3 run_model_server.py
一键式本地容器化部署和运行,参见: 《CubeAI模型独立部署指南》 或 CubeAI Docker Builder
本模型服务可一键发布至 CubeAI智立方平台 进行共享和部署,参见: 《CubeAI模型发布指南》
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