from langchain.text_splitter import CharacterTextSplitter import re from typing import List class AliTextSplitter(CharacterTextSplitter): def __init__(self, pdf: bool = False, **kwargs): super().__init__(**kwargs) self.pdf = pdf def split_text(self, text: str) -> List[str]: # use_document_segmentation参数指定是否用语义切分文档,此处采取的文档语义分割模型为达摩院开源的nlp_bert_document-segmentation_chinese-base,论文见https://arxiv.org/abs/2107.09278 # 如果使用模型进行文档语义切分,那么需要安装modelscope[nlp]:pip install "modelscope[nlp]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html # 考虑到使用了三个模型,可能对于低配置gpu不太友好,因此这里将模型load进cpu计算,有需要的话可以替换device为自己的显卡id if self.pdf: text = re.sub(r"\n{3,}", r"\n", text) text = re.sub('\s', " ", text) text = re.sub("\n\n", "", text) from modelscope.pipelines import pipeline p = pipeline( task="document-segmentation", model='damo/nlp_bert_document-segmentation_chinese-base', device="cpu") result = p(documents=text) sent_list = [i for i in result["text"].split("\n\t") if i] return sent_list