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- from langchain.text_splitter import CharacterTextSplitter
- import re
- from typing import List
- from modelscope.pipelines import pipeline
- p = pipeline(
- task="document-segmentation",
- model='damo/nlp_bert_document-segmentation_chinese-base',
- device="cpu")
- class ChineseTextSplitter(CharacterTextSplitter):
- def __init__(self, pdf: bool = False, **kwargs):
- super().__init__(**kwargs)
- self.pdf = pdf
- def split_text(self, text: str, use_document_segmentation: bool=False) -> 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,}", "\n", text)
- text = re.sub('\s', ' ', text)
- text = text.replace("\n\n", "")
- if use_document_segmentation:
- result = p(documents=text)
- sent_list = [i for i in result["text"].split("\n\t") if i]
- else:
- sent_sep_pattern = re.compile('([﹒﹔﹖﹗.。!?]["’”」』]{0,2}|(?=["‘“「『]{1,2}|$))') # del :;
- sent_list = []
- for ele in sent_sep_pattern.split(text):
- if sent_sep_pattern.match(ele) and sent_list:
- sent_list[-1] += ele
- elif ele:
- sent_list.append(ele)
- return sent_list
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