39 lines
1.8 KiB
Python
39 lines
1.8 KiB
Python
from langchain.text_splitter import CharacterTextSplitter
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import re
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from typing import List
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from modelscope.pipelines import pipeline
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p = pipeline(
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task="document-segmentation",
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model='damo/nlp_bert_document-segmentation_chinese-base',
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device="cpu")
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class ChineseTextSplitter(CharacterTextSplitter):
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def __init__(self, pdf: bool = False, **kwargs):
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super().__init__(**kwargs)
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self.pdf = pdf
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def split_text(self, text: str, use_document_segmentation: bool=False) -> List[str]:
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# 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
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# 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
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# 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
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if self.pdf:
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text = re.sub(r"\n{3,}", "\n", text)
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text = re.sub('\s', ' ', text)
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text = text.replace("\n\n", "")
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if use_document_segmentation:
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result = p(documents=text)
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sent_list = [i for i in result["text"].split("\n\t") if i]
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else:
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sent_sep_pattern = re.compile('([﹒﹔﹖﹗．。！？]["’”」』]{0,2}|(?=["‘“「『]{1,2}|$))') # del ：；
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sent_list = []
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for ele in sent_sep_pattern.split(text):
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if sent_sep_pattern.match(ele) and sent_list:
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sent_list[-1] += ele
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elif ele:
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sent_list.append(ele)
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return sent_list
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