CongMa / textsplitter /ali_text_splitter.py
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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