LTP分词器python版本

# 以下是一个使用LTP中文分词器替换Whoosh默认分词器的Python脚本： # pythonCopy code import os import jieba from whoosh.analysis import Tokenizer, Token from whoosh.compat import u from whoosh import fields, index from whoosh.qparser import QueryParser from ltp import LTP import torch from settings import * # LTP添加自定义词典 f=open(LTP_user_dict_path,"r",encoding="utf-8") user_dicts=f.readlines() user_dicts=[user_dict.strip() for user_dict in user_dicts] ltp = LTP( "LTP/base2") ltp.add_words(user_dicts) # 将模型移动到 GPU 上 if torch.cuda.is_available(): ltp.to("cuda") # print("begin") # 定义LTP分词器 class LtpTokenizer(Tokenizer): def __call__(self, value, positions=False, chars=False, keeporiginal=False, removestops=True, start_pos=0, start_char=0, mode="", **kwargs): assert isinstance(value, str), "%r is not unicode" % value t = Token(positions, chars, removestops=removestops, mode=mode, **kwargs) segs = ltp.pipeline([value]).cws[0] # print("begin2") for w in segs: t.original = t.text = u(w) t.boost = 1.0 if positions: t.pos = start_pos + value.find(w) if chars: t.startchar = start_char + value.find(w) t.endchar = start_char + value.find(w) + len(w) yield t # 在上面的脚本中，我们首先导入了LTP中文分词器，并创建了一个自定义的LTPTokenizer类，该类用于将文本分成单词。