ljz2016的博客

训练并保存模型 def train_savemodel(): model = Word2Vec(PathLineSentences(directory), size=400, window=5, min_count=5, workers=multiprocessing.cpu_count(), sg=1, # 使用 skip-gram算法 ...

这篇主题是查询相似的文档 和前文一样，先把文档转换为向量表示 from collections import defaultdict from gensim import corpora documents = [ "Human machine interface for lab abc computer applications", "A survey of user opin...

训练模型，原始文本是使用jieba分词后的语料 model = Word2Vec(LineSentence('jieba_zhu1'), size=400, window=5, min_count=5, workers=multiprocessing.cpu_count()) model.save('model/zhu.model') model.wv.save_word2vec...

这篇文章演示如何把文本转换为向量表示，以及语料库文档流式处理并保存到硬盘上。 import logging from pprint import pprint from collections import defaultdict # 设置日志格式，日志级别 logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s'...