word2vec and fasttext操作

1 word2vec训练

#coding=utf-8
import jieba
import jieba.posseg as pseg
import smart_open
import xlrd
import codecs
from gensim.models import Word2Vec

# 读文件
def read_file():
    jieba.load_userdict("dic.txt")
    user_query_segment_list = []
    with open("07.txt", mode="r", encoding="utf-8") as f1:
        lines = f1.readlines()
        for line in lines:
            line = line.replace("\n", "").strip()
            every_query_list = []
            # print(line)
            words = pseg.cut(line)
            words_list = list(words)
            every_query_list = [element.word for element in words_list]
            user_query_segment_list.append(every_query_list)

    print(len(user_query_segment_list))


    with open("zhishiku.txt", mode="r", encoding="utf-8") as f1:
        lines = f1.readlines()
        for line in lines:
            line = line.replace("\n", "").strip()
            every_query_list = []
            # print(line)
            words = pseg.cut(line)
            words_list = list(words)
            every_query_list = [element.word for element in words_list]
            user_query_segment_list.append(every_query_list)
    print(len(user_query_segment_list))

    with open("seg_result.txt", mode="w", encoding="utf-8") as fw:
        for element in user_query_segment_list:
            temp = " ".join(list(element))
            temp = temp.strip()
            fw.writelines(temp + "\n")




    return user_query_segment_list



def export_to_file(model, output_file):
    output = codecs.open(output_file, 'w', 'utf-8')
    print('done loading Word2Vec')
    vocab = model.wv.vocab
    for mid in vocab:
        #print(model[mid])
        #print(mid)
        vector = list()
        for dimension in model[mid]:
            vector.append(str(dimension))
        #line = { "mid": mid, "vector": vector  }
        vector_str = " ".join(vector)
        line = mid + " "  + vector_str
        #line = json.dumps(line)
        output.write(line + "\n")
    output.close()

if __name__ == '__main__':
    user_query_list = read_file()
    # # user_query_list是list的list，里面是分好词的句子
    # model = Word2Vec(user_query_list, size=100, window=5, min_count=1, workers=4, iter=10)
    # # model.save('/tmp/MyModel')
    # # model.save_word2vec_format('mymodel2.bin', binary=True)
    # model.wv.save_word2vec_format('mymodel3.bin', binary=True)
    # # print(model.wv.most_similar("公积金"))
    # # export_to_file(model,"word2vec_by_gensim_ly_train_dev_test_0702.txt")

    # model = Word2Vec()
    # model.build_vocab(user_query_list)
    # model.train(user_query_list, total_examples = model.corpus_count, epochs = 10)
    # print(model.wv.most_similar("公积金"))
    # model.wv.save_word2vec_format('mymodel2.bin', binary=True)
    # # model.save('mymodel2.bin')
    # # https: // www.jianshu.com / p / 05fb666a72c4

2 word2vec使用

import gensim
from gensim.models import word2vec
model = gensim.models.KeyedVectors.load_word2vec_format("skipgram_200_mincount10.vec")
print(model.similarity("日期", "时间"))
print(model.similarity("日期", "延期"))

https://blog.csdn.net/qq_41814556/article/details/80990976

3 fasttext使用