文本表示方法--单词嵌入向量(word2vec)

今天药忘吃喽~ 2022-09-07 09:12 172阅读 0赞

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# 下载相应数据集
    # wget https://storage.googleapis.com/cluebenchmark/tasks/tnews_public.zip
    
    import pandas as pd
    import json
    import jieba
    
    
    ## 0.gesim词向量实战
    # 1.读取预处理的数据集
    # 2.训练
    # 3.结果
    
    # 1.1数据预处理
    def get_sentence(data_file):
        # 读取文件
        f = open(data_file, encoding='utf-8')
        reader = f.readlines()
        sentence = []
        for line in reader:
            line = json.loads(line.strip())
            sentence.append(line['sentence'])
        return sentence
    
    
    train_sentence = get_sentence(r'tnews_public/train.json')
    test_sentence = get_sentence(r'tnews_public/test.json')
    dev_sentence = get_sentence(r'tnews_public/dev.json')
    
    # 全量数据集
    train_data = train_sentence+test_sentence+dev_sentence
    train_data = [list(jieba.cut(sen))for sen in train_data]
    print(train_data)
    print(len(train_data))
    
    # 3.构建词向量模型
    from gensim.models.word2vec import  LineSentence
    from gensim.models import word2vec
    import gensim
    
    import logging
    
    logging.basicConfig(format='%(asctime)s:%(levelname)s:%(message)',level=logging.INFO)
    # 构建模型
    from gensim.models import FastText
    # print(help(FastText))
    """ sentences=None, corpus_file=None, sg=0, hs=0, vector_size=100, alpha=0.025, window=5, min_count=5, max_vocab_size=None, word_ngrams=1, sample=1e-3, seed=1, workers=3, min_alpha=0.0001, negative=5, ns_exponent=0.75, cbow_mean=1, hashfxn=hash, epochs=5, null_word=0, min_n=3, max_n=6, sorted_vocab=1, bucket=2000000, trim_rule=None, batch_words=MAX_WORDS_IN_BATCH, callbacks=(), max_final_vocab=None """
    # model = FastText(train_data,vector_size=4,window=3,min_count=1,epochs=10)
    
    """ sentences=None, corpus_file=None, vector_size=100, alpha=0.025, window=5, min_count=5, max_vocab_size=None, sample=1e-3, seed=1, workers=3, min_alpha=0.0001, sg=0, hs=0, negative=5, ns_exponent=0.75, cbow_mean=1, hashfxn=hash, epochs=5, null_word=0, trim_rule=None, sorted_vocab=1, batch_words=MAX_WORDS_IN_BATCH, compute_loss=False, callbacks=(), comment=None, max_final_vocab=None, """
    ## skip-gram与CBOW
    model = word2vec.Word2Vec(train_data,sg=1,workers=4,min_count=4,vector_size=200,epochs=1)
    
    # 查找一个最近的词
    print(model.wv.most_similar(['金融'],topn=10))
    
    model_save_path = 'word2vec.model'
    model.save(model_save_path)
    
    # 载入模型
    model = word2vec.Word2Vec.load(model_save_path)

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