注：本文主要是記錄自己常用的關於Doc2Vec的簡單程式程式碼。因此不做過多的解釋，直接寫出程式碼，如有問題可以討論交流。

一、doc2vec求文件向量

import sys
import numpy as np
import gensim
from gensim.models.doc2vec import Doc2Vec, LabeledSentence

TaggedDocument = gensim.models.doc2vec.TaggedDocument
#讀取並處理資料
def get_datatset(sentence):
    all_sentence = []    
    for i, sentence in enumerate(sentences):
        all_sentence.append(TaggedDocument(sentence.split(), tag=[i]))
    return all_sentence

#得到資料集corpus的文字向量        
def getVecs(model, corpus, vector_size):
    vecs = [np.array(model.docvecs[z.tags[0]].reshape(1, vector_size)) for z in corpus]
    return np.concatenate(vecs)

#用資料集的文字訓練模型
def train(all_sentence, vector_size, min_count, epoch):
    model = Doc2Vec(vector_size=vector_size, min_count=min_count, epochs=epoch)
    model.build_vocab(all_sentence)
    model.train(all_sentence, total_examples = model.corpus_count, epochs=model.epochs)
    return model

if __name__ == "__main__":
    sentence = open('sentence.txt','r').readlines()
    all_sentence = get_dataset(sentence)
    model = train(all_sentence, vector_size, min_count, epoch)
    sentence_vecs = getVecs(model, all_sentence, vector_size)
 

二、doc2vec求文字相似度

import sys
import gensim
import sklearn
import numpy as np
from gensim.models.doc2vec import Doc2Vec, LabeledSentence

TaggedDocument = gensim.models.doc2vec.TaggedDocument

#訓練部分同上

def similarity(model):
    test_text = 'xxx xxx xxxxx'.split()
    inferred_vector = model.infer_vector(test_text)
    sims = model.most_similar([inferred_vector], topn=10)
    return sims

if __name__ == '__main__':
    all_sentence = get_dataset(sentence)
    model = train(all_sentence, vector_size, min_count, epoch)
    sims = test()