我是NLP嵌入世界的新手 . 我使用了gensim的word2vec模型和tensorflow矢量表示 .
我有一个问题，在培训gensim 's word2vec model it takes tokenize sentences, while tensorflow takes a long list of words. How does it differ in training. Is there any quality impact? Also how does then tensorflow cater to the needs of skip-gram as now the data is a list of words and no more sentences. I am referring to the tensorflow'的教程发现链接https://www.tensorflow.org/tutorials/word2vec
如果我的理解得到澄清，如果我对这个领域的理解是错误的，请原谅我 .
感谢您的指导和帮助 .