<p>When using a <code>size=100</code>, there are <em>not</em> "100 vectors" per text example – there is <em>one</em> vector, which includes 100 scalar dimensions (each a floating-point value, like <code>0.513</code> or <code>-1.301</code>). </p>

<p>Note that the values represent points in 100-dimensional space, and the individual dimensions/axes don't have easily-interpretable meanings. Rather, it is only the <em>relative distances</em> and <em>relative directions</em> between individual vectors that have useful meaning for text-based applications, such as assisting in information-retrieval or automatic classification.</p>

<p>The method for computing the vectors is described in the paper <a href="https://arxiv.org/abs/1405.4053" rel="nofollow noreferrer">'Distributed Representation of Sentences and Documents' by Le &amp; Mikolov</a>. But, it is closely associated to the 'word2vec' algorithm, so understanding that 1st may help, such as via its <a href="https://arxiv.org/abs/1301.3781" rel="nofollow noreferrer">first</a> and <a href="https://arxiv.org/abs/1310.4546" rel="nofollow noreferrer">second</a> papers. If that style of paper isn't your style, queries like <code>[word2vec tutorial]</code> or <code>[how does word2vec work]</code> or <code>[doc2vec intro]</code> should find more casual beginning descriptions.    </p>
How are vectors calculated in doc2vec and what does the size parameter depict?
<p>If I pass a Sentence containing 5 words to the Doc2Vec model and if the size is 100, there are 100 vectors. I'm not getting what are those vectors. If I increase the size to 200, there are 200 vectors for just a simple sentence. Please tell me how are those vectors calculated.</p>