<p><a href="https://github.com/jhlau/gensim" rel="noreferrer">This forked version of gensim</a> allows loading pre-trained word vectors for training doc2vec. <a href="https://github.com/jhlau/doc2vec/blob/master/train_model.py" rel="noreferrer">Here</a> you have an example on how to use it. The word vectors must be in the C-word2vec tool text format: one line per word vector where first comes a string representing the word and then space-separated float values, one for each dimension of the embedding.</p>

<p>This work belongs to a <a href="https://arxiv.org/abs/1607.05368" rel="noreferrer">paper</a> in which they claim that using pre-trained word embeddings actually helps building the document vectors. However I am getting <em>almost</em> the same results no matter I load the pre-trained embeddings or not.</p>

<p><strong>Edit:</strong> actually there is one remarkable difference in my experiments. When I loaded the pretrained embeddings I trained doc2vec for half of the iterations to get <em>almost</em> the same results (training longer than that produced worse results in my task).</p>
How to use Gensim doc2vec with pre-trained word vectors?
<p>I recently came across the doc2vec addition to Gensim. How can I use pre-trained word vectors (e.g. found in word2vec original website) with doc2vec?</p>

<p>Or is doc2vec getting the word vectors from the same sentences it uses for paragraph-vector training?</p>

<p>Thanks.</p>