Doc2vec: How to get document vectors

If you want to train Doc2Vec model, your data set needs to contain lists of words (similar to Word2Vec format) and tags (id of documents). It can also contain some additional info (see for more information).

# Import libraries

from gensim.models import doc2vec
from collections import namedtuple

# Load data

doc1 = ["This is a sentence", "This is another sentence"]

# Transform data (you can add more data preprocessing steps) 

docs = []
analyzedDocument = namedtuple('AnalyzedDocument', 'words tags')
for i, text in enumerate(doc1):
    words = text.lower().split()
    tags = [i]
    docs.append(analyzedDocument(words, tags))

# Train model (set min_count = 1, if you want the model to work with the provided example data set)

model = doc2vec.Doc2Vec(docs, size = 100, window = 300, min_count = 1, workers = 4)

# Get the vectors


UPDATE (how to train in epochs):
This example became outdated, so I deleted it. For more information on training in epochs, see this answer or @gojomo’s comment.

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