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Fix misleading Doc2Vec.docvecs comment #2472

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May 4, 2019
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Fix misleading Doc2Vec.docvecs comment #2472

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Fix misleading Doc2Vec.docvecs comment
gojomo May 1, 2019
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rm trailing whitespace
gojomo May 2, 2019
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rm more whitespace
gojomo May 3, 2019
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15 changes: 5 additions & 10 deletions gensim/models/doc2vec.py
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Expand Up @@ -447,18 +447,13 @@ class Doc2Vec(BaseWordEmbeddingsModel):
directly to query those embeddings in various ways. See the module level docstring for examples.

docvecs : :class:`~gensim.models.keyedvectors.Doc2VecKeyedVectors`
This object contains the paragraph vectors. Remember that the only difference between this model and
:class:`~gensim.models.word2vec.Word2Vec` is that besides the word vectors we also include paragraph embeddings
to capture the paragraph.
This object contains the paragraph vectors learned from the training data. There will be one such vector
for each unique document tag supplied during training. They may be individually accessed using the tag
as an indexed-access key. For example, if one of the training documents used a tag of 'doc003':

In this way we can capture the difference between the same word used in a different context.
For example we now have a different representation of the word "leaves" in the following two sentences ::

1. Manos leaves the office every day at 18:00 to catch his train
2. This season is called Fall, because leaves fall from the trees.
.. sourcecode:: pycon

In a plain :class:`~gensim.models.word2vec.Word2Vec` model the word would have exactly the same representation
in both sentences, in :class:`~gensim.models.doc2vec.Doc2Vec` it will not.
>>> model.docvecs['doc003']

vocabulary : :class:`~gensim.models.doc2vec.Doc2VecVocab`
This object represents the vocabulary (sometimes called Dictionary in gensim) of the model.
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