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Updated docstring of get_documents_topics() #350

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Updated docstring of get_documents_topics() #350

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6 changes: 3 additions & 3 deletions top2vec/Top2Vec.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1607,10 +1607,10 @@ def get_documents_topics(self, doc_ids, reduced=False, num_topics=1):

Returns
-------
topic_nums: array of int, shape(len(doc_ids), num_topics)
doc_topics: array of int, shape(len(doc_ids), num_topics)
The topic number(s) of the document corresponding to each doc_id.

topic_score: array of float, shape(len(doc_ids), num_topics)
doc_dist: array of float, shape(len(doc_ids), num_topics)
Semantic similarity of document to topic(s). The cosine similarity
of the document and topic vector.

Expand All @@ -1623,7 +1623,7 @@ def get_documents_topics(self, doc_ids, reduced=False, num_topics=1):
['environment', 'warming', 'climate ... 'temperature'] <Topic 21>
...]

word_scores: array of shape(num_topics, 50)
topic_word_scores: array of shape(len(doc_ids),num_topics, 50)
For each topic the cosine similarity scores of the
top 50 words to the topic are returned.

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