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Support generating representative docs when using online modelling #1618

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Support generating representative docs when using online modelling #1618

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12 changes: 9 additions & 3 deletions bertopic/_bertopic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3492,7 +3492,13 @@ def _extract_representative_docs(self,
# Calculate similarity
nr_docs = nr_repr_docs if len(selected_docs) > nr_repr_docs else len(selected_docs)
bow = self.vectorizer_model.transform(selected_docs)
ctfidf = self.ctfidf_model.transform(bow)
try:
ctfidf = self.ctfidf_model.transform(bow)
except ValueError:
repr_docs_ids.append([])
repr_docs_indices.append([])
continue

sim_matrix = cosine_similarity(ctfidf, c_tf_idf[index])

# Use MMR to find representative but diverse documents
Expand All @@ -3507,8 +3513,8 @@ def _extract_representative_docs(self,
doc_ids = [selected_docs_ids[index] for index, doc in enumerate(selected_docs) if doc in docs]
repr_docs_ids.append(doc_ids)
repr_docs.extend(docs)
repr_docs_indices.append([repr_docs_indices[-1][-1] + i + 1 if index != 0 else i for i in range(nr_docs)])
repr_docs_mappings = {topic: repr_docs[i[0]:i[-1]+1] for topic, i in zip(topics.keys(), repr_docs_indices)}
repr_docs_indices.append([(repr_docs_indices[-1][-1] if len(repr_docs_indices[-1]) != 0 else 0) + i + 1 if index != 0 else i for i in range(nr_docs)])
repr_docs_mappings = {topic: repr_docs[i[0]:i[-1]+1] if len(i) > 0 else [] for topic, i in zip(topics.keys(), repr_docs_indices)}

return repr_docs_mappings, repr_docs, repr_docs_indices, repr_docs_ids

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