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Allow chained main representation and aspects simultaneously #2021

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Allow chained main representation and aspects simultaneously #2021

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6 changes: 5 additions & 1 deletion bertopic/_bertopic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4071,7 +4071,11 @@ def _extract_words_per_topic(self,
topics = self.representation_model.extract_topics(self, documents, c_tf_idf, topics)
elif isinstance(self.representation_model, dict):
if self.representation_model.get("Main"):
topics = self.representation_model["Main"].extract_topics(self, documents, c_tf_idf, topics)
if isinstance(self.representation_model["Main"], list):
for tuner in self.representation_model["Main"]:
topics = tuner.extract_topics(self, documents, c_tf_idf, topics)
else:
topics = self.representation_model["Main"].extract_topics(self, documents, c_tf_idf, topics)
topics = {label: values[:self.top_n_words] for label, values in topics.items()}

# Extract additional topic aspects
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