Update 06-lsa.md

Author: qualiaMachine

_episodes/06-lsa.md

@@ -279,7 +279,6 @@ We don't know *why* they are getting arranged this way, since we don't know what
 Let's write a helper to get the strongest words for each topic. This will show the terms with the *highest* and *lowest* association with a topic. In LSA, each topic is a spectra of subject matter, from the kinds of terms on the low end to the kinds of terms on the high end. So, inspecting the *contrast* between these high and low terms (and checking that against our domain knowledge) can help us interpret what our model is identifying.
 
 ```python
-def show_topics(topic, n):
 def show_topics(topic, n):
     # Get the feature names (terms) from the vectorizer
     terms = vectorizer.get_feature_names_out()