nadiafelix
nadiafelix
@MaartenGR thanks a lot for you attention. I am trying this. But I found a sentence in topics set that doesn't exist in dictionary. Is it ok? Do all the...
Do you have any recommendations for working with this n_gram_range parameter? topic_model = BERTopic (verbose = True, embedding_model = embedder, **n_gram_range** = (1,3), calculate_probabilities = True)
> I believe it is best to make sure that the Countvectorizer in Bertopic should be the same as you used to create the dictionary, corpus and tokens. > >...
> > corpus > > I would suggest that instead of creating **n_grams** of the **corpus**, you can simply split the **n_grams** of the **topics** and flatten them to have...
I t > The following steps should be the correct ones in calculating the coherence scores. Some additional preprocessing is necessary since there is a very small part of that...
Great! Thanks a lot!
If P_wi is zero we have an error: ZeroDivisionError Traceback (most recent call last) in () 33 P_wi = freq_dist.freq(word) 34 ---> 35 pmi = np.log(P_wi_dj/(P_wi * P_dj)) 36 #print(f'pmi:{pmi}...