Gensim LDA Coherence Score Nan

I created a Gensim LDA Model as shown in this tutorial: https://www.machinelearningplus.com/nlp/topic-modeling-gensim-python/

lda_model = gensim.models.LdaMulticore(data_df['bow_corpus'], num_topics=10, id2word=dictionary, random_state=100, chunksize=100, passes=10, per_word_topics=True)

And it generates 10 topics with a log_perplexity of:

lda_model.log_perplexity(data_df[‘bow_corpus’]) = -5.325966117835991

But when I run the coherence model on it to calculate coherence score, like so:

coherence_model_lda = CoherenceModel(model=lda_model, texts=data_df['bow_corpus'].tolist(), dictionary=dictionary, coherence='c_v')
with np.errstate(invalid='ignore'):
    lda_score = coherence_model_lda.get_coherence()

My LDA-Score is nan. What am I doing wrong here?

Answer

Solved! Coherence Model requires the original text, instead of the training corpus fed to LDA_Model – so when i ran this:

coherence_model_lda = CoherenceModel(model=lda_model, texts=data_df['corpus'].tolist(), dictionary=dictionary, coherence='c_v')
with np.errstate(invalid='ignore'):
    lda_score = coherence_model_lda.get_coherence()

I got a coherence score of: 0.462

Hope this helps someone else making the same mistake. Thanks!