Additional Question

[nabilariefm-hi]

Halo Joe, Thank you again for the coding and your last answer to my question. If you are glad, I want to ask some questions about this code.

1. What value do you use to evaluate the model? Is that perplexity? What is the meaning of the perplexity in LLDA?
2. What is the difference between llda.LldaModel, .training, and .inference? Which one did you use to build a model?
3. How much delta should be input?

[JoeZJH]

1. You can use any metric for classification problem to evaluate the model. You can enter "what is perplexity of language models" to search answer for "perplexity", such as https://stats.stackexchange.com/questions/129352/how-to-find-the-perplexity-of-a-corpus.
2. 'training' and 'inference' are two phases of ML, inference also called 'prediction', youcan build a model with training, and use a model to predict with inference.
3. 'delta' is a hyperparameter, you can use different value for different scenarios, you can find the representation from comments in model.py.

[nabilariefm-hi]

Thankyouuu very much for your answer☺ Really appreciate that you want to reply this

[nabilariefm-hi]

Hi Joe Im sorry if I am asking too much question. Just want to make sure what is the difference between iteration and times ini this code? Which one should I change if I want to make a better accuration?
Thankyouu so muchh

topics = llda_model.inference(document=document, iteration=100, times=10)
print(topics)