I want to compare word2vec and fasttext model based on this comparison tutorial. https://github.com/jayantj/gensim/blob/fast_text_notebook/docs/notebooks/Word2Vec_FastText_Comparison.ipynb
According to this, the semantic accuracy of fastText model increase when we set the max length of char n-grams to zero, such that fastText starts to behave almost like to word2vec. It ignores the ngrams.
However, I can not find any formation on how to set this parameter while loading a fastText model. Any ideas on how to do this?
The parameter is set at training time – and then the model is built using that parameter, and dependent on that parameter for interpretation. So you wouldn't typically change it upon loading an already-trained model, and there's no API in gensim (or the original FastText) to change the setting on an already-trained model.
(By looking at the source and tampering with the loaded model state directly, you might be able to approximate the effect of ignoring char-ngrams that had been trained – but that'd be a novel mode, not at all like the no-ngrams-trained mode evaluated in the notebook you've linked. It might generate interesting, or awful, results – no way to tell without trying it.)