Using the pre-trained model:
import fasttext.util
fasttext.util.download_model('en', if_exists='ignore') # English
ft = fasttext.load_model('cc.en.300.bin')
both queries ft['HOME'] and ft['home'] works, but return different vectors.
What is the optimal query to make?
If I'm working with an uppercased corpus, should I transform it into a lowercased?