I have a list of strings as shown
sent_list = ["Carrefour is in France", "Apple pie is delicious", "Amazon has just delivered", ...]
My code to get word embeddings below
import spacy nlp = spacy.load("en_trf_bertbaseuncased_lg") for sent in sent_list: print(nlp(sent).vector)
This takes considerable time when the list is of large size (>10000). I tried disabling
sentencizer within the
nlp pipe but with not much improvement. How can this be optimized for shorter run time?