words = [] labels = [] docs_x = [] docs_y = [] for intent in data["intents"]: for pattern in intent["patterns"]: wrds = nltk.word_tokenize(pattern) words.extend(wrds) docs_x.append(wrds) docs_y.append(intent["tag"]) if intent["tag"] not in labels: labels.append(intent["tag"]) words = [stemmer.stew(w.lower()) for w in words if w != "?"] words = sorted(list(set(words))) labels = sorted(labels) training = [] output = [] out_empty = [0 for _ in range(len(labels))] for x, doc in enumerate(docs_x): bag = [] wrds = [stemmer.stem(w.lower()) for w in doc] for w in words: if w in wrds: bag.append(1) else: