#!/usr/bin/env python3

import spacy

# ===  
# === model generated word vectors
# ===

#spacy.cli.download("en_core_web_lg")
nlp = spacy.load("en_core_web_lg")        # large model, dim = 300

vocabulary = ["apple", "banana", "cherry", "date", "elderberry"]

word_vectors = {}                         # dictionary for word vectors

for word in vocabulary:                   # word vectors
    token = nlp.vocab[word]
    word_vectors[word] = token.vector

print(word_vectors["apple"])
print()
print(len(word_vectors["apple"]))
print("# ==============================")

# === 
# === dynamic word vectors
# === 

nlp = spacy.load("en_core_web_sm")        # small, dynamic word vectors

sentence = "I like to eat an apple every morning."
doc = nlp(sentence)

for token in doc:
  print(f"{token.text:12s} | {len(token.vector):5d}")
# print(f"{token.text:12s} | {len(token.vector):5d} | {token.vector}")