Word_embedding.py

# create word embeddings
import spacy
nlp = spacy.load('en_core_web_lg')

# create word embeddings
embedding_dimension = 300
embedding_matrix = np.zeros((vocab_len, embedding_dimension))

# travel through every word in vocabulary and get its corresponding vector
for word, index in tokenizer.word_index.items():
    doc = nlp(word)
    embedding_vector = np.array(doc.vector)
    embedding_matrix[index] = embedding_vector
    
# adding embeddings to model
predictive_model.layers[2]
predictive_model.layers[2].set_weights([embedding_matrix])
predictive_model.layers[2].trainable = False