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@shayaf84
Created February 19, 2022 23:25
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#Convert each of the testing data series to a Word2Vec embedding
test = []
for i in title_test:
temp = np.array(embed(i))
test.append(temp)
#Accounts for the different length of words in test data
test = tf.keras.preprocessing.sequence.pad_sequences(test,dtype='float')
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