the right way to make prediction using Spacy word vectors

Question

Im learning how to convert text into numbers for NLP problems and following a course Im learning about word vectors provided by Spacy package. the code works all fine from learning and evaluation but I have some problems regarding:

1. making prediction for new sentences, I cannot seems to make it work and most examples just fit the model then use X_test set for evaluation. ( Code below)

2. The person explaining stated that its bad( won't give good results) if I used

"" doc.vector over doc.vector.values

""

when trying both I don't see a difference, what is the difference between the two?

the example is to classify news title between fake and real

import spacy
import pandas as pd

df= pd.read_csv('Fake_Real_Data.csv')

print(df.head())
print(f"shape is: {df.shape}")


print("checking the impalance: \n ", df.label.value_counts())



df['label_No'] = df['label'].map({'Fake': 0, 'Real': 1})
print(df.head())




nlp= spacy.load('en_core_web_lg') # only large and medium model have word vectors


df['Text_vector'] = df['Text'].apply(lambda x: nlp(x).vector) #apply the function to EACH element in the column
print(df.head(5))



from sklearn.model_selection import train_test_split

X_train, X_test, y_train,y_test= train_test_split(df.Text_vector.values, df.label_No, test_size=0.2, random_state=2022)




x_train_2D= np.stack(X_train)
x_test_2D= np.stack(X_test)



from sklearn.naive_bayes import MultinomialNB

clf=MultinomialNB()

from sklearn.preprocessing import MinMaxScaler

scaler= MinMaxScaler()

scaled_train_2d= scaler.fit_transform(x_train_2D)
scaled_test_2d= scaler.transform(x_test_2D) 

clf.fit(scaled_train_2d, y_train)

from sklearn.metrics import classification_report

y_pred=clf.predict(scaled_test_2d)

print(classification_report(y_test, y_pred))