close(if label_type ='neg'labels append( elselabels append( sequences tokenizer texts_to_sequences(textsx_test pad_sequences(sequencesmaxlen=maxleny_test np asarray(labelsnextload and evaluate the first model listing evaluat ing the model on he est set model load_weights('pre_trained_glove_model 'model evaluate(x_testy_testyou get an appalling test accuracy of working with just handful of training samples is difficultwrapping up now you're able to do the followingturn raw text into something neural network can process use the embedding layer in keras model to learn task-specific token embeddings use pretrained word embeddings to get an extra boost on small naturallanguage-processing problems licensed to