tf-nightly if present.wget.train-data.tsv: Training messagesvalid-data.tsv: Testing and validation messagesham or spam)ham → 0 (legitimate message)spam → 1 (spam message)<br /> using regular expressionsTextVectorization from Keras.max_tokens = 10,000: Vocabulary size limitsequence_length = 120: Fixed output length with padding and truncationoutput_mode = 'int': Convert words to integer indicesstandardize = custom_standardization: Apply text cleaning functiontrain_messages → train_sequencestest_messages → test_sequences"Free money now!"[42, 187, 933, 0, 0, ...] (padded to length 120).keras).spam_model.keraspredict_message(pred_text)vectorize_layer[probability_float, label_string][0.9234, 'spam'] or [0.1567, 'ham']test_predictions() function.Posted Dec 27, 2025
Built an SMS spam detector using deep learning and NLP techniques.
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Dec 20, 2025 - Dec 23, 2025