bert_binary_classifier.py

inp = tf.keras.Input(shape=(1,), dtype=tf.string)
encoder = BertLayer(bert_path="./bert-module/", seq_len=48, 
                    tune_embeddings=False, do_preprocessing=True,
                    pooling='cls', n_tune_layers=3, verbose=False)

pred = tf.keras.layers.Dense(1, activation='sigmoid')(encoder(inp))

model = tf.keras.models.Model(inputs=[inp], outputs=[pred])

model.summary()

model.compile(
      optimizer=tf.keras.optimizers.Adam(learning_rate=1e-5),
      loss="binary_crossentropy",
      metrics=["accuracy"])
      
saver = keras.callbacks.ModelCheckpoint("bert_tuned.hdf5")

model.fit(trX, trY, validation_data=[tsX, tsY], batch_size=128, epochs=5, callbacks=[saver])