python
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
tokenizer = Tokenizer(num_words=1000, oov_token="<OOV>")
tokenizer.fit_on_texts(train_sentences)
word_index = tokenizer.word_index
train_sequences = tokenizer.texts_to_sequences(train_sentences)
train_padded = pad_sequences(train_sequences)
model = keras.Sequential([
keras.layers.Embedding(len(word_index)+1, 16, input_length=train_padded.shape[1]),
keras.layers.Bidirectional(keras.layers.LSTM(16)),
keras.layers.Dense(16, activation="relu"),
keras.layers.Dense(1, activation="sigmoid")
])
model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])
model.fit(train_padded, train_labels, epochs=10)
test_sequence = tokenizer.texts_to_sequences(test_sentence)
test_padded = pad_sequences(test_sequence, maxlen=train_padded.shape[1])
prediction = model.predict(test_padded)
print(prediction)
