text_plain_output_1.png
from tensorflow.keras import layers

import tensorflow as tf

import tensorflow as tf

vocab_size = 10000
max_length = 32
embedding_dim = 16
oov_token = '<oov>'
padding_type = 'post'
trunc_type = 'post'

model = tf.keras.models.Sequential([layers.Embedding(vocab_size, embedding_dim, input_length=max_length), layers.Flatten(), layers.Dense(32, activation='relu'), layers.Dropout(0.5), layers.Dense(10, activation='relu'), layers.Dropout(0.5), layers.Dense(1, activation='sigmoid')], name='sarcasm-detection-model')
model.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001), metrics=['accuracy'])

model.summary()