How are tokenization tasks handled in Keras?

In Keras, handling tokenization tasks typically involves using the Tokenizer class, which is used to convert text data into integer sequences. The following are the main steps for handling tokenization tasks.

1. Instantiate a Tokenizer object and fit it to the training data.

from keras.preprocessing.text import Tokenizer

tokenizer = Tokenizer()
tokenizer.fit_on_texts(train_texts)

1. Convert text data to integer sequence.

train_sequences = tokenizer.texts_to_sequences(train_texts)
test_sequences = tokenizer.texts_to_sequences(test_texts)

1. Pad the sequence of integers to ensure they have the same length.

from keras.preprocessing.sequence import pad_sequences

max_len = 100
train_sequences_padded = pad_sequences(train_sequences, maxlen=max_len)
test_sequences_padded = pad_sequences(test_sequences, maxlen=max_len)

1. Build a model and train it.

from keras.models import Sequential
from keras.layers import Embedding, LSTM, Dense

model = Sequential()
model.add(Embedding(input_dim=num_words, output_dim=embedding_dim, input_length=max_len))
model.add(LSTM(units=64))
model.add(Dense(units=num_classes, activation='softmax'))

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(train_sequences_padded, train_labels, epochs=10, batch_size=32)

1. Predicting on test data and evaluating model performance:

predictions = model.predict(test_sequences_padded)

These are the basic steps for handling tokenization tasks, and you can adjust and expand them according to specific requirements and datasets.