python-kerasHow can I use the Python Keras Tokenizer to preprocess text data?

The Python Keras Tokenizer is a text preprocessing tool that can be used to prepare text data for use in neural network models.

To use the Tokenizer, first import the Tokenizer class from the Keras library:

from keras.preprocessing.text import Tokenizer

Then create an instance of the Tokenizer class:

tokenizer = Tokenizer()

Next, fit the Tokenizer to the text data:

tokenizer.fit_on_texts(text_data)

Where text_data is a list of strings containing the text data to be tokenized.

The Tokenizer can then be used to transform the text data into numerical vectors:

x = tokenizer.texts_to_sequences(text_data)

Where x is a list of numerical vectors, each representing a single text data point.

Finally, the numerical vectors can be padded to a uniform length, if necessary:

from keras.preprocessing.sequence import pad_sequences
x = pad_sequences(x)

Where x is now a 2D array of numerical vectors, all of uniform length.

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