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text_vectorizer = preprocessing.TextVectorization(output_mode="int") |
# Index the vocabulary via `adapt()` |
text_vectorizer.adapt(data) |
# You can retrieve the vocabulary we indexed via get_vocabulary() |
vocab = text_vectorizer.get_vocabulary() |
print("Vocabulary:", vocab) |
# Create an Embedding + LSTM model |
inputs = keras.Input(shape=(1,), dtype="string") |
x = text_vectorizer(inputs) |
x = layers.Embedding(input_dim=len(vocab), output_dim=64)(x) |
outputs = layers.LSTM(1)(x) |
model = keras.Model(inputs, outputs) |
# Call the model on test data (which includes unknown tokens) |
test_data = tf.constant(["The Brain is deeper than the sea"]) |
test_output = model(test_data) |
Vocabulary: ['', '[UNK]', 'the', 'side', 'you', 'with', 'will', 'wider', 'them', 'than', 'sky', 'put', 'other', 'one', 'is', 'for', 'ease', 'contain', 'by', 'brain', 'beside', 'and'] |
You can see the TextVectorization layer in action, combined with an Embedding mode, in the example text classification from scratch. |
Note that when training such a model, for best performance, you should use the TextVectorization layer as part of the input pipeline (which is what we do in the text classification example above). |
Encoding text as a dense matrix of ngrams with multi-hot encoding |
This is how you should preprocess text to be passed to a Dense layer. |
# Define some text data to adapt the layer |
data = tf.constant( |
[ |
"The Brain is wider than the Sky", |
"For put them side by side", |
"The one the other will contain", |
"With ease and You beside", |
] |
) |
# Instantiate TextVectorization with "binary" output_mode (multi-hot) |
# and ngrams=2 (index all bigrams) |
text_vectorizer = preprocessing.TextVectorization(output_mode="binary", ngrams=2) |
# Index the bigrams via `adapt()` |
text_vectorizer.adapt(data) |
print( |
"Encoded text:\n", |
text_vectorizer(["The Brain is deeper than the sea"]).numpy(), |
"\n", |
) |
# Create a Dense model |
inputs = keras.Input(shape=(1,), dtype="string") |
x = text_vectorizer(inputs) |
outputs = layers.Dense(1)(x) |
model = keras.Model(inputs, outputs) |
# Call the model on test data (which includes unknown tokens) |
test_data = tf.constant(["The Brain is deeper than the sea"]) |
test_output = model(test_data) |
print("Model output:", test_output) |
Encoded text: |
[[1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 0. 0. 0. 0. 0. |
0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0.]] |
Model output: tf.Tensor([[0.53373265]], shape=(1, 1), dtype=float32) |
Encoding text as a dense matrix of ngrams with TF-IDF weighting |
This is an alternative way of preprocessing text before passing it to a Dense layer. |
# Define some text data to adapt the layer |
data = tf.constant( |
[ |
"The Brain is wider than the Sky", |
"For put them side by side", |
"The one the other will contain", |
"With ease and You beside", |
] |
) |
# Instantiate TextVectorization with "tf-idf" output_mode |
# (multi-hot with TF-IDF weighting) and ngrams=2 (index all bigrams) |
text_vectorizer = preprocessing.TextVectorization(output_mode="tf-idf", ngrams=2) |
# Index the bigrams and learn the TF-IDF weights via `adapt()` |
text_vectorizer.adapt(data) |
print( |
"Encoded text:\n", |
text_vectorizer(["The Brain is deeper than the sea"]).numpy(), |
"\n", |
) |
# Create a Dense model |
inputs = keras.Input(shape=(1,), dtype="string") |
x = text_vectorizer(inputs) |
outputs = layers.Dense(1)(x) |
model = keras.Model(inputs, outputs) |
# Call the model on test data (which includes unknown tokens) |
test_data = tf.constant(["The Brain is deeper than the sea"]) |
test_output = model(test_data) |
print("Model output:", test_output) |
Encoded text: |
[[5.461647 1.6945957 0. 0. 0. 0. 0. |
0. 0. 0. 0. 0. 0. 0. |
0. 0. 1.0986123 1.0986123 1.0986123 0. 0. |
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