Datasets:
HenryAI
/
KerasBERTv1-Data

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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.