The new TextVectorization layer in Keras enables you to put your text preprocessing directly inside your models, in a way that is automatically serializable and deployable. You won't have to worry about separately keeping track of your vocabulary again. Here's how to use it:

New Keras feature: the TextVectorization layer. It takes as input strings and takes care of text standardization, tokenization, and vocabulary indexing. This enables you to create models that process raw strings. End-to-end text classification example:

Using pre-trained word embeddings with Keras: Feat. TextVectorization layer

Going further, you can even specify your own preprocessing layers. Let's say one of your feature is a text paragraph, and you want to encode it as a TF-IDF vector to be concatenated with your other features. Easy! Create a TextVectorization layer and pass it to the FeatureSpace.

The TextVectorization in tf.keras enables you to do text tokenization and token indexing, to turn text into sequences of integers or into a categorical of bags of ngrams. Guide:

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Tensorflow TextVectorization layer: How to define a custom standardize function? #numpy #python #keras #tensorflow #text

I almost forgot to share this tutorial on multiclass emotion classification using TextVectorization -- an experimental feature in TensorFlow 2.1.0. 🧠➡️😃☹️. I adopted @fchollet tutorial and show how to use your own dataset to train the text classifier.

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2.1 is out 👍. Notable features:. - The experimental TextVectorization layer allows you to include your text processing logic inside your model (for cleaner deployment & serialization). - The standard TF pip package now includes GPU support by default .

RT fchollet: New Keras feature: the TextVectorization layer. It takes as input strings and takes care of text standardization, tokenization, and vocabulary indexing. This enables you to create models that process raw strings. End-to-end text classifica…

RT fchollet: The new TextVectorization layer in Keras enables you to put your text preprocessing directly inside your models, in a way that is automatically serializable and deployable. You won't have to worry about separately keeping track of your vocab…

@magistrapta kalau dari dokumentasinya, alternative nya pake tf.keras.layers.TextVectorization. source:

You should try the new TensorFlow’s TextVectorization layer.

NLP Newsletter: Tokenizers, TensorFlow 2.1, TextVectorization, TorchIO, NLP Shortfalls,… #ML #AI

Pretrained Word Embeddings using SpaCy and Keras TextVectorization

tf.keras in ⁦@TensorFlow⁩ 2.1 adds TextVectorization layer to flexibly map raw strings to tokens/word pieces/ngrams/vocab. An image is just a matrix of numbers but text always needs extra work and it‘s cleaner having preprocessing inside the model 👍

Sweet @TensorFlow 2.1.0-rc1 is out! Notable additions: easier GPU support, keras generators now run eagerly, experimental support for TPUs with keras, new TextVectorization layer for all your text preprocessing bundled together, and more! All the deets:

I'm really liking the new TextVectorization layer. Definitely feels like the future of deployment. Packaging preprocessing logic inside your model simplifies serving & converting Python models to other languages like TF.js. Links:

tensorflow 2 TextVectorization process tensor and dataset error #tensorflow #dataset #tensor