Keras Text Vectorization Layer: Configure, Adapt, Use, Save, Load, and Deploy
Author: Murat Karakaya
Date created: 05 Oct 2021
Last modified: 18 March 2023
Description: This is a tutorial about how to build, adapt, use, save, load, and deploy the Keras TextVectorization layer. You can access this tutorial on YouTube in English and Turkish. “TensorFlow Keras Text Vectorization Katmanı” / “TensorFlow Keras Text Vectorization Layer”.
In this tutorial, we will download a Kaggle Dataset in which there are 32 topics and more than 400K total reviews. We will use this dataset for a multi-class text classification task.
Our main aim is to learn how to effectively use the Keras TextVectorization layer in Text Processing and Text Classification.
The tutorial has 5 parts:
- PART A: BACKGROUND
- PART B: KNOW THE DATA
- PART C: USE KERAS TEXT VECTORIZATION LAYER
- PART D: BUILD AN END-TO-END MODEL
- PART E: DEPLOY END-TO-END MODEL TO HUGGINGFACE SPACES USING GRADIO
- SUMMARY
At the end of this tutorial, we will cover:
- What a Keras
TextVectorizationlayer is - Why we need to use a Keras
TextVectorizationlayer in Natural Language Processing (NLP) tasks - How to employ a Keras
TextVectorizationlayer in Text Preprocessing - How to integrate a Keras
TextVectorizationlayer to a trained model - How to save and load a Keras
TextVectorizationlayer and a model with a KerasTextVectorizationlayer - How to integrate a Keras
TextVectorizationlayer with TensorFlow Data Pipeline API (tf.data) - How to design, train, save, and load an End-to-End model using Keras
TextVectorizationlayer - How to deploy the End-to-End model with a Keras
TextVectorizationlayer implemented with a custom standardize (custom_standardization) function using the Gradio library and the HuggingFace Spaces
Accessible on:
