Contents
How to train a text classification model in TensorFlow?
There are five steps to train a text classification model: Step 1. Choose a text classification model architecture. Here we use the average word embedding model architecture, which will produce a small and fast model with decent accuracy. Model Maker also supports other model architectures such as BERT.
Which is an example of Bert in TensorFlow?
It’s an example of predicting sentiment of movie reviews. I was so excited, for I learned BERT is now included in Tensorflow Hub. However, when I opened it, I found there are still too many details for a user who only cares about the application of text classification.
Which is the best model maker for TensorFlow?
TensorFlow Lite Model Maker currently supports MobileBERT, averaging word embeddings and BERT-Base models. Averaging text word embeddings with RELU activation. 4.3x smaller and 5.5x faster than BERT-Base while achieving competitive results, suitable for on-device applications.
How to use TensorFlow Lite in mobile app?
The TensorFlow Lite model file can be integrated in a mobile app using the BertNLClassifier API in TensorFlow Lite Task Library. Please note that this is different from the NLClassifier API used to integrate the text classification trained with the average word vector model architecture.
How to use transfer learning with TensorFlow hub?
The tutorial demonstrates the basic application of transfer learning with TensorFlow Hub and Keras. It uses the IMDB dataset that contains the text of 50,000 movie reviews from the Internet Movie Database. These are split into 25,000 reviews for training and 25,000 reviews for testing.
How many classes are there in TensorFlow core?
Found 25000 files belonging to 2 classes. Using 20000 files for training. As you can see above, there are 25,000 examples in the training folder, of which you will use 80% (or 20,000) for training. As you will see in a moment, you can train a model by passing a dataset directly to model.fit.