Training and Evaluating GloVe
Explore how to effectively train and evaluate GloVe word embeddings with TensorFlow and Keras. Understand the use of validation callbacks, analyze training outputs for semantic grouping, and learn how to save embeddings for future NLP applications. This lesson helps you grasp the advantages of GloVe over other models and implement robust embedding techniques.
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Validation call back function
Training the model is effortless because we have all the components needed to train it. As the first step, we’ll reuse the ValidationCallback() we created. To recap, ValidationCallback() is a Keras callback. Keras callbacks give us a way to execute some important operations at the end of every training iteration, epoch, prediction step, etc. Here, we’re using the callback to perform a validation step at the end of every epoch. Our callback would take a list of word IDs intended as the validation words (held out in valid_term_ids), the model containing the embedding matrix, and a tokenizer to decode word IDs. Then it will compute ...