Positional

An article explaining the intuition behind the “positional embedding” in transformer models from the renowned research paper - “Attention Is All You Need”.Table of ContentsIntroductionConcept of embedding in NLPNeed for positional embedding in TransformersVarious types of initial trial and error experimentsFrequency-based positional embeddingConclusionReferencesIntroductionThe introduction of transformer architecture in the field of deep learning undoubtedly has paved a way for the silent revolution, especially in the…

In languages the order of the words and their position in a sentence really matters. The meaning of the entire sentence can change if the words are re-ordered. When implementing NLP solutions, the recurrent neural networks have an inbuilt mechanism that deals with the order of sequences. The transformer model, however, does not use recurrence or convolution and treats each data point as independent of the other. Hence, positional information is added to the model explicitly to retain the information regarding the order of…

In part 1: A gentle introduction to positional encoding in transformer models, we discussed the positional encoding layer of the transformer model. We also showed how you can implement this layer and its functions yourself in Python. In this tutorial, we’ll implement the positional encoding layer in Keras and Tensorflow. You can then use this layer in a complete transformer model. After completing this tutorial, you will know: Text vectorization in Keras Embedding layer in Keras How to subclass the embedding layer…