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How To Train a Seq2Seq Summarization Model Using “BERT” as Both Encoder and Decoder!! (BERT2BERT)
Latest   Machine Learning

How To Train a Seq2Seq Summarization Model Using “BERT” as Both Encoder and Decoder!! (BERT2BERT)

Last Updated on July 18, 2023 by Editorial Team

Author(s): Ala Alam Falaki

Originally published on Towards AI.

BERT is a well-known and powerful pre-trained “encoder” model. Let’s see how we can use it as a “decoder” to form an encoder-decoder architecture.

Photo by Aaron Burden on Unsplash

The Transformer architecture consists of two main building blocks — encoder and decoder components — which we stack on top of each other to form a seq2seq model. (You can read more about it in my previous story) It is generally hard to train a transformer-based model from scratch since it needs both large datasets and high GPU memory. So, there are numerous pre-trained models with different objectives in mind.

Firstly, the encoder… Read the full blog for free on Medium.

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