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. 2026 Jan 9;5:1733003. doi: 10.3389/fradi.2025.1733003

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© 2026 Matsuzaka and Iyoda.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Diagram of an encoder in a transformer neural network. It shows three connected blocks, each containing a self-attention layer in green and a feed-forward layer in blue. Arrows indicate data flow from input to decoder, with a legend explaining the layers' colors. — The architecture of the encoder in a transformer neural network. A transformer is a deep learning model that adopts the mechanism of self-attention, differently weighing the significance of each part of the input data. The encoder's job is to process the input data, such as a sentence in a source language, and convert it into a high-dimensional representation.