Transformer 架構下的編碼器-解碼器（Encoder-Decoder）模型深度解析
Original: Transformer-based Encoder-Decoder Models
This classic blog post written by Hugging Face researcher Patrick von Platen takes a deep dive into the Transformer-based Encoder-Decoder…
本文為 Hugging Face 撰寫的經典技術指南，深入探討基於 Transformer 的編碼器-解碼器（Encoder-Decoder）架構。文章詳細解析了雙向編碼器、自迴歸解碼器以及兩者之間的交叉注意力機制（Cross-Attention），並介紹如何利用 Hugging Face `EncoderDecoderModel` 結合預訓練模型（如 BERT 與 GPT-2）來建構強大的序列到序列（Seq2Seq）模型。
This classic blog post written by Hugging Face researcher Patrick von Platen takes a deep dive into the Transformer-based Encoder-Decoder model architecture. This type of architecture — often referred to as sequence-to-sequence, or Seq2Seq models — plays a central role in tasks such as machine translation, text summarization, and question answering. Notable models in this family include T5, BART, and MarianMT.
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