Transformer-based Encoder-Decoder Models — inference with HuggingFace Transformers

AI Impact Summary

This document formalizes the transformer-based encoder–decoder architecture and frames inference as the primary deployment path. It highlights pre-trained models (T5, Bart, MarianMT, Pegasus, ProphetNet, Marge) and the HuggingFace Transformers ecosystem with SentencePiece as practical tooling for seq2seq tasks. The emphasis on inference over training indicates a shift toward rapid production deployment of translation, summarization, and other NLG capabilities using ready-made weights. Technical teams should plan integration of these models into existing inference pipelines to reduce development time and enable scalable NLP features.