Encoder-Decoder Mechanism
The process of translating a sentence involves a detailed encoder-decoder mechanism, where the encoder transforms words into contextually rich vectors through several layers, including positional encoding and self-attention. The decoder then generates the translated output word by word, utilizing the rich representations from the encoder while maintaining the context of previously generated words. This intricate interplay highlights the complexities and capabilities of modern language models in understanding and generating human language.In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
759: Full Encoder-Decoder Transformers Fully Explained — with Kirill Eremenko
Related Questions
How do vector embeddings work in the context of the episode 747: Technical Intro to Transformers and LLMs — with Kirill Eremenko and the clip Word Embeddings Explained?
How do vector embeddings work in the context of the episode 747: Technical Intro to Transformers and LLMs — with Kirill Eremenko and the clip Understanding Q, K, V Vectors?
How does this language model work?