ENCODER-DECODER in Seq2Seq models is a game-changer for sequence ... Mastering this structure is foundational for state-of-the-art NLP applications! I would say, encoder-decoder structure allows ...

One of the first factors to consider is the length of your input and output sequences. If they are very long, you might need a more complex encoder-decoder architecture that can handle the long ...

like any other NLP task, we load the text data and perform pre-processing and also do a train-test split. The data needs some cleaning before being used to train our neural translation model. I ...

We used the architecture of Transformer Encoder-Decoder for the learning model, as it gives good results for numerous seq2seq tasks. The two configurations we experimented with were T5 and BART.

Seq2seq (abbreviation of sequence to sequence) model is a group of neural-network-based models. It usually consists of an encoder and a decoder. The encoder takes a sequence as input and produces an ...

There are a couple of drawbacks to code-focused LLMs: they often adopt an encoder-only or decoder-only architecture ... This pretraining uses a mixture of objectives — span denoising, decoder-only ...