Sequence transduction models can only be used for language translation tasks and cannot be applied to other types of sequence data such as time series or genomic sequences.

Sequence transduction models are built to map one sequence to another, so they work whenever you have an input sequence that must be turned into an output sequence, not just words in a sentence. They can process audio, stock prices, DNA strings, or any ordered data by learning patterns in the input and generating a related output. For example, a transformer can take a time‑series of temperature readings and output a forecasted sequence of future temperatures, or it can read a genomic sequence and predict a protein‑coding region. The only requirement is that the problem can be framed as input‑to‑output sequences, so these models are widely applicable beyond language translation.