The premise of your question is wrong. A model that goes from a sequence to a single prediction is simply NOT called a sequence to sequence to model.
The model type you are describing is called a sequence encoder.
An example would be sentiment prediction, where we input a sequence of text and output a number.
Similarly, a model that goes from a fixed size value to a sequence is called a sequence decoder. An example would be image captioning.
If a model inputs one sequence and outputs another, as in machine translation, it is called a sequence to sequence model, and consists of both an encoder and a decoder.
If you saw different terminology, it was either mislabeled or misunderstood.