Invalid connections can occur in situations where there is a need to restrict the input sequence from attending to itself or certain parts of itself. For example, in a language modeling task, it may not be necessary or desirable for a word to attend to other words that appear after it in the same sentence. In such cases, the triangular mask created by the code can be used to ensure that the attention mechanism does not allocate any attention weight to invalid connections.

Invalid connections can also occur when dealing with variable-length input sequences, where padding is used to make all sequences the same length. In such cases, the padded elements should be masked out to prevent the model from attending to them.

Masking out invalid connections is a common occurrence in deep learning models that use attention mechanisms. It helps to ensure that the model attends only to relevant parts of the input sequence and produces accurate results.
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence