The step-by-step processing of a sentence pair through a Transformer encoder can be visualized to understand sequence classification. Given a concatenated input sequence such as $[\mathrm{CLS}]$ It is raining . $[\mathrm{SEP}]$ I need an umbrella . $[\mathrm{SEP}]$, the procedure unfolds as follows: First, each input token $x_i$ is mapped to its corresponding embedding vector $\mathbf{e}_i$. Next, the entire sequence of embeddings ($\mathbf{e}_0, \dots, \mathbf{e}_{11}$) is fed into the encoder. The encoder then generates a corresponding sequence of contextualized output vectors ($\mathbf{h}_0, \dots, \mathbf{h}_{11}$). Finally, because the initial hidden state $\mathbf{h}_0$ acts as the aggregate representation of the entire sequence, a Softmax classification layer is applied directly to it to yield a binary prediction, such as 'Is Next or Not?'.