Given two text inputs, design and implement a method to compute their semantic similarity. You may use either of the following approaches: 1. Encode each sentence into a single embedding using a pretrained sentence encoder, then compute cosine similarity. 2. Convert each token to a word embedding, average the token embeddings for each sentence, then compute cosine similarity between the two averaged vectors. Your answer should describe: - How the text is preprocessed - How embeddings are produced - How cosine similarity is computed - How to handle empty text or unknown tokens - The trade-offs between sentence-level encoders and average word embeddings If coding is requested, provide clear pseudocode or implementation-level steps.