Sequential Ordering

This task evaluates how well a model's embeddings capture temporal information by measuring how well embeddings preserve sequential structure. First, a forward pass of the data is run through each model to retrieve embeddings. Then, the embeddings are used to fit a kNN and query the neighbors, with the intuition that if the embedding preserves sequence, then each point's nearest neighbors should have similar labels.