The dataset that fixes embedding failure modes.

MTEB PairClassification tests whether a model can correctly judge whether two sentences mean the same thing. The failure cases are not random — they cluster around four structural patterns that standard corpora almost never include with the right label.

Role inversion ("The dog bit the man" vs. "The man bit the dog") looks similar lexically but is semantically opposite. Most training pairs don't capture this. Opinion asymmetry shares an entity but reverses polarity in a way cosine distance can't detect without geometric training signal.

Without explicit training on these patterns, embedding models learn to treat surface similarity as semantic similarity — exactly the failure that MTEB's PairClassification tasks expose.