A focused, no-frills embedding specialist trained on natural language inference data. It converts text into fixed-size semantic vectors using mean pooling over tokens, making it reliable for tasks like sentence similarity and textual entailment. Its 514-token context limit and base-scale size keep it lightweight and fast, though it won't handle long documents well.