The chemical language model FART, fine-tuned on a large corpus of labeled small molecule tastants, performs on par or superior compared to baseline tree-based ensemble methods. Enabled by the large training set of 15,025 labeled tastants collected in this work, FART is the first model capable of predictions across all four major taste categories (sweet, sour, bitter, umami) while accommodating other, including tasteless, molecules into a fifth, undefined category. While trained on multi-class taste prediction, FART outperforms state-of-the-art binary classifiers that specialize on predicting one taste class, e.g. sweet/non-sweet. FART gives robust and fast predictions based on chemical structure alone thereby avoiding the computational overhead of calculating physical or chemical descriptors.