In recent decades, a number of machine learning approaches have been suggested for molecular taste prediction, as has been helpfully reviewed elsewhere . Very recently, large language models such as OpenAI's GPT-3.5 and GPT-4 have been used for taste prediction although it is unclear how well these proprietary models generalize to molecules not contained in their training data . Fundamentally, the performance of machine learning methods is limited by the quality and size of the dataset available. Here we curated the largest, public dataset of 15,025 molecules and their associated taste labels, which is fully accessible in accordance with the FAIR principles . Using this dataset, we developed a chemical language model using the transformer architecture , named Flavor Analysis and Recognition Transformer (FART), which compares favorably to baseline machine learning methods in a multi-class setting. While FART is trained to predict four taste classes in parallel, it consistently outperforms models specifically designed for binary classification tasks, e.g. sweet/non-sweet or bitter/non-bitter. Central to the success of transformer models is the pre-training, fine-tuning paradigm . In natural language processing (NLP) for which the transformer architecture was originally developed, a model is first pre-trained on a large corpus of text allowing it to learn general language structures and semantic relationships. This broad foundation is then refined through fine-tuning on smaller, domain-specific datasets, adapting the model to excel at specialized tasks. Analogously, FART is based on a pre-trained foundation model for chemistry, ChemBERTa , which was then fine-tuned with the dataset presented here, see Fig. . Instead of natural language, a chemical language model takes SMILES (Simplified Molecular Input Line Entry System) as input, a text-based notation that encodes chemical structures into linear strings. FART is hence capable of predicting taste from chemical structure alone, obviating the need for further data collection from either experiments or computations.