Given the successful application of large language models (LLM) in chemistry , we leverage the use of LLMs as text encoders for the NMR data . In particular, Spectro uses LLM2Vec to build a continuous representation of the 1 H and 13 C NMR spectra. The input prompt for each NMR consisted of the individual string of the 1 H and 13 C NMR text (Text 1-2), preceded by the instruction "Analyze the following NMR spectral data:". This prompt structure helped to provide context for the model, thereby improving the quality of the generated embeddings. Notably, LLM2Vec demonstrates the ability to differentiate between 1 H and 13 C NMR spectra without any fine-tuning, while also capturing possible correlations with molecular size, see Fig. ). This can be attributed to the bidirectional attention and masked next token prediction architecture in LLM2Vec, which is advantageous for complex data like NMR spectra, where relationships between different parts of the spectrum are essential. For each NMR, the embedding text is a 4, 096-dimensional vector ( NMR z).