The UMAP representation allows us to visualize where MOFs with similar performance are located in the feature space. The exact process can be done without fine-tuning, which will return the embeddings of a MOF that are not contextualized on any target and represent a general representation of the MOF learned during pre-training. We call these embeddings the general feature space.