 Model description  This model is a BERT based multi-vector model trained for fine-grained similarity of biomedical papers. This model inputs the title and abstract of a paper and represents a paper with a contextual sentence vectors obtained by averaging the token representations of individual sentences - the whole title and abstract are encoded with cross-attention in the encoder block before obtaining sentence embeddings. The model is trained by minimizing an Wasserstein/Earth Movers Distance between sentence vectors for a pair of documents - in the process also learning a sparse alignment between sentences in both documents. Test time behavior ranks documents based on the Wasserstein Distance between all sentences of documents or a set of query sentences and a candidate documents sentences.  