This is a PyLate model finetuned from neuml/biomedbert-small. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
Usage (txtai)
This model can be used to build embeddings databases with txtai for semantic search and/or as a knowledge source for retrieval augmented generation (RAG). Note that since this is a custom architecture, trust_remote_code is required to be enabled.
python
1import txtai
23embeddings = txtai.Embeddings(4 path="neuml/biomedbert-small-colbert",5 content=True6)7embeddings.index(documents())89# Run a query10embeddings.search("query to run")
Late interaction models excel as reranker pipelines.
Performance of these models are compared to previously released models trained on medical literature. The most commonly used small embeddings model is also included for comparison.
The following datasets were used to evaluate model performance.
As with other ColBERT models on this dataset, it tends to score lower with longer form queries. But note how this model outperforms it's equivalent small model on the PubMed QA dataset. For traditional user queries, this model will likely get better results in production.
