BM25S: Orders of magnitude faster lexical search via eager sparse
  scoring
Authors: name: Xing Han Lù

We introduce BM25S, an efficient Python-based implementation of BM25 that
only depends on Numpy and Scipy. BM25S achieves up to a 500x speedup compared
to the most popular Python-based framework by eagerly computing BM25 scores
during indexing and storing them into sparse matrices. It also achieves
considerable speedups compared to highly optimized Java-based implementations,
which are used by popular commercial products. Finally, BM25S reproduces the
exact implementation of five BM25 variants based on Kamphuis et al. (2020) by
extending eager scoring to non-sparse variants using a novel score shifting
method. The code can be found at https://github.com/xhluca/bm25s