BM25S: Orders of magnitude faster lexical search via eager sparse
  scoring
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
668cb1f1fecd7e0483421c61
Xing Han Lù
claimed_verified
2024-07-10T12:52:56.027Z
5fa9ff3ea13e063b8b2b60cb
https://cdn-avatars.huggingface.co/v1/production/uploads/1633380224986-5fa9ff3ea13e063b8b2b60cb.jpeg
Xing Han Lù
user
xhluca
BM25S: Orders of magnitude faster lexical search via eager sparse
  scoring
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
BM25S provides a fast and efficient Python implementation of BM25 using sparse matrices and eager scoring, achieving significant speedups over popular Python and Java-based frameworks.