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"""
# Example: Use Numba to speed up the retrieval process
```bash
pip install "bm25s[full]" numba
```
To build an index, please refer to the `examples/index_and_upload_to_hf.py` script.
Now, to run this script, execute:
```bash
python examples/retrieve_with_numba.py
```
"""
import os
import Stemmer
import bm25s.hf
def main(repo_name="xhluca/bm25s-fiqa-index"):
queries = [
"Is chemotherapy effective for treating cancer?",
"Is Cardiac injury is common in critical cases of COVID-19?",
]
retriever = bm25s.hf.BM25HF.load_from_hub(
repo_name, load_corpus=False, mmap=False
)
# Tokenize the queries
stemmer = Stemmer.Stemmer("english")
queries_tokenized = bm25s.tokenize(queries, stemmer=stemmer)
# Retrieve the top-k results
retriever.activate_numba_scorer()
results = retriever.retrieve(queries_tokenized, k=3, backend_selection="numba")
# show first results
result = results.documents[0]
print(f"First score (# 1 result):{results.scores[0, 0]}")
print(f"First result (# 1 result):\n{result[0]}")
if __name__ == "__main__":
main() |