BM25S Index
This is a BM25S index created with the bm25s
library (version 0.1.7
), an ultra-fast implementation of BM25. It can be used for lexical retrieval tasks.
💻BM25S GitHub Repository
🌐BM25S Homepage
Installation
You can install the bm25s
library with pip
:
pip install "bm25s==0.1.7"
# Include extra dependencies like stemmer
pip install "bm25s[full]==0.1.7"
# For huggingface hub usage
pip install huggingface_hub
Loading a bm25s
index
You can use this index for information retrieval tasks. Here is an example:
import bm25s
from bm25s.hf import BM25HF
# Load the index
retriever = BM25HF.load_from_hub("mteb/index_arxiv_bm25")
# You can retrieve now
query = "a cat is a feline"
results = retriever.retrieve(bm25s.tokenize(query), k=3)
Saving a bm25s
index
You can save a bm25s
index to the Hugging Face Hub. Here is an example:
import bm25s
from bm25s.hf import BM25HF
corpus = [
"a cat is a feline and likes to purr",
"a dog is the human's best friend and loves to play",
"a bird is a beautiful animal that can fly",
"a fish is a creature that lives in water and swims",
]
retriever = BM25HF(corpus=corpus)
retriever.index(bm25s.tokenize(corpus))
token = None # You can get a token from the Hugging Face website
retriever.save_to_hub("mteb/index_arxiv_bm25", token=token)
Advanced usage
You can leverage more advanced features of the BM25S library during load_from_hub
:
# Load corpus and index in memory-map (mmap=True) to reduce memory
retriever = BM25HF.load_from_hub("mteb/index_arxiv_bm25", load_corpus=True, mmap=True)
# Load a different branch/revision
retriever = BM25HF.load_from_hub("mteb/index_arxiv_bm25", revision="main")
# Change directory where the local files should be downloaded
retriever = BM25HF.load_from_hub("mteb/index_arxiv_bm25", local_dir="/path/to/dir")
# Load private repositories with a token:
retriever = BM25HF.load_from_hub("mteb/index_arxiv_bm25", token=token)
Stats
This dataset was created using the following data:
Statistic | Value |
---|---|
Number of documents | 2511805 |
Number of tokens | 177111187 |
Average tokens per document | 70.51 |
Parameters
The index was created with the following parameters:
Parameter | Value |
---|---|
k1 | 1.5 |
b | 0.75 |
delta | 0.5 |
method | lucene |
idf method | lucene |
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