mteb/index_stackexchange_bm25

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_stackexchange_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_stackexchange_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_stackexchange_bm25", load_corpus=True, mmap=True)

# Load a different branch/revision
retriever = BM25HF.load_from_hub("mteb/index_stackexchange_bm25", revision="main")

# Change directory where the local files should be downloaded
retriever = BM25HF.load_from_hub("mteb/index_stackexchange_bm25", local_dir="/path/to/dir")

# Load private repositories with a token:
retriever = BM25HF.load_from_hub("mteb/index_stackexchange_bm25", token=token)

Stats

This dataset was created using the following data:

Statistic	Value
Number of documents	2161851
Number of tokens	131389247
Average tokens per document	60.78

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