---
language: en
tags:
- bm25
- bm25s
- retrieval
- search
- lexical
---

# BM25S Index

This is a BM25S index created with the [`bm25s` library](https://github.com/xhluca/bm25s) (version `0.0.1dev0`), an ultra-fast implementation of BM25. It can be used for lexical retrieval tasks.

[BM25S GitHub Repository](https://github.com/xhluca/bm25s)

## Installation

You can install the `bm25s` library with `pip`:

```bash
pip install "bm25s==0.0.1dev0"

# Include extra dependencies like stemmer
pip install "bm25s[full]==0.0.1dev0"

# 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:

```python
import bm25s
from bm25s.hf import BM25HF

# Load the index
retriever = BM25HF.load_from_hub("xhluca/bm25s-dbpedia-entity-index", revision="main")

# You can retrieve now
query = "a cat is a feline"
results = retriever.retrieve(query, k=3)
```

## Saving a `bm25s` index

You can save a `bm25s` index to the Hugging Face Hub. Here is an example:

```python
import bm25s
from bm25s.hf import BM25HF

# Create a BM25 index and add documents
retriever = 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",
]
corpus_tokens = bm25s.tokenize(corpus)
retriever.index(corpus_tokens)

token = None  # You can get a token from the Hugging Face website
retriever.save_to_hub("xhluca/bm25s-dbpedia-entity-index", token=token)
```


## Stats

This dataset was created using the following data:

| Statistic | Value |
| --- | --- |
| Number of documents | 4635922 |
| Number of tokens | 127145332 |
| Average tokens per document | 27.426115452330734 |

## 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` |