Update BM25S model

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+corpus.jsonl filter=lfs diff=lfs merge=lfs -text
+corpus.mmindex.json filter=lfs diff=lfs merge=lfs -text
+vocab.index.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+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` |

corpus.jsonl

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corpus.mmindex.json

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params.index.json

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+{
+    "k1": 1.5,
+    "b": 0.75,
+    "delta": 0.5,
+    "method": "lucene",
+    "idf_method": "lucene",
+    "dtype": "float32",
+    "int_dtype": "int32",
+    "num_docs": 4635922
+}

vocab.index.json

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