xhluca commited on
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825366e
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Update BM25S model

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.gitattributes CHANGED
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ corpus.jsonl filter=lfs diff=lfs merge=lfs -text
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+ corpus.mmindex.json filter=lfs diff=lfs merge=lfs -text
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+ vocab.index.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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+ ---
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+ language: en
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+ tags:
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+ - bm25
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+ - bm25s
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+ - retrieval
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+ - search
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+ - lexical
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+ ---
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+
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+ # BM25S Index
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+
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+ 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.
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+
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+ [BM25S GitHub Repository](https://github.com/xhluca/bm25s)
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+
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+ ## Installation
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+
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+ You can install the `bm25s` library with `pip`:
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+
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+ ```bash
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+ pip install "bm25s==0.0.1dev0"
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+
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+ # Include extra dependencies like stemmer
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+ pip install "bm25s[full]==0.0.1dev0"
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+
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+ # For huggingface hub usage
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+ pip install huggingface_hub
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+ ```
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+
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+ ## Loading a `bm25s` index
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+
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+ You can use this index for information retrieval tasks. Here is an example:
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+
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+ ```python
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+ import bm25s
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+ from bm25s.hf import BM25HF
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+
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+ # Load the index
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+ retriever = BM25HF.load_from_hub("xhluca/bm25s-dbpedia-entity-index", revision="main")
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+
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+ # You can retrieve now
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+ query = "a cat is a feline"
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+ results = retriever.retrieve(query, k=3)
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+ ```
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+
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+ ## Saving a `bm25s` index
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+
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+ You can save a `bm25s` index to the Hugging Face Hub. Here is an example:
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+
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+ ```python
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+ import bm25s
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+ from bm25s.hf import BM25HF
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+
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+ # Create a BM25 index and add documents
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+ retriever = BM25HF()
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+ corpus = [
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+ "a cat is a feline and likes to purr",
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+ "a dog is the human's best friend and loves to play",
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+ "a bird is a beautiful animal that can fly",
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+ "a fish is a creature that lives in water and swims",
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+ ]
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+ corpus_tokens = bm25s.tokenize(corpus)
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+ retriever.index(corpus_tokens)
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+
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+ token = None # You can get a token from the Hugging Face website
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+ retriever.save_to_hub("xhluca/bm25s-dbpedia-entity-index", token=token)
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+ ```
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+
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+
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+ ## Stats
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+
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+ This dataset was created using the following data:
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+
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+ | Statistic | Value |
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+ | --- | --- |
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+ | Number of documents | 4635922 |
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+ | Number of tokens | 127145332 |
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+ | Average tokens per document | 27.426115452330734 |
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+
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+ ## Parameters
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+
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+ The index was created with the following parameters:
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+
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+ | Parameter | Value |
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+ | --- | --- |
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+ | k1 | `1.5` |
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+ | b | `0.75` |
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+ | delta | `0.5` |
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+ | method | `lucene` |
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+ | idf method | `lucene` |
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+ "num_docs": 4635922
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+ }
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