Update BM25S model
Browse files- .gitattributes +3 -0
- README.md +91 -0
- corpus.jsonl +3 -0
- corpus.mmindex.json +3 -0
- data.csc.index.npy +3 -0
- indices.csc.index.npy +3 -0
- indptr.csc.index.npy +3 -0
- params.index.json +11 -0
- vocab.index.json +3 -0
.gitattributes
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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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*.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
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README.md
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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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# BM25S Index
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This is a BM25S index created with the [`bm25s` library](https://github.com/xhluca/bm25s) (version `0.1.3`), an ultra-fast implementation of BM25. It can be used for lexical retrieval tasks.
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[BM25S GitHub Repository](https://github.com/xhluca/bm25s)
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## Installation
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You can install the `bm25s` library with `pip`:
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```bash
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pip install "bm25s==0.1.3"
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# Include extra dependencies like stemmer
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pip install "bm25s[full]==0.1.3"
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# For huggingface hub usage
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pip install huggingface_hub
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```
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## Loading a `bm25s` index
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You can use this index for information retrieval tasks. Here is an example:
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```python
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import bm25s
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from bm25s.hf import BM25HF
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# Load the index
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retriever = BM25HF.load_from_hub("orionweller/kilt-100w-bm25", revision="main")
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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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## Saving a `bm25s` index
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You can save a `bm25s` index to the Hugging Face Hub. Here is an example:
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```python
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import bm25s
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from bm25s.hf import BM25HF
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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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token = None # You can get a token from the Hugging Face website
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retriever.save_to_hub("orionweller/kilt-100w-bm25", token=token)
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```
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## Stats
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This dataset was created using the following data:
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| Statistic | Value |
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| --- | --- |
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| Number of documents | 24853658 |
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| Number of tokens | 1232790853 |
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| Average tokens per document | 49.601988286794644 |
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## Parameters
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The index was created with the following parameters:
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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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corpus.jsonl
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version https://git-lfs.github.com/spec/v1
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oid sha256:c9de4342e66d35e4c604c9188d833da24f7de3fb0e837f489b2b0a5dc9098c76
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size 15202866031
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corpus.mmindex.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:5ec30db6658386771dede619abe6a555f351ba3fbbfd81187b9d6d8a4afd9668
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size 280103400
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data.csc.index.npy
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version https://git-lfs.github.com/spec/v1
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oid sha256:e15e2f300f211a5f2aa7fc6a075de211c3ec02706e7f589cd12c48fd8edffdbc
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size 4931163540
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indices.csc.index.npy
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version https://git-lfs.github.com/spec/v1
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oid sha256:75072b69d9c9d107882306fdfc80e0e9cf90a83f25e481b1019a5aadf627fb5b
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size 4931163540
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indptr.csc.index.npy
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version https://git-lfs.github.com/spec/v1
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oid sha256:805d08ed7e1b3a75168aef338bcc6757c6d70b7a397563d8bafc5946310f66a9
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size 21935564
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params.index.json
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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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"dtype": "float32",
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"int_dtype": "int32",
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"num_docs": 24853658,
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"version": "0.1.3"
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}
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vocab.index.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:b4a939a50258cf8615fca63c589f01dd517952c3ee5685901ca912c44ed85654
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size 113605687
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