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Update README.md

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@@ -41,7 +41,7 @@ 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("{username}/{repo_name}}")
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  # You can retrieve now
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  query = "a cat is a feline"
@@ -57,17 +57,28 @@ import bm25s
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  from bm25s.hf import 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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  retriever = BM25HF(corpus=corpus)
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  retriever.index(bm25s.tokenize(corpus))
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  token = None # You can get a token from the Hugging Face website
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- retriever.save_to_hub("{username}/{repo_name}", token=token)
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  ```
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  ## Advanced usage
@@ -76,16 +87,16 @@ You can leverage more advanced features of the BM25S library during `load_from_h
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  ```python
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  # Load corpus and index in memory-map (mmap=True) to reduce memory
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- retriever = BM25HF.load_from_hub("{username}/{repo_name}", load_corpus=True, mmap=True)
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  # Load a different branch/revision
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- retriever = BM25HF.load_from_hub("{username}/{repo_name}", revision="main")
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  # Change directory where the local files should be downloaded
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- retriever = BM25HF.load_from_hub("{username}/{repo_name}", local_dir="/path/to/dir")
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  # Load private repositories with a token:
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- retriever = BM25HF.load_from_hub("{username}/{repo_name}", token=token)
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  ```
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  ## Stats
@@ -94,9 +105,9 @@ 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 | {num_docs} |
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- | Number of tokens | {num_tokens} |
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- | Average tokens per document | {avg_tokens_per_doc} |
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  ## Parameters
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@@ -104,11 +115,11 @@ The index was created with the following parameters:
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  | Parameter | Value |
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  | --- | --- |
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- | k1 | `{k1}` |
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- | b | `{b}` |
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- | delta | `{delta}` |
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- | method | `{method}` |
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- | idf method | `{idf_method}` |
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  ## Citation
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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("dadashzadeh/2023_10_en_keywords_Cryptocurrency")
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  # You can retrieve now
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  query = "a cat is a feline"
 
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  from bm25s.hf import BM25HF
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  corpus = [
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+ "northwest bank",
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+ "misfits market",
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+ "merrick bank login",
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+ "marketing",
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+ "market place",
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+ "jetblue customer service",
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+ "internal revenue service",
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+ "how to make money online",
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+ "gordon food service",
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+ "futures market",
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+ "frontier airlines customer service",
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+ "food banks near me",
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+ "first convenience bank",
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+ "eastern bank",
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+ "dollar bank",
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  ]
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  retriever = BM25HF(corpus=corpus)
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  retriever.index(bm25s.tokenize(corpus))
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  token = None # You can get a token from the Hugging Face website
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+ retriever.save_to_hub("dadashzadeh/2023_10_en_keywords_Cryptocurrency", token=token)
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  ```
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  ## Advanced usage
 
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  ```python
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  # Load corpus and index in memory-map (mmap=True) to reduce memory
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+ retriever = BM25HF.load_from_hub("dadashzadeh/2023_10_en_keywords_Cryptocurrency", load_corpus=True, mmap=True)
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  # Load a different branch/revision
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+ retriever = BM25HF.load_from_hub("dadashzadeh/2023_10_en_keywords_Cryptocurrency", revision="main")
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  # Change directory where the local files should be downloaded
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+ retriever = BM25HF.load_from_hub("dadashzadeh/2023_10_en_keywords_Cryptocurrency", local_dir="/path/to/dir")
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  # Load private repositories with a token:
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+ retriever = BM25HF.load_from_hub("dadashzadeh/2023_10_en_keywords_Cryptocurrency", token=token)
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  ```
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  ## Stats
 
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  | Statistic | Value |
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  | --- | --- |
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+ | Number of documents | 602959 |
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+ | Number of tokens | 2414020 |
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+ | Average tokens per document | 4.0 |
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  ## 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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  ## Citation
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