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Browse files- .gitattributes +2 -0
- index.html +1 -1
- posts/assets/hybrid-search-retrieval-bm25-modernbert/example-1.png +3 -0
- posts/assets/hybrid-search-retrieval-bm25-modernbert/example-2.png +3 -0
- posts/hybrid-search-retrieval-bm25-modernbert.html +12 -3
- posts/index.html +1 -1
- search.json +3 -3
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.gitattributes
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<h3 class="no-anchor listing-title">
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<a href="./posts/hybrid-search-retrieval-bm25-modernbert.html" class="no-external">Hybrid Search Retrieval: Combining Semantic Search and BM25 Algorithm with Examples</a>
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<a href="./posts/hybrid-search-retrieval-bm25-modernbert.html" class="no-external">Hybrid Search Retrieval: Combining Semantic Search and BM25 Algorithm with Examples</a>
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posts/assets/hybrid-search-retrieval-bm25-modernbert/example-1.png
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posts/hybrid-search-retrieval-bm25-modernbert.html
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<p>Source: https://redislabs.atlassian.net/wiki/spaces/RED/pages/5798592518/Hybrid+Search+Retrieval+Combining+Semantic+Search+and+BM25+Algorithm+with+Examples</p>
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<p>The examples below illustrate a hybrid search retrieval of a query from a corpus of documents by combining semantic search using ModernBERT embeddings and the BM25 algorithm.</p>
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<p>The scores from each are normalized between 0 and 1 in order to create a weighted average hybrid score. The raw embedding score is also shown in the columns for diagnostics. The ranks in each measure are then combined using RRF to create another score that can be used for retrieval.</p>
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<p>The most correct match for each query is highlighted in each example.</p>
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<section id="example-1" class="level2">
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<h2 class="anchored" data-anchor-id="example-1">Example 1</h2>
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<section id="failure-mode-1---negationsdirectionality" class="level2">
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<h2 class="anchored" data-anchor-id="failure-mode-1---negationsdirectionality">Failure mode 1 - Negations/directionality</h2>
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</header>
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<p>The examples below illustrate a hybrid search retrieval of a query from a corpus of documents by combining semantic search using ModernBERT embeddings and the BM25 algorithm.</p>
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<p>The scores from each are normalized between 0 and 1 in order to create a weighted average hybrid score. The raw embedding score is also shown in the columns for diagnostics. The ranks in each measure are then combined using RRF to create another score that can be used for retrieval.</p>
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<p>The most correct match for each query is highlighted in each example.</p>
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<section id="example-1" class="level2">
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<h2 class="anchored" data-anchor-id="example-1">Example 1</h2>
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<p><img src="assets/hybrid-search-retrieval-bm25-modernbert/example-1.png" class="img-fluid figure-img"></p>
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<figcaption>Hybrid retrieval example 1 (BM25 vs embedding vs hybrid).</figcaption>
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</figure>
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</section>
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<section id="example-2" class="level2">
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<h2 class="anchored" data-anchor-id="example-2">Example 2</h2>
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<p><img src="assets/hybrid-search-retrieval-bm25-modernbert/example-2.png" class="img-fluid figure-img"></p>
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<figcaption>Hybrid retrieval example 2 (BM25 vs embedding vs hybrid).</figcaption>
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</figure>
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</section>
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<section id="failure-mode-1---negationsdirectionality" class="level2">
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<h2 class="anchored" data-anchor-id="failure-mode-1---negationsdirectionality">Failure mode 1 - Negations/directionality</h2>
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<a href="../posts/hybrid-search-retrieval-bm25-modernbert.html" class="no-external">Hybrid Search Retrieval: Combining Semantic Search and BM25 Algorithm with Examples</a>
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<a href="../posts/hybrid-search-retrieval-bm25-modernbert.html" class="no-external">Hybrid Search Retrieval: Combining Semantic Search and BM25 Algorithm with Examples</a>
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search.json
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"title": "Hybrid Search Retrieval: Combining Semantic Search and BM25 Algorithm with Examples",
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"title": "Hybrid Search Retrieval: Combining Semantic Search and BM25 Algorithm with Examples",
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"title": "Hybrid Search Retrieval: Combining Semantic Search and BM25 Algorithm with Examples",
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"section": "",
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"text": "The examples below illustrate a hybrid search retrieval of a query from a corpus of documents by combining semantic search using ModernBERT embeddings and the BM25 algorithm.\nThe scores from each are normalized between 0 and 1 in order to create a weighted average hybrid score. The raw embedding score is also shown in the columns for diagnostics. The ranks in each measure are then combined using RRF to create another score that can be used for retrieval.\nThe most correct match for each query is highlighted in each example."
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"title": "Hybrid Search Retrieval: Combining Semantic Search and BM25 Algorithm with Examples",
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"section": "Example 1",
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"title": "Hybrid Search Retrieval: Combining Semantic Search and BM25 Algorithm with Examples",
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"section": "Example 2",
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