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fix(docs): correct typo essentially in search_reranking_using_embeddings.ipynb #578

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Merged
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Apr 19, 2025
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fix(docs): correct typo essentially in search_reranking_using_embeddings.ipynb #578

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2 changes: 1 addition & 1 deletion site/en/docs/search_reranking_using_embeddings.ipynb
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Expand Up @@ -1113,7 +1113,7 @@
"id": "ltbB0vDsKQtI"
},
"source": [
"You will now implement **cosine similarity** as your metric. Here returned embedding vectors will be of unit length and hence their L1 norm (`np.linalg.norm()`) will be ~1. Hence, calculating **cosine similarity** is esentially same as calculating their **dot product score**."
"You will now implement **cosine similarity** as your metric. Here returned embedding vectors will be of unit length and hence their L1 norm (`np.linalg.norm()`) will be ~1. Hence, calculating **cosine similarity** is essentially same as calculating their **dot product score**."
]
},
{
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