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@@ -44,9 +44,9 @@ pipeline_tag: sentence-similarity
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  - [With Sentence Transformers:](#with-sentence-transformers)
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  - [With Huggingface Transformers:](#with-huggingface-transformers)
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  - [FAQs](#faqs)
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- - [How can we run these models with out heavy torch dependency?](#how-can-we-run-these-models-with-out-heavy-torch-dependency)
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- - [How do I optimise vector index cost?](#how-do-i-optimise-vector-index-cost)
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- - [How do I offer hybrid search to address Vocabulary Mismatch Problem?](#how-do-i-offer)
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  - [Why not run MTEB?](#why-not-run-mteb)
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  - [Roadmap](#roadmap)
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  - [Notes on Reproducing:](#notes-on-reproducing)
@@ -144,14 +144,14 @@ for query, query_embedding in zip(queries, query_embeddings):
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  # FAQs:
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- #### How can we run these models with out heavy torch dependency?
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- - You can use ONNX flavours of these models via [FlashRetrieve](https://github.com/PrithivirajDamodaran/FlashRetrieve) library.
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- #### How do I optimise vector index cost ?
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  [Use Binary and Scalar Quantisation](https://huggingface.co/blog/embedding-quantization)
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- <h4>How do I offer hybrid search to address Vocabulary Mismatch Problem?</h4>
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  MIRACL paper shows simply combining BM25 is a good starting point for a Hybrid option: The below numbers are with mDPR model, but miniMiracle_hi_v1 should give a even better hybrid performance.
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  | Language | ISO | nDCG@10 BM25 | nDCG@10 mDPR | nDCG@10 Hybrid |
 
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  - [With Sentence Transformers:](#with-sentence-transformers)
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  - [With Huggingface Transformers:](#with-huggingface-transformers)
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  - [FAQs](#faqs)
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+ - [How can I reduce overall inference cost ?](#how-can-i-reduce-overall-inference-cost)
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+ - [How do I reduce vector storage cost?](#how-do-i-reduce-vector-storage-cost)
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+ - [How do I offer hybrid search to improve accuracy?](#how-do-i-offer-hybrid-search-to-improve-accuracy)
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  - [Why not run MTEB?](#why-not-run-mteb)
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  - [Roadmap](#roadmap)
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  - [Notes on Reproducing:](#notes-on-reproducing)
 
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  # FAQs:
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+ #### How can I reduce overall inference cost ?
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+ - You host these models without heavy torch dependency using the ONNX flavours of these models via [FlashRetrieve](https://github.com/PrithivirajDamodaran/FlashRetrieve) library.
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+ #### How do I reduce vector storage cost ?
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  [Use Binary and Scalar Quantisation](https://huggingface.co/blog/embedding-quantization)
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+ #### How do I offer hybrid search to improve accuracy ?
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  MIRACL paper shows simply combining BM25 is a good starting point for a Hybrid option: The below numbers are with mDPR model, but miniMiracle_hi_v1 should give a even better hybrid performance.
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  | Language | ISO | nDCG@10 BM25 | nDCG@10 mDPR | nDCG@10 Hybrid |