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Update README.md

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@@ -75,7 +75,7 @@ SPLADE models are a fine balance between retrieval effectiveness (quality) and r
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  3. Init Weights: Vanilla **bert-base-uncased**. No corpus awarness unlike Official splade++ / ColBERT
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  4. Yet achieves competitive effectiveness of MRR@10 **37.22** in ID data (& OOD) and a retrieval latency of - **47.27ms**. (multi-threaded) all On **Consumer grade-GPUs** with **only 5 negatives per query**.
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  4. For Industry setting: Effectiveness on custom domains needs more than just **Trading FLOPS for tiny gains** and The Premise "SPLADE++ are not well suited to mono-cpu retrieval" does not hold.
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- 5. Owing query-time inference latency we still need 2 models one for query & doc, This is a Doc model and Query model will be released soon.
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  <img src="./ID.png" width=750 height=650/>
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  3. Init Weights: Vanilla **bert-base-uncased**. No corpus awarness unlike Official splade++ / ColBERT
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  4. Yet achieves competitive effectiveness of MRR@10 **37.22** in ID data (& OOD) and a retrieval latency of - **47.27ms**. (multi-threaded) all On **Consumer grade-GPUs** with **only 5 negatives per query**.
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  4. For Industry setting: Effectiveness on custom domains needs more than just **Trading FLOPS for tiny gains** and The Premise "SPLADE++ are not well suited to mono-cpu retrieval" does not hold.
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+ 5. Owing to query-time inference latency we still need 2 models one for query & doc, This is a Doc model and Query model will be **released soon.**
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  <img src="./ID.png" width=750 height=650/>
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