prithivida
/

Splade_PP_en_v2

document-expansion

sparse representation

passage-retrieval

knowledge-distillation

document encoder

Inference Endpoints

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prithivida commited on Mar 13

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@@ -79,7 +79,7 @@ SPLADE models are a fine balance between retrieval effectiveness (quality) and r
 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.
 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.**
-<img src="./ID.png" width=850 height=750/>
 *Note: The paper refers to the best performing models as SPLADE++, hence for consistency we are reusing the same.*

 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.
 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.**
+<img src="./ID.png" width=750 height=650/>
 *Note: The paper refers to the best performing models as SPLADE++, hence for consistency we are reusing the same.*