Efficient SPLADE Efficient SPLADE model for passage retrieval. This architecture uses two distinct models for query and document inference. This is the query one, please also download the doc one (https://huggingface.co/naver/efficient splade V large doc). For additional details, please visit: paper: https://dl.acm.org/doi/10.1145/3477495.3531833 code: https://github.com/naver/splade MRR@10 (MS MARCO dev) R@1000 (MS MARCO dev) Latency (PISA) ms Latency (Inference) ms naver/efficient splade V large 38.8 98.0 29.0 45.3 naver/efficient splade VI BT large 38.0 97.8 31.1 0.7 Model Details This is a Asymmetric SPLADE Sparse Encoder model. It maps sentences & paragraphs to a 30522 dimensional sparse vector space and can be used for semantic search and sparse retrieval. Model Description Model Type: SPLADE Sparse Encoder Maximum Sequence Length: 512 tokens (256 for evaluation reproduction) Output Dimensionality: 30522 dimensions Similarity Function: Dot Product Full Model Architecture Usage Direct Usage (Sentence Transformers) First install the Sentence Transformers library: Then you can load this model and run inference. Note that with Sentence Transformers you load the entire model, i.e.…
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