The code is distributed under the CC BY-NC-SA 4.0 License. See LICENSE for more information. For the checkpoints, make sure to agree to the license of all the public training datasets and base checkpoints we used, in addition to CC-BY-NC-SA 4.0. See section: Our Hyperparameters for details. Gihub page: https://github.com/naver/dust3r/ Project page: https://dust3r.europe.naverlabs.com/ First, install dust3r. To load the model:
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View details ↗Efficient SPLADE model for passage retrieval. This architecture uses two distinct models for query and document inference. This is the **doc** one, please also download the **query** one (https://huggingface.co/naver/efficient-splade-VI-BT-large-query). 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 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 Type:** SPLADE Sparse Encoder - **Maximum Sequence Length:** 512 tokens (256 for evaluation reproduction) - **Output Dimensionality:** 30522 dimensions - **Similarity Function:** Dot Product
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View details ↗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-VI-BT-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 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 Type:** SPLADE Sparse Encoder - **Maximum Sequence Length:** 512 tokens (256 for evaluation reproduction) - **Output Dimensionality:** 30522 dimensions - **Similarity Function:** Dot Product
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View details ↗The code is distributed under the CC BY-NC-SA 4.0 License. See LICENSE for more information. For the checkpoints, make sure to agree to the license of all the public training datasets and base checkpoints we used, in addition to CC-BY-NC-SA 4.0. The mapfree dataset license in particular is very restrictive. For more information, check CHECKPOINTS_NOTICE. First, install mast3r. To load the model:
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View details ↗SPLADE model for passage retrieval. For additional details, please visit: * paper: https://arxiv.org/abs/2205.04733 * code: https://github.com/naver/splade This is a 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 Type:** SPLADE Sparse Encoder - **Base model:** Luyu/co-condenser-marco - **Maximum Sequence Length:** 512 tokens (256 for evaluation reproduction) - **Output Dimensionality:** 30522 dimensions - **Similarity Function:** Dot Product
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View details ↗SPLADE model for passage retrieval. For additional details, please visit: * paper: https://arxiv.org/abs/2205.04733 * code: https://github.com/naver/splade This is a 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 Type:** SPLADE Sparse Encoder - **Base model:** Luyu/co-condenser-marco - **Maximum Sequence Length:** 512 tokens (256 for evaluation reproduction) - **Output Dimensionality:** 30522 dimensions - **Similarity Function:** Dot Product
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View details ↗SPLADE model for passage retrieval. For additional details, please visit: * paper: https://arxiv.org/abs/2109.10086 * code: https://github.com/naver/splade This is a 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 Type:** SPLADE Sparse Encoder - **Base model:** distilbert/distilbert-base-uncased - **Maximum Sequence Length:** 512 tokens (256 for evaluation reproduction) - **Output Dimensionality:** 30522 dimensions - **Similarity Function:** Dot Product
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View details ↗For more details, see our arXiv companion book: https://arxiv.org/abs/2403.06789 To use SPLADE, please visit our GitHub repository: https://github.com/naver/splade This is a 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.
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