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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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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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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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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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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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
译文排队中 · 当前显示来源原文
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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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础资料已收录 · 缺失资料后续补充
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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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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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础资料已收录 · 缺失资料后续补充
可以调整厂商、分类或输入输出条件；新模型正持续收录。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。
