Efficient Splade V Large Doc

🚀 高效SPLADE模型

高效SPLADE模型用於段落檢索。該架構使用兩個不同的模型進行查詢和文檔推理。這是文檔模型，請同時下載查詢模型（https://huggingface.co/naver/efficient-splade-V-large-query）。如需更多詳細信息，請訪問：

• 論文：https://dl.acm.org/doi/10.1145/3477495.3531833
• 代碼：https://github.com/naver/splade

✨ 主要特性

本項目的高效SPLADE模型具有以下特性：

• 採用兩個不同的模型分別進行查詢和文檔推理，提升檢索效率。
• 提供了不同版本的模型，並在MS MARCO數據集上有相應的性能指標。

📚 詳細文檔

📄 許可證

本項目採用CC BY-NC-SA 4.0許可證。

🔖 引用

如果您使用我們的模型檢查點，請引用我們的工作（需更新）：

@inproceedings{10.1145/3477495.3531833,
author = {Lassance, Carlos and Clinchant, St\'{e}phane},
title = {An Efficiency Study for SPLADE Models},
year = {2022},
isbn = {9781450387323},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3477495.3531833},
doi = {10.1145/3477495.3531833},
abstract = {Latency and efficiency issues are often overlooked when evaluating IR models based on Pretrained Language Models (PLMs) in reason of multiple hardware and software testing scenarios. Nevertheless, efficiency is an important part of such systems and should not be overlooked. In this paper, we focus on improving the efficiency of the SPLADE model since it has achieved state-of-the-art zero-shot performance and competitive results on TREC collections. SPLADE efficiency can be controlled via a regularization factor, but solely controlling this regularization has been shown to not be efficient enough. In order to reduce the latency gap between SPLADE and traditional retrieval systems, we propose several techniques including L1 regularization for queries, a separation of document/query encoders, a FLOPS-regularized middle-training, and the use of faster query encoders. Our benchmark demonstrates that we can drastically improve the efficiency of these models while increasing the performance metrics on in-domain data. To our knowledge, we propose the first neural models that, under the same computing constraints, achieve similar latency (less than 4ms difference) as traditional BM25, while having similar performance (less than 10% MRR@10 reduction) as the state-of-the-art single-stage neural rankers on in-domain data.},
booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {2220–2226},
numpages = {7},
keywords = {splade, latency, information retrieval, sparse representations},
location = {Madrid, Spain},
series = {SIGIR '22}
}