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OpenMatch/dpr_bert-base_msmarco_qry-psg-encoder
dpr_bert-base_msmarco_qry-psg-encoder is a feature extraction model from OpenMatch. Use it when you need embeddings to search or compare text. It is set up for transformers. The card lists the license as mit.
This model is DPR trained on MS MARCO. The training details and evaluation results are as follows:
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From the Hugging Face model README
This model is DPR trained on MS MARCO. The training details and evaluation results are as follows:
| Model | Pretrain Model | Train w/ Marco Title | Marco Dev MRR@10 | BEIR Avg NDCG@10 |
|---|---|---|---|---|
| DPR | bert-base-uncased | w/ | 32.4 | 35.5 |
| BERI Dataset | NDCG@10 |
|---|---|
| TREC-COVID | 58.8 |
| NFCorpus | 23.4 |
| FiQA | 20.6 |
| ArguAna | 39.4 |
| Touché-2020 | 22.3 |
| Quora | 78.0 |
| SCIDOCS | 11.9 |
| SciFact | 49.4 |
| NQ | 43.9 |
| HotpotQA | 45.3 |
| Signal-1M | 20.2 |
| TREC-NEWS | 31.8 |
| DBPedia-entity | 28.7 |
| Fever | 65.0 |
| Climate-Fever | 14.9 |
| BioASQ | 24.1 |
| Robust04 | 32.3 |
| CQADupStack | 28.3 |
The implementation is the same as our EMNLP 2022 paper "Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives". The associated GitHub repository is available at https://github.com/OpenMatch/ANCE-Tele.
@inproceedings{sun2022ancetele,
title={Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives},
author={Si, Sun and Chenyan, Xiong and Yue, Yu and Arnold, Overwijk and Zhiyuan, Liu and Jie, Bao},
booktitle={Proceedings of EMNLP 2022},
year={2022}
}