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library_name: xpmir |
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# monoBERT trained on MS-Marco |
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Passage Re-ranking with BERT (Rodrigo Nogueira, Kyunghyun Cho). 2019. |
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https://arxiv.org/abs/1901.04085 |
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This model has been trained on MsMarco v1 |
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## Using the model |
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The model can be loaded with [experimaestro |
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IR](https://experimaestro-ir.readthedocs.io/en/latest/) |
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```py from xpmir.models import AutoModel |
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from xpmir.models import AutoModel |
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# Model that can be re-used in experiments |
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model, init_tasks = AutoModel.load_from_hf_hub("xpmir/monobert") |
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# Use this if you want to actually use the model |
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model = AutoModel.load_from_hf_hub("xpmir/monobert", as_instance=True) |
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model.rsv("walgreens store sales average", "The average Walgreens salary ranges...") |
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``` |
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## Results |
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| Dataset | AP | P@20 | RR | RR@10 | Success@5 | nDCG | nDCG@10 | nDCG@20 | |
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|----| ---|------|------|------|------|------|------|------| |
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| msmarco_dev | 0.3722 | 0.0377 | 0.3774 | 0.3689 | 0.5390 | 0.4767 | 0.4316 | 0.4517 | |
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| trec2019 | 0.4900 | 0.7512 | 0.9426 | 0.9426 | 1.0000 | 0.6933 | 0.7190 | 0.6997 | |
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| trec2020 | 0.4851 | 0.6269 | 0.9354 | 0.9354 | 0.9815 | 0.6935 | 0.7156 | 0.6796 | |
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