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