metadata
library_name: xpmir
monoBERT trained on MS-Marco
Passage Re-ranking with BERT (Rodrigo Nogueira, Kyunghyun Cho). 2019. https://arxiv.org/abs/1901.04085
This model has been trained on MsMarco v1
Using the model
The model can be loaded with experimaestro IR
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
| 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 |