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library_name: xpmir |
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# SPLADE_DistilMSE: SPLADEv2 trained with the distillated triplets |
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Training data from: https://github.com/sebastian-hofstaetter/neural-ranking-kd |
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From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models |
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More Effective (Thibault Formal, Carlos Lassance, Benjamin Piwowarski, |
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Stéphane Clinchant). 2022. https://arxiv.org/abs/2205.04733 |
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## Using the model) |
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The model can be loaded with [experimaestro IR](https://experimaestro-ir.readthedocs.io/en/latest/) |
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```py |
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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 = AutoModel.load_from_hf_hub("xpmir/SPLADE_DistilMSE") |
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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/SPLADE_DistilMSE", as_instance=True) |
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model.initialize() |
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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 | nDCG | nDCG@10 | nDCG@20 | |
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|----| ---|------|------|------|------|------|------| |
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| trec2019 | 0.5102 | 0.7360 | 0.9612 | 0.9612 | 0.7407 | 0.7300 | 0.7097 | |
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| msmarco_dev | 0.3623 | 0.0384 | 0.3673 | 0.3560 | 0.4870 | 0.4207 | 0.4451 | |
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