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library_name: xpmir |
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--- |
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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 |
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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/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.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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| msmarco_dev | 0.3642 | 0.0382 | 0.3693 | 0.3582 | 0.4879 | 0.4222 | 0.4458 | |
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| trec2019 | 0.4896 | 0.7209 | 0.9496 | 0.9496 | 0.7253 | 0.7055 | 0.6926 | |
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| trec2020 | 0.5026 | 0.6315 | 0.9483 | 0.9475 | 0.7273 | 0.6868 | 0.6627 | |
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