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---
library_name: xpmir
---
# SPLADE_DistilMSE: SPLADEv2 trained with the distillated triplets
Training data from: https://github.com/sebastian-hofstaetter/neural-ranking-kd
From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models
More Effective (Thibault Formal, Carlos Lassance, Benjamin Piwowarski,
Stéphane Clinchant). 2022. https://arxiv.org/abs/2205.04733
## 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 = AutoModel.load_from_hf_hub("xpmir/SPLADE_DistilMSE")
# Use this if you want to actually use the model
model = AutoModel.load_from_hf_hub("xpmir/SPLADE_DistilMSE", as_instance=True)
model.initialize() model.rsv("walgreens store sales average", "The average
Walgreens salary ranges...")
```
## Results
| Dataset | AP | P@20 | RR | RR@10 | nDCG | nDCG@10 | nDCG@20 |
|----| ---|------|------|------|------|------|------|
| trec2019 | 0.5102 | 0.7360 | 0.9612 | 0.9612 | 0.7407 | 0.7300 | 0.7097 |
| msmarco_dev | 0.3623 | 0.0384 | 0.3673 | 0.3560 | 0.4870 | 0.4207 | 0.4451 |
|