metadata
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
from xpmir.models import AutoModel
# Model that can be re-used in experiments
model, init_tasks = 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.rsv("walgreens store sales average", "The average Walgreens salary ranges...")
Results
| Dataset | AP | P@20 | RR | RR@10 | nDCG | nDCG@10 | nDCG@20 |
|---|---|---|---|---|---|---|---|
| msmarco_dev | 0.3642 | 0.0382 | 0.3693 | 0.3582 | 0.4879 | 0.4222 | 0.4458 |
| trec2019 | 0.4896 | 0.7209 | 0.9496 | 0.9496 | 0.7253 | 0.7055 | 0.6926 |
| trec2020 | 0.5026 | 0.6315 | 0.9483 | 0.9475 | 0.7273 | 0.6868 | 0.6627 |