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 = 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