Fill-Mask
Transformers
PyTorch
Safetensors
English
bert
splade
query-expansion
document-expansion
bag-of-words
passage-retrieval
knowledge-distillation
Instructions to use baseplate/splade-cocondenser-selfdistil with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use baseplate/splade-cocondenser-selfdistil with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="baseplate/splade-cocondenser-selfdistil")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("baseplate/splade-cocondenser-selfdistil") model = AutoModelForMaskedLM.from_pretrained("baseplate/splade-cocondenser-selfdistil") - Notebooks
- Google Colab
- Kaggle
metadata
license: cc-by-nc-sa-4.0
language: en
tags:
- splade
- query-expansion
- document-expansion
- bag-of-words
- passage-retrieval
- knowledge-distillation
datasets:
- ms_marco
duplicated_from: naver/splade-cocondenser-selfdistil
SPLADE CoCondenser SelfDistil
SPLADE model for passage retrieval. For additional details, please visit:
| MRR@10 (MS MARCO dev) | R@1000 (MS MARCO dev) | |
|---|---|---|
splade-cocondenser-selfdistil |
37.6 | 98.4 |
Citation
If you use our checkpoint, please cite our work:
@misc{https://doi.org/10.48550/arxiv.2205.04733,
doi = {10.48550/ARXIV.2205.04733},
url = {https://arxiv.org/abs/2205.04733},
author = {Formal, Thibault and Lassance, Carlos and Piwowarski, Benjamin and Clinchant, Stéphane},
keywords = {Information Retrieval (cs.IR), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}