
## SPLADE CoCondenser SelfDistil

SPLADE model for passage retrieval. For additional details, please visit:
* paper: https://arxiv.org/abs/2205.04733
* code: https://github.com/naver/splade

| | 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}
}
```
{"language": "en", "license": "cc-by-nc-sa-4.0", "tags": ["splade", "query-expansion", "document-expansion", "bag-of-words", "passage-retrieval", "knowledge-distillation"], "datasets": ["ms_marco"], "duplicated_from": "naver/splade-cocondenser-selfdistil"}
transformers pytorch safetensors bert fill-mask splade query-expansion document-expansion bag-of-words passage-retrieval knowledge-distillation en dataset:ms_marco arxiv:2205.04733 license:cc-by-nc-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #fill-mask #splade #query-expansion #document-expansion #bag-of-words #passage-retrieval #knowledge-distillation #en #dataset-ms_marco #arxiv-2205.04733 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 

SPLADE CoCondenser SelfDistil
-----------------------------


SPLADE model for passage retrieval. For additional details, please visit:


* paper: URL
* code: URL


MRR@10 (MS MARCO dev): 'splade-cocondenser-selfdistil', R@1000 (MS MARCO dev): 37.6


If you use our checkpoint, please cite our work:



TAGS
#transformers #pytorch #safetensors #bert #fill-mask #splade #query-expansion #document-expansion #bag-of-words #passage-retrieval #knowledge-distillation #en #dataset-ms_marco #arxiv-2205.04733 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #safetensors #bert #fill-mask #splade #query-expansion #document-expansion #bag-of-words #passage-retrieval #knowledge-distillation #en #dataset-ms_marco #arxiv-2205.04733 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
