Update README.md

README.md

@@ -1,3 +1,35 @@
 ---
 license: cc-by-nc-sa-4.0
+language: "en"
+tags:
+- splade
+- query-expansion
+- document-expansion
+- bag-of-words
+- passage-retrieval
+- knowledge-distillation
+- document encoder
+datasets:
+- ms_marco
 ---
+## Efficient SPLADE 
+Efficient SPLADE model for passage retrieval. This architecture uses two distinct models for query and document inference. This is the **query** one, please also download the **doc** one (https://huggingface.co/naver/efficient-splade-VI-BT-large-doc). For additional details, please visit:
+* paper: 
+* code: https://github.com/naver/splade
+| | MRR@10 (MS MARCO dev) | R@1000 (MS MARCO dev) | Latency (PISA) ms | Latency (Inference) ms
+| --- | --- | --- | --- | --- |
+| `naver/efficient-splade-V-large` | 38.8 | 98.0 | 29.0 | 45.3
+| `naver/efficient-splade-VI-BT-large` | 38.0 | 97.8 | 31.1 | 0.7
+## Citation
+If you use our checkpoint, please cite our work (need to update):
+```
+@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}
+}