Update README.md

README.md

@@ -14,6 +14,12 @@ This is a [SetFit model](https://github.com/huggingface/setfit) that can be used
 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
 2. Training a classification head with features from the fine-tuned Sentence Transformer.
 
+Model is meant to classify Ostrom rule types for institutional analysis. Read more at https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=5845434
+
+###Performance
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/646431c538083255f677008b/PblvA-NzVEeHdJRA4ogS7.png)
+
 ## Usage
 
 To use this model for inference, first install the SetFit library:
@@ -33,17 +39,17 @@ model = SetFitModel.from_pretrained("mahaswec/setfit_ostrom")
 preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
 ```
 
+
 ## BibTeX entry and citation info
 
 ```bibtex
-@article{https://doi.org/10.48550/arxiv.2209.11055,
-doi = {10.48550/ARXIV.2209.11055},
-url = {https://arxiv.org/abs/2209.11055},
-author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
-keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
-title = {Efficient Few-Shot Learning Without Prompts},
-publisher = {arXiv},
-year = {2022},
-copyright = {Creative Commons Attribution 4.0 International}
+@article{Yin2025-rt,
+  title  = "Governing the digital commons at scale: Detecting Ostrom rule types
+            in {OSS}",
+  author = "Yin, Likang and Atkisson, Curtis and Chakraborti, Mahasweta and
+            Ruiz, Santiago Virguez and Bushouse, Brenda and Schweik, Charlie
+            and Frey, Seth and Filkov, Vladimir",
+  year   =  2025
 }
+
 ```