Update README.md

README.md

@@ -4,4 +4,38 @@ language:
 base_model:
 - google-bert/bert-base-uncased
 pipeline_tag: text-classification
----
+---
+# CBSI-BERT Models
+
+This model is trained on the replication data of [Nițoi et al. (2023)](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/40JFEK).  
+Check out their [paper](https://www.sciencedirect.com/science/article/abs/pii/S2214635023000230) and [website](https://sites.google.com/view/bert-cbsi/) for more information.
+
+The model is trained with the hyperparameters used by [Nițoi et al. (2023)](https://www.sciencedirect.com/science/article/abs/pii/S2214635023000230).  
+In addition, different hyperparameters, seeds, and reinitialization of the first L layers were tested. The performance seems relatively stable across hyperparameter settings.  
+
+Alongside these models, FinBERT, different versions of RoBERTa, and EconBERT were tested. The performance of the BERT-based models reported here is significantly better.
+
+## Results
+
+| Model                                                                 | F1 Score | Accuracy | Loss |
+|------------------------------------------------------------------------|----------|----------|------|
+| [CBSI-bert-base-uncased](https://huggingface.co/brjoey/CBSI-bert-base-uncased)   | 0.88     | 0.88     | 0.49 |
+| [CBSI-bert-large-uncased](https://huggingface.co/brjoey/CBSI-bert-large-uncased) | 0.92     | 0.92     | 0.45 |
+
+## How to use
+
+```python
+```
+---
+# Citation
+If you use this model, please cite: \
+Data:\
+Nițoi Mihai; Pochea Maria-Miruna; Radu Ștefan-Constantin, 2023, \
+"Replication Data for: Unveiling the sentiment behind central bank narratives: A novel deep learning index", \
+https://doi.org/10.7910/DVN/40JFEK, Harvard Dataverse, V1
+
+Model / Paper: \
+Mihai Niţoi, Maria-Miruna Pochea, Ştefan-Constantin Radu, \
+Unveiling the sentiment behind central bank narratives: A novel deep learning index, \
+Journal of Behavioral and Experimental Finance, Volume 38, 2023, 100809, ISSN 2214-6350. \
+https://doi.org/10.1016/j.jbef.2023.100809