README.md

---
language:
- en
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