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).
Check out their paper and website for more information.
The model is trained with the hyperparameters used by Nițoi et al. (2023).
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 | 0.88 | 0.88 | 0.49 |
| CBSI-bert-large-uncased | 0.92 | 0.92 | 0.45 |
How to use
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