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. In addition, fine-tuned ModernBERT and CentralBank-BERT versions are also available.

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
CBSI-ModernBERT-base	0.93	0.93	0.40
CBSI-ModernBERT-large	0.91	0.91	0.53
CBSI-CentralBank-BERT	0.92	0.92	0.36

How to use

python

import pandas as pd
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

# Load model and tokenizer
model_name = "brjoey/CBSI-CentralBank-BERT"
classifier = pipeline(
    "text-classification",
    model=model_name,
    tokenizer=model_name
)

# Define label mapping
cbsi_label_map = {
    0: "neutral",
    1: "dovish",
    2: "hawkish"
}

# Classify a column in a Pandas DataFrame - replace with your DataFrame
texts = [
    "The Governing Council decided to lower interest rates.",
    "The central bank will maintain its current policy stance."
]

df = pd.DataFrame({
    "text": texts
})

# Run classification 
predictions = classifier(
    df["text"].tolist()
)

# Store the results
df["label"], df["score"] = zip(*[
    (cbsi_label_map[int(pred["label"].split("_")[-1])], pred["score"])
    for pred in predictions
])

print("\n                              === Results ===\n")
print(df[["text", "label", "score"]])

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

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

CentralBank-BERT:
Zafar, M. B., 2025,
CentralBank-BERT: Machine Learning Evidence on Central Bank Digital Currency Discourse.
SSRN. https://papers.ssrn.com/abstract=5404456