This model is a RoBERTa-based classifier fine-tuned on 7,200 sentences extracted from the Central Bank of the Republic of Türkiye's (CBRT) Monetary Policy Committee (MPC) Summary Reports. The goal is to classify monetary policy tone as hawkish, dovish, or neutral.

Dataset: mrince/CBRT-MPC-SummaryReports

PERFORMANCE

Label	Precision	Recall	F1-score	Support
neutral	0.86	0.80	0.83	491
hawkish	0.89	0.88	0.89	495
dovish	0.86	0.93	0.89	454

Accuracy			0.87	1440
Macro avg	0.87	0.87	0.87	1440
Weighted avg	0.87	0.87	0.87	1440

Model Description

Developed by: Muhammet Rıdvan İNCE
Model type: RoBERTa-based text classifier
Language(s) (NLP): English
License: MIT
Finetuned from model: FacebookAI/roberta-base

Model Sources

Repository: CBRT-RoBERTa-HawkishDovish-Classifier
Paper: In preparation

Direct Use

This model can be used to analyze the tone of central bank policy statements or similar macroeconomic texts in English, especially in academic research or financial market monitoring.

Downstream Use

It may be incorporated into monetary policy forecasting tools, financial sentiment dashboards, or market reaction models.

Out-of-Scope Use

This model is not intended for real-time trading decisions, legal interpretation of monetary policy, or classification of unrelated financial texts.

Bias, Risks, and Limitations

Limitations

The model does not incorporate numerical macroeconomic indicators or forward guidance intensities.

Recommendations

Use in conjunction with economic domain knowledge. Model performance should be monitored when applied to new types of central bank communication.

How to Get Started with the Model

python

from transformers import pipeline

# Load the classifier pipeline
classifier = pipeline("text-classification", model="mrince/CBRT-RoBERTa-HawkishDovish-Classifier")

# Example sentence from a monetary policy context
sentence = "On the other hand, the recent deceleration in economic activity may curb services inflation."

# Perform classification
result = classifier(sentence)
print(result)

# Output example:
# [{'label': 'Label_2', 'score': 0.91}]

# Label meanings:
# - 'hawkish [Label_1]': Tightening bias, upward rate signal. Indicates prioritization of inflation control.
# - 'dovish [Label_2]' : Easing bias, downward/inflation-tolerant tone. Indicates support for growth/stimulus.
# - 'neutral [Label_0]': Informational or balanced tone without a clear policy stance.