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mrince/CBRT-RoBERTa-Large-HawkishDovish-Classifier
CBRT-RoBERTa-Large-HawkishDovish-Classifier is a text classification model from mrince. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
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 monet…
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From the Hugging Face model README
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
| Label | Precision | Recall | F1-score | Accuracy |
|---|---|---|---|---|
| neutral | 0.8970 | 0.8235 | 0.8587 | 0.8235 |
| hawkish | 0.9226 | 0.9245 | 0.9235 | 0.9245 |
| dovish | 0.8926 | 0.9641 | 0.9270 | 0.9641 |
| Overall Accuracy | - | - | - | 0.9042 |
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.
It may be incorporated into monetary policy forecasting tools, financial sentiment dashboards, or market reaction models.
This model is not intended for real-time trading decisions, legal interpretation of monetary policy, or classification of unrelated financial texts.
Use in conjunction with economic domain knowledge. Model performance should be monitored when applied to new types of central bank communication.
from transformers import pipeline
# Load the classifier pipeline
classifier = pipeline("text-classification", model="mrince/CBRT-RoBERTa-Large-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': 0.9983660578727722}]
# 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.