gtfintechlab/model_bank_negara_malaysia_stance_label

World of Central Banks Model

Model Name: Central Bank of Malaysia (Bank Negara Malaysia) Stance Detection Model

Model Type: Text Classification

Language: English

License: CC-BY-NC-SA 4.0

Base Model: ModernBERT-base

Dataset Used for Training: gtfintechlab/bank_negara_malaysia

Model Overview

Central Bank of Malaysia (Bank Negara Malaysia) Stance Detection Model is a fine-tuned ModernBERT-base model designed to classify text data on Stance Detection. This label is annotated in the bank_negara_malaysia dataset, which focuses on meeting minute-level documents for the Central Bank of Malaysia (Bank Negara Malaysia).

Intended Use

This model is intended for researchers and practitioners working on subjective text classification for the Central Bank of Malaysia (Bank Negara Malaysia), particularly within financial and economic contexts. It is specifically designed to assess the Stance Detection label, aiding in the analysis of subjective content in financial and economic communications.

How to Use

To utilize this model, load it using the Hugging Face transformers library:

from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoConfig

# Load tokenizer, model, and configuration
tokenizer = AutoTokenizer.from_pretrained("gtfintechlab/bank_negara_malaysia", do_lower_case=True, do_basic_tokenize=True)
model = AutoModelForSequenceClassification.from_pretrained("gtfintechlab/bank_negara_malaysia", num_labels=4)
config = AutoConfig.from_pretrained("gtfintechlab/bank_negara_malaysia")

# Initialize text classification pipeline
classifier = pipeline('text-classification', model=model, tokenizer=tokenizer, config=config, framework="pt")

# Classify Stance Detection
sentences = [
    "[Sentence 1]",
    "[Sentence 2]"
]
results = classifier(sentences, batch_size=128, truncation="only_first")

print(results)

In this script:

• Tokenizer and Model Loading:
  Loads the pre-trained tokenizer and model from gtfintechlab/bank_negara_malaysia.

• Configuration:
  Loads model configuration parameters, including the number of labels.

• Pipeline Initialization:
  Initializes a text classification pipeline with the model, tokenizer, and configuration.

• Classification:
  Labels sentences based on Stance Detection.

Ensure your environment has the necessary dependencies installed.

Label Interpretation

• LABEL_0: Hawkish; the sentnece supports contractionary monetary policy.
• LABEL_1: Dovish; the sentence supports expansionary monetary policy.
• LABEL_2: Neutral; the sentence contains neither hawkish or dovish sentiment, or both hawkish and dovish sentiment.
• LABEL_3: Irrelevant; the sentence is not related to monetary policy.

Training Data

The model was trained on the bank_negara_malaysia dataset, comprising annotated sentences from the Central Bank of Malaysia (Bank Negara Malaysia) meeting minute-level documents, labeled by Stance Detection. The dataset includes training, validation, and test splits.

Citation

If you use this model in your research, please cite the bank_negara_malaysia:

@article{WCBShahSukhaniPardawala,
  title={Words That Unite The World: A Unified Framework for Deciphering Global Central Bank Communications},
  author={Agam Shah, Siddhant Sukhani, Huzaifa Pardawala et al.},
  year={2025}
}

For more details, refer to the bank_negara_malaysia dataset documentation.

Contact

For any bank_negara_malaysia related issues and questions, please contact:

• Huzaifa Pardawala: huzaifahp7[at]gatech[dot]edu

• Siddhant Sukhani: ssukhani3[at]gatech[dot]edu

• Agam Shah: ashah482[at]gatech[dot]edu