gtfintechlab/model_bank_negara_malaysia_time_label

World of Central Banks Model

Model Name: Central Bank of Malaysia (Bank Negara Malaysia) Temporal Classification Model

Model Type: Text Classification

Language: English

License: CC-BY-NC-SA 4.0

Base Model: bert-base-uncased

Dataset Used for Training: gtfintechlab/bank_negara_malaysia

Model Overview

Central Bank of Malaysia (Bank Negara Malaysia) Temporal Classification Model is a fine-tuned bert-base-uncased model designed to classify text data on Temporal Classification. 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 Temporal Classification 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=2)
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 Temporal Classification
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 Temporal Classification.

Ensure your environment has the necessary dependencies installed.

Label Interpretation

• LABEL_0: Forward-looking; the sentence discusses future economic events or decisions.
• LABEL_1: Not forward-looking; the sentence discusses past or current economic events or decisions.

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 Temporal Classification. 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