gtfintechlab/model_reserve_bank_of_australia_stance_label

World of Central Banks Model

Model Name: Reserve Bank of Australia Stance Detection Model

Model Type: Text Classification

Language: English

License: CC-BY-NC-SA 4.0

Base Model: roberta-base

Dataset Used for Training: gtfintechlab/reserve_bank_of_australia

Model Overview

Reserve Bank of Australia Stance Detection Model is a fine-tuned roberta-base model designed to classify text data on Stance Detection. This label is annotated in the reserve_bank_of_australia dataset, which focuses on meeting minutes for the Reserve Bank of Australia.

Intended Use

This model is intended for researchers and practitioners working on subjective text classification for the Reserve Bank of Australia, 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/reserve_bank_of_australia", do_lower_case=True, do_basic_tokenize=True)
model = AutoModelForSequenceClassification.from_pretrained("gtfintechlab/reserve_bank_of_australia", num_labels=4)
config = AutoConfig.from_pretrained("gtfintechlab/reserve_bank_of_australia")

# 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/reserve_bank_of_australia.

• 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 reserve_bank_of_australia dataset, comprising annotated sentences from the Reserve Bank of Australia meeting minutes, labeled by Stance Detection. The dataset includes training, validation, and test splits.

Citation

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

@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 reserve_bank_of_australia dataset documentation.

Contact

For any reserve_bank_of_australia 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