World of Central Banks Model
Model Name: Federal Reserve Temporal Classification Model
Model Type: Text Classification
Language: English
License: CC-BY-NC-SA 4.0
Base Model: roberta-base
Dataset Used for Training: gtfintechlab/federal_reserve_system
Model Overview
Federal Reserve Temporal Classification Model is a fine-tuned roberta-base model designed to classify text data on Temporal Classification. This label is annotated in the federal_reserve_system dataset, which focuses on meeting minutes for the Federal Reserve.
Intended Use
This model is intended for researchers and practitioners working on subjective text classification for the Federal Reserve, 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/federal_reserve_system", do_lower_case=True, do_basic_tokenize=True)
model = AutoModelForSequenceClassification.from_pretrained("gtfintechlab/federal_reserve_system", num_labels=2)
config = AutoConfig.from_pretrained("gtfintechlab/federal_reserve_system")
# 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 fromgtfintechlab/federal_reserve_system
.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 federal_reserve_system dataset, comprising annotated sentences from the Federal Reserve meeting minutes, labeled by Temporal Classification. The dataset includes training, validation, and test splits.
Citation
If you use this model in your research, please cite the federal_reserve_system:
@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 federal_reserve_system dataset documentation.
Contact
For any federal_reserve_system 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
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