---
license: cc-by-nc-sa-4.0

datasets:
- gtfintechlab/WCB

language:
- en

metrics:
- accuracy
- f1
- precision
- recall

base_model:
- roberta-base

pipeline_tag: text-classification

library_name: transformers
---

# World of Central Banks Model

**Model Name:** WCB Temporal Classification Model

**Model Type:** Text Classification

**Language:** English

**License:** [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en)

**Base Model:** [RoBERTa](https://huggingface.co/FacebookAI/roberta-base)

**Dataset Used for Training:** [gtfintechlab/all_annotated_sentences_25000](https://huggingface.co/datasets/gtfintechlab/all_annotated_sentences_25000)

## Model Overview

WCB Temporal Classification Model is a fine-tuned RoBERTa-based model designed to classify text data on **Temporal Classification**. This label is annotated in the model_WCB_time_label dataset, which focuses on meeting minutes for the all 25 central banks, listed in the paper _Words That Unite The World: A Unified Framework for Deciphering Global Central Bank Communications_.

## Intended Use

This model is intended for researchers and practitioners working on subjective text classification, 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:

```python
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoConfig

# Load tokenizer, model, and configuration
tokenizer = AutoTokenizer.from_pretrained("gtfintechlab/model_WCB_time_label", do_lower_case=True, do_basic_tokenize=True)
model = AutoModelForSequenceClassification.from_pretrained("gtfintechlab/model_WCB_time_label", num_labels=2)
config = AutoConfig.from_pretrained("gtfintechlab/model_WCB_time_label")

# 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/model_WCB_time_label`.

- **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 model_WCB_time_label dataset, comprising annotated sentences from 25 central banks, labeled by Temporal Classification. The dataset includes training, validation, and test splits.

## Citation

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

```bibtex
@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 [model_WCB_time_label dataset documentation](https://huggingface.co/gtfintechlab/model_WCB_time_label).

## Contact

For any model_WCB_time_label 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