# Disaster News VAD Model

This model predicts Valence, Arousal, and Dominance (VAD) values for disaster news headlines. 
It was trained on the EmoBank dataset and fine-tuned on disaster news headlines.

## Model Details

- **Architecture**: RoBERTa-based transformer model with regression heads for VAD prediction
- **Training Data**: EmoBank dataset
- **Application**: Emotional analysis of disaster news headlines
- **Date**: 2025-03-16

## Usage

```python
from transformers import RobertaTokenizer, AutoModel
import torch

# Load model and tokenizer
tokenizer = RobertaTokenizer.from_pretrained("postgrammar/disaster-news-vad-model")
model = AutoModel.from_pretrained("postgrammar/disaster-news-vad-model")

# Prepare input
text = "Earthquake devastates coastal town, rescue efforts underway"
inputs = tokenizer(text, return_tensors="pt")

# Get predictions
with torch.no_grad():
    outputs = model(**inputs)
    
# Extract VAD values (first three values in the output tuple)
valence, arousal, dominance = outputs[0], outputs[1], outputs[2]

print(f"Valence: {valence.item():.4f}, Arousal: {arousal.item():.4f}, Dominance: {dominance.item():.4f}")
```

## Citation

If you use this model, please cite:

```
@misc{disaster-news-vad-model,
  author = {Hamed Yaghoobian},
  title = {Disaster News VAD Model},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/postgrammar/disaster-news-vad-model}}
}
```