You can deploy this model and start earning money today!

postgrammar/
disaster-news-vad-model

Name: disaster-news-vad-model
Availability: InStock

pytorch

roberta

Views

No views yet

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}}
}