AWCO/mdeberta-v3-base-narratives-classifier-hierarchical

mDeBERTa v3 Base - Hierarchical Narratives Classifier

This model is a fine-tuned version of microsoft/mdeberta-v3-base for hierarchical text classification of narratives related to Climate Change and Ukraine-Russia War.

Model Description

This model performs hierarchical classification on text documents to identify:

1. Main Narrative Categories: Climate Change (CC) and Ukraine-Russia War (URW) related narratives
2. Sub-Narrative Classifications: Detailed subcategories within each main narrative

Supported Languages

The model has been trained and tested on multiple languages:

• English (EN)
• Bulgarian (BG)
• Hindi (HI)
• Portuguese (PT)
• Russian (RU)

Usage

from transformers import AutoTokenizer, AutoModel
import torch

# Load model and tokenizer
model_name = "AWCO/mdeberta-v3-base-narratives-classifier-hierarchical"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

# Example usage for inference
text = "Your text here..."
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)

with torch.no_grad():
    outputs = model(**inputs)
    # Process outputs according to your inference pipeline

Training Data

The model was trained on a custom dataset containing narratives related to:

• Climate Change discussions and misinformation
• Ukraine-Russia War narratives and propaganda

Performance

The model achieves strong performance across multiple languages for both narrative-level and sub-narrative level classification tasks.

Model Files

• config.json: Model configuration
• model.safetensors: Model weights in SafeTensors format
• tokenizer.json: Tokenizer configuration
• label_mappings_hierarchical.json: Hierarchical label mappings for classification

Citation

If you use this model in your research, please cite accordingly.

License

This model is released under the MIT License.