AI4SDG-BERT Model Card

Model Overview

AI4SDG-BERT is a fine-tuned BERT model for detecting disaster-related content in tweets. It supports real-time monitoring of social media data to identify and classify potential urban disasters. This model contributes to Sustainable Development Goal 11 (Sustainable Cities and Communities) by facilitating early warning and emergency response systems.

Intended Use

This model is intended for:

• Disaster detection from Twitter or other social platforms
• Urban emergency alert systems
• Smart city infrastructure monitoring
• Use by NGOs, civil defense units, or emergency data pipelines

Model Architecture

• Base model: bert-base-uncased
• Fine-tuned on: Disaster Tweet Classification Dataset (binary classification: disaster vs. not disaster)
• Framework: Hugging Face Transformers + PyTorch
• Post-BERT Classifier: Random Forest trained on BERT [CLS] embeddings

Training Dataset

• Class distribution: 43% disaster, 57% non-disaster
• Preprocessing: Lowercase, punctuation/emoji removal, URL stripping

Evaluation

Evaluated on held-out test set from the dataset:

• Accuracy: 92%
• F1-score: 91%
• AUC: 94%

Limitations

• English-only model
• May not generalize well to sarcasm, spam, or very informal text
• Lacks direct geolocation unless paired with NER/metadata
• Relies on Twitter API access for live inference

Ethical Considerations

• May reflect bias in who reports disasters and how
• Not intended as a sole source for life-critical decision making
• Requires moderation to avoid misinformation risks

Example Usage

from transformers import pipeline
classifier = pipeline("text-classification", model="elam2909/bert-disaster-classifier")
classifier("Huge earthquake just hit southern Turkey. Buildings are collapsing!")