This model classifies whether a tweet is related to a real disaster or not using the DistilBERT transformer model. It was fine-tuned on the Kaggle "Natural Language Processing with Disaster Tweets" dataset.
Dataset
Dataset: Natural Language Processing with Disaster Tweets (Kaggle)
Labels:
0 = Not Disaster
1 = Disaster
Model
Base Model: distilbert-base-uncased
Framework: Hugging Face Transformers
Task: Binary Text Classification
Training Details
Epochs: 3
Learning Rate: 2e-5
Batch Size: 16
Optimizer: AdamW
Evaluation Results
Metric
Score
Accuracy
82.52%
Precision
82.52%
Recall
82.52%
F1-Score
82.39%
Example
Input:
A massive earthquake destroyed several buildings.
Prediction:
Disaster
Limitations
The model may misclassify sarcastic or ambiguous tweets.
Performance depends on the quality of the input text.
Intended Use
This model is intended for educational purposes and disaster tweet classification research.
