nasa_incident_classifier

NASA Aviation Incident Classifier

This model is a fine-tuned version of microsoft/deberta-v3-large on the NASA Aviation Incident Reports dataset.

It achieves the following results on the evaluation set:

• Loss: 0.3248
• Accuracy: 95%
• F1 Score: 0.9504

Model Description

A state-of-the-art text classification model designed to categorize aviation incident reports into five distinct categories. Built on DeBERTa-v3-large (435M parameters), this model leverages disentangled attention mechanisms for superior natural language understanding of technical aviation terminology.

Classification Categories

Intended Uses & Limitations

Intended Uses

• Automated classification of aviation safety reports (ASRS-style)
• Safety data analysis and trend identification
• Training data augmentation for aviation NLP research
• Educational demonstrations of transformer-based text classification

Limitations

• Trained on synthetic data modeled after NASA ASRS reports
• Best suited for English-language incident narratives
• May struggle with highly ambiguous multi-cause incidents
• Not validated for operational safety-critical decisions

Training and Evaluation Data

Dataset: neurontorch/nasa_aviation_incident_reports

Each category contains 100 balanced samples with professional aviation terminology and root-cause-based labeling.

Training Procedure

Training Hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: AdamW (fused) with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 20 (early stopping at epoch 10)
• mixed_precision_training: Native AMP (FP16)

Training Results

Usage

from transformers import pipeline

classifier = pipeline("text-classification", model="neurontorch/nasa_incident_classifier")

# Example inference
text = "Engine oil pressure warning illuminated during cruise at FL350. Crew followed QRH procedures and diverted."
result = classifier(text)
print(result)  # [{'label': 'mechanical_failure', 'score': 0.98}]