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

Label	Description	Examples
`mechanical_failure`	Equipment/system malfunctions	Engine failures, hydraulic leaks, electrical issues
`human_error`	Crew mistakes or procedural errors	Wrong settings, missed checklists, miscommunication
`weather_related`	Weather-induced incidents	Turbulence, icing, windshear, storms
`bird_strike`	Wildlife collisions	Bird ingestion, flock encounters
`ground_incident`	Ground operations issues	Runway incursions, vehicle collisions, ramp incidents

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

Split	Samples
Train	400
Eval	100
Total	500

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

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
1.626	1.0	25	1.5861	0.28	0.1813
1.2794	2.0	50	1.1491	0.57	0.4570
0.9383	3.0	75	0.8447	0.71	0.7000
0.2942	4.0	100	0.3566	0.90	0.9007
0.1401	5.0	125	0.3002	0.94	0.9386
0.1607	6.0	150	0.3129	0.94	0.9402
0.0038	7.0	175	0.3071	0.95	0.9504
0.0022	8.0	200	0.3248	0.95	0.9504
0.0015	9.0	225	0.3409	0.95	0.9504
0.0012	10.0	250	0.3545	0.95	0.9504

Usage

python

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}]