---
library_name: transformers
license: apache-2.0
base_model: microsoft/deberta-v3-large
datasets:
  - neurontorch/nasa_aviation_incident_reports
language:
  - en
pipeline_tag: text-classification
tags:
  - generated_from_trainer
  - aviation
  - incident-classification
  - deberta
  - text-classification
metrics:
  - accuracy
  - f1
model-index:
- name: nasa_incident_classifier
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: NASA Aviation Incident Reports
      type: neurontorch/nasa_aviation_incident_reports
    metrics:
    - type: accuracy
      value: 0.95
    - type: f1
      value: 0.9504
---

# NASA Aviation Incident Classifier

This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the [NASA Aviation Incident Reports](https://huggingface.co/datasets/neurontorch/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

| 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](https://huggingface.co/datasets/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}]