A fine-tuned DeBERTa-v3 model for classifying marine incident types based on accident investigation reports from the Marine Accident Investigation Branch (MAIB).

Model Description

This model is a fine-tuned version of microsoft/deberta-v3-base specifically designed to classify marine incidents into 11 different categories. It was trained on the MAIB incident reports dataset and achieves high performance in maritime safety incident classification.

Developed by: Ilia Munaev
Model type: Text Classification
Language(s): English
License: Apache 2.0
Finetuned from model: microsoft/deberta-v3-base

Model Performance

The model achieves the following performance metrics on the test set:

Metric	Score
Accuracy	89.0%
Weighted F1-Score	89.0%
Macro F1-Score	70.2%

Evaluation Results

The model evaluation:

Confusion Matrix: Shows classification accuracy across all incident types
Per-Class F1 Scores: Displays F1 performance for each incident category

Intended Use

Primary Use Cases

Maritime Safety Analysis: Classify marine incident reports for safety analysis
Regulatory Compliance: Automate incident categorization for regulatory reporting
Risk Assessment: Support risk analysis by categorizing incident types
Research: Academic and industry research on maritime safety patterns

Out-of-Scope Use Cases

Real-time Emergency Response: Not suitable for emergency situations requiring immediate response
Legal Proceedings: Should not be used as primary evidence in legal cases
Non-English Text: Model is trained only on English incident reports

Training Data

The model was trained on the baker-street/maib-incident-reports-5K dataset, which contains:

Total Samples: 5,768 incident reports
Training Set: 5,191 samples
Validation Set: 288 samples
Test Set: 289 samples
Source: Marine Accident Investigation Branch (MAIB) reports
Language: English
Time Period: Historical MAIB incident reports

Training Procedure

Training Hyperparameters

Learning Rate: 2e-5
Batch Size: 32
Epochs: 3
Max Length: 256 tokens
Optimizer: AdamW
Scheduler: Linear with warmup

Training Infrastructure

Hardware: CUDA-compatible GPU (Tesla T4)
Training Time: ~16 minutes for 3 epochs
Framework: PyTorch with Transformers library

Usage

Using Transformers Pipeline

python

from transformers import pipeline

# Load the model
classifier = pipeline("text-classification",
                     model="your-username/maib-incident-classifier")

# Classify an incident
result = classifier("A crew member fell overboard from a motorboat")
print(result)

Using Model Directly

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("your-username/maib-incident-classifier")
model = AutoModelForSequenceClassification.from_pretrained("your-username/maib-incident-classifier")

# Prepare input
text = "Fire broke out in the engine room during routine maintenance"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)

# Get predictions
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)

# Get class labels
class_labels = [
    "Accident to person(s)",
    "Capsizing / Listing",
    "Collision",
    "Contact",
    "Damage / Loss Of Equipment",
    "Fire / Explosion",
    "Flooding / Foundering",
    "Grounding / Stranding",
    "Hull Failure",
    "Loss Of Control",
    "Non-accidental Event"
]

# Get top prediction
top_prediction = torch.argmax(predictions, dim=-1)
print(f"Predicted class: {class_labels[top_prediction]}")
print(f"Confidence: {predictions[0][top_prediction]:.3f}")

Using the Command Line

bash

# Install the package
pip install maib-incident-classifier

# Run inference
maib-inference --model_path your-username/maib-incident-classifier --text "Incident description"

Class Labels

The model classifies incidents into the following 11 categories:

Accident to person(s) - Injuries or fatalities to crew or passengers
Capsizing / Listing - Vessel capsizing or severe listing
Collision - Collision with another vessel or object
Contact - Contact with fixed or floating objects
Damage / Loss Of Equipment - Equipment failure or damage
Fire / Explosion - Fire or explosion incidents
Flooding / Foundering - Water ingress or vessel sinking
Grounding / Stranding - Vessel running aground
Hull Failure - Structural hull damage
Loss Of Control - Loss of steering or propulsion control
Non-accidental Event - Events not classified as accidents

Limitations and Bias

Known Limitations

Class Imbalance: Some incident types (Hull Failure, Non-accidental Event) have very few samples
Language: Model only works with English text
Domain Specific: Trained specifically on MAIB reports, may not generalize to other maritime contexts
Temporal Bias: Based on historical data, may not reflect current incident patterns

Potential Biases

Reporting Bias: Reflects biases in how incidents are reported to MAIB
Geographic Bias: Primarily UK-focused incident reports
Vessel Type Bias: May be biased toward certain vessel types more commonly reported

Citation

bibtex

@software{maib_classifier,
  title={MAIB Incident Type Classifier},
  author={Ilia Munaev},
  year={2024},
  url={https://huggingface.co/your-username/maib-incident-classifier}
}

Acknowledgments

Marine Accident Investigation Branch (MAIB) for providing the dataset
Microsoft for the DeBERTa-v3 base model
Hugging Face for the transformers library and platform
Baker Street for hosting the MAIB incident reports dataset

Contact

For questions, issues, or contributions:

Repository: [GitHub Repository URL]
Issues: [GitHub Issues URL]
Email: team@maib-classifier.com

License

This model is released under the Apache 2.0 License. See the LICENSE file for more details.

baker-street/maib-incident-classifier

MAIB Incident Type Classifier