Model Card for Defense BERT Classifier

Model Details

Model Description

This is a fine-tuned version of the bert-base-uncased model for a binary text classification task. The model predicts whether a given text is related to defense topics (LABEL_1) or not (LABEL_0).

Developed by: Bayram Eker
Funded by: Self-initiated project
Model type: BERT-based binary classifier
Language(s): English
License: Apache 2.0
Fine-tuned from: bert-base-uncased

Model Sources

Repository: Hugging Face Model Page

Uses

Direct Use

The model can be directly used for binary classification tasks, especially for categorizing text as defense-related or not.

Downstream Use

The model can be fine-tuned further for related tasks or used as-is for applications involving text categorization in the defense domain.

Out-of-Scope Use

The model may not perform well on tasks outside its training scope, such as multi-class classification, domain-specific subcategories, or other unrelated text analysis.

Bias, Risks, and Limitations

Risks

The model was trained on a small and simple dataset, which may not generalize well to all defense-related contexts.
Imbalanced classes in the dataset may lead to biased predictions, favoring the dominant label.

Limitations

The training dataset includes only basic examples and may not cover nuanced or complex defense-related topics.
Misclassifications may occur for texts with ambiguous contexts or overlapping themes (e.g., cybersecurity, geopolitics).

Recommendations

It is recommended to fine-tune the model on a larger, balanced, and more diverse dataset for improved performance.
Use additional preprocessing steps to ensure input data quality for better predictions.

How to Get Started with the Model

You can load and test the model using the following code:

from transformers import pipeline

# Load the model
classifier = pipeline("text-classification", model="bayrameker/defense-bert-classifier")

# Example texts
texts = [
    "The military conducted joint exercises to enhance readiness.",
    "The government approved increased spending on national security.",
    "A new bakery opened downtown, offering a variety of pastries.",
    "The movie was a thrilling adventure set in space."
]

# Predictions
for text in texts:
    result = classifier(text)
    print(f"Text: {text}")
    print(f"Prediction: {result}")
    print("-" * 50)

Training Details

Training Data

The model was fine-tuned on a small, simple dataset containing sentences labeled as defense-related or not based on their context. The dataset was synthetically generated and not domain-specific.

Training Procedure

The model was trained for 5 epochs using the following settings:

Optimizer: AdamW
Learning rate: 2e-5
Batch size: 4 (train), 8 (validation)
Evaluation strategy: Epoch-based
Weight Decay: 0.01

Evaluation

Testing Data

The testing dataset consisted of examples from the training data's domain and context. The accuracy was approximately 83%, indicating acceptable but improvable performance.

Metrics

The evaluation was conducted using standard binary classification metrics such as precision, recall, F1-score, and accuracy.

Results

Example predictions from the model:

"The military conducted joint exercises to enhance readiness.": Predicted LABEL_0 (Not Defense) with 95.2% confidence.
"The government approved increased spending on national security.": Predicted LABEL_1 (Defense) with 66.6% confidence.
"A new bakery opened downtown, offering a variety of pastries.": Predicted LABEL_1 (Defense) with 55.9% confidence.

These results indicate areas where the model can be improved, particularly in distinguishing nuanced cases.

Model Examination

The model shows high confidence for certain classes but struggles with borderline or ambiguous cases. This behavior can be addressed by improving the training dataset's quality and diversity.

Environmental Impact

Training the model on a simple dataset required minimal computational resources, resulting in negligible environmental impact. However, larger-scale training would require significant hardware and energy.

Citation

If you use this model, please cite it as:

Bayram Eker, Defense BERT Classifier, 2024. Available at https://huggingface.co/bayrameker/defense-bert-classifier.

Contact

For questions or further details, please contact: Bayram Eker.