### Model Card for Defense BERT Classifier

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# 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](https://huggingface.co/bayrameker/defense-bert-classifier)

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## 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.

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## 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.

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## How to Get Started with the Model

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

```python
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)
```

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## 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

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## 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.

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## 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.

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## 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.

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## Citation

If you use this model, please cite it as:

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

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### Contact

For questions or further details, please contact: [Bayram Eker](https://huggingface.co/bayrameker).