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README.md

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----
-library_name: transformers
-tags: []
----
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+### Model Card for Defense BERT Classifier
 
+---
 
-## Model Details
+# Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+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`). 
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+- **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`
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+### Model Sources
 
-### Model Sources [optional]
+- **Repository:** [Hugging Face Model Page](https://huggingface.co/bayrameker/defense-bert-classifier)
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+---
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+The model can be directly used for binary classification tasks, especially for categorizing text as defense-related or not.
 
-### Downstream Use [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+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
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+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.
 
-[More Information Needed]
+---
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+### Risks
 
-[More Information Needed]
+- 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.
 
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+### Limitations
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+- 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).
 
-## How to Get Started with the Model
+### Recommendations
 
-Use the code below to get started with the model.
+- 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.
 
-[More Information Needed]
+---
 
-## Training Details
+## How to Get Started with the Model
 
-### Training Data
+You can load and test the model using the following code:
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+```python
+from transformers import pipeline
 
-[More Information Needed]
+# Load the model
+classifier = pipeline("text-classification", model="bayrameker/defense-bert-classifier")
 
-### Training Procedure
+# 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."
+]
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+# Predictions
+for text in texts:
+    result = classifier(text)
+    print(f"Text: {text}")
+    print(f"Prediction: {result}")
+    print("-" * 50)
+```
 
-#### Preprocessing [optional]
+---
 
-[More Information Needed]
+## Training Details
 
+### Training Data
 
-#### Training Hyperparameters
+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 regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+### Training Procedure
 
-#### Speeds, Sizes, Times [optional]
+The model was trained for 5 epochs using the following settings:
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+- **Optimizer:** AdamW
+- **Learning rate:** `2e-5`
+- **Batch size:** 4 (train), 8 (validation)
+- **Evaluation strategy:** Epoch-based
+- **Weight Decay:** 0.01
 
-[More Information Needed]
+---
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
+### Testing Data
 
-[More Information Needed]
+The testing dataset consisted of examples from the training data's domain and context. The accuracy was approximately **83%**, indicating acceptable but improvable performance.
 
-#### Factors
+### Metrics
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+The evaluation was conducted using standard binary classification metrics such as precision, recall, F1-score, and accuracy.
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
+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 [optional]
+## Model Examination
 
-<!-- Relevant interpretability work for the model goes here -->
+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.
 
-[More Information Needed]
+---
 
 ## Environmental Impact
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
+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.
 
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
+---
 
-## More Information [optional]
+## Citation
 
-[More Information Needed]
+If you use this model, please cite it as:
 
-## Model Card Authors [optional]
+```plaintext
+Bayram Eker, Defense BERT Classifier, 2024. Available at https://huggingface.co/bayrameker/defense-bert-classifier.
+```
 
-[More Information Needed]
+---
 
-## Model Card Contact
+### Contact
 
-[More Information Needed]
+For questions or further details, please contact: [Bayram Eker](https://huggingface.co/bayrameker).