Update README.md

README.md

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-library_name: transformers
-tags: []
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+# Design Pattern Detection Model
 
-# Model Card for Model ID
+This model detects software design patterns in Java source code using CodeBERT. The model has been fine-tuned for single-label classification tasks and supports the following design pattern labels:
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Supported Labels
 
+| Label ID | Design Pattern     |
+|----------|--------------------|
+| 0        | Observer           |
+| 1        | Decorator          |
+| 2        | Adapter            |
+| 3        | Proxy              |
+| 4        | Singleton          |
+| 5        | Facade             |
+| 6        | AbstractFactory    |
+| 7        | Memento            |
+| 8        | FactoryMethod      |
+| 9        | Prototype          |
+| 10       | Visitor            |
+| 11       | Builder            |
+| 12       | Unknown            |
 
+## How to Use
 
-## Model Details
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-### Model Description
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("ichsanbudiman/design-pattern-detection-codebert")
+model = AutoModelForSequenceClassification.from_pretrained("ichsanbudiman/design-pattern-detection-codebert")
 
-<!-- Provide a longer summary of what this model is. -->
+# Example input
+input_code = """
+public class Singleton {
+    private static Singleton instance;
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+    private Singleton() {}
 
-- **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]
+    public static Singleton getInstance() {
+        if (instance == null) {
+            instance = new Singleton();
+        }
+        return instance;
+    }
+}
+"""
 
-### Model Sources [optional]
+# Tokenize the input
+inputs = tokenizer(input_code, return_tensors="pt", padding="max_length", truncation=True, max_length=512)
 
-<!-- Provide the basic links for the model. -->
+# Make predictions
+with torch.no_grad():
+    outputs = model(**inputs)
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+# Get the predicted class and label
+predicted_class = torch.argmax(outputs.logits, dim=1).item()
+predicted_label = model.config.id2label[predicted_class]
 
-## Uses
+print(f"Predicted label: {predicted_label}")
+```
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+## Input Requirements
+- **Input Format**: Java code snippets as strings.
+- **Max Length**: Input code longer than 512 tokens will be truncated.
+- **Padding**: Automatically pads to 512 tokens for batch processing.
 
-### Direct Use
+## Task
+This model performs single-label classification for the detection of design patterns in Java source code. The supported design patterns are listed above.
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+## Fine-Tuning Details
+- **Base Model**: [CodeBERT](https://huggingface.co/microsoft/codebert-base)
+- **Dataset**: Fine-tuned on a curated dataset of labeled Java code examples. The dataset was sourced from the following research article:
 
-[More Information Needed]
+  > Najam Nazar, Aldeida Aleti, Yaokun Zheng, Feature-based software design pattern detection, Journal of Systems and Software, Volume 185, 2022, 111179, ISSN 0164-1212, [https://doi.org/10.1016/j.jss.2021.111179](https://doi.org/10.1016/j.jss.2021.111179).
 
-### Downstream Use [optional]
+- **Metrics**: The model achieves high accuracy on detecting design patterns, making it suitable for software engineering tasks.
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+## Contact
+For inquiries or feedback, please reach out to [Ichsan Budiman](mailto:[email protected]).
 
-[More Information Needed]
+## License
+This model is licensed under the Apache 2.0 License.
 
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical 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.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- 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. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[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. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- 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]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[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]
-
-#### 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]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]