Update README.md

README.md

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----
-license: apache-2.0
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+---
+license: apache-2.0
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- huawei-noah/TinyBERT_General_4L_312D
+tags:
+- TaxonomyOfVideoGameBugs
+---
+# Invalid Graphical Representation Bug Detection Model
+
+This model is trained to detect **Invalid Graphical Representation** issues in video games. It identifies bugs where aspects of the world state are incorrectly rendered. For example, it can detect when a character is performing a swimming animation while on land, or when a clothing item is not appearing as intended in the game.
+
+The model is based on concepts from the paper [_"What went wrong; the taxonomy of video game bugs"_](https://users.soe.ucsc.edu/~ejw/papers/lewis-taxonomy-fdg2010.pdf).
+
+## Model Details
+
+- **Model Type**: Classification (Binary)
+- **Training Data**: Trained on a dataset of video game bugs, specifically focused on **Invalid Graphical Representation** as described in the paper _"What went wrong; the taxonomy of video game bugs"_. 
+- **Task**: Bug detection in video games.
+- **Intended Use**: This model is designed for game developers and QA teams to automate the detection of rendering issues or animation bugs in video games.
+
+## Training Metrics
+
+The model was trained for 4 epochs, with the following performance metrics:
+
+| Epoch | Training Loss | Validation Loss | Accuracy | F1 Score | Precision | Recall  |
+|-------|---------------|-----------------|----------|----------|-----------|---------|
+| 1     | 0.6919        | 0.6906          | 61.75%   | 0.7086   | 0.5688    | 0.9394  |
+| 2     | 0.6707        | 0.6646          | 67.25%   | 0.6289   | 0.7161    | 0.5606  |
+| 3     | 0.6181        | 0.5799          | 76.50%   | 0.7939   | 0.7016    | 0.9141  |
+| 4     | 0.4611        | 0.4110          | 87.00%   | 0.8791   | 0.8147    | 0.9545  |
+
+### Key Metrics:
+- **F1 Score**: Balances precision and recall, with a final value of 0.8791 after 4 epochs.
+- **Precision**: The accuracy of positive predictions.
+- **Recall**: The ability to detect true positives.
+
+## Intended Audience
+
+- **Game Developers**: To detect graphical and animation bugs in video games.
+- **Quality Assurance (QA) Teams**: To automate the detection of rendering or animation issues during game testing.
+- **Researchers**: Interested in analyzing or extending bug detection models for video games.
+
+
+## Limitations:
+- This model is specifically trained to detect Invalid Graphical Representation bugs, focusing on issues like animation errors and missing rendered items.
+- It may not generalize well to other types of video game bugs outside of this category.
+- Performance can vary depending on the game context and rendering engine. Further fine-tuning may be required for use in different games.
+
+## How to Use
+
+You can use the model for **binary classification** to predict whether a given game state exhibits an **Invalid Graphical Representation** bug. Here's an example using the Hugging Face `transformers` library:
+
+```python
+from transformers import pipeline
+
+# Load the model from Hugging Face
+bug_detection = pipeline('text-classification', model='fyp-buglens/VideoGameReviews-InvalidGraphicalRepresentation-TinyBERT')
+
+# Example usage
+result = bug_detection("A character is swimming on land")
+print(result)  # Output: label indicating if it's a bug or not
+
+
+---
+license: apache-2.0
+---