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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
--- |