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-# iLLAMA: LLM for App Issue Detection and Prioritization Obtained by Fine-Tuning LLAMA 3
-
-This repository contains a fine-tuned version of LLAMA 3 using the Unsloth framework and the vitormesaque/irisk dataset. The model is designed for detecting issues in text data.
-
-## Model Details
-
-- **Developed by:** [Vitor Mesaque](https://huggingface.co/vitormesaque)
-- **Model type:** App Issue Detection Model
-- **Language:** English
-- **License:** MIT
-- **Fine-tuned from:** LLAMA 3
-- **Datasets:** [vitormesaque/irisk](https://huggingface.co/datasets/vitormesaque/irisk)
-
-The vitormesaque/irisk dataset was obtained through the knowledge base of the MApp-IDEA research project.
-
-## Model Usage
-
-### How to Get Started with the Model
-
-Use the code below to get started with the model:
-
-
-
-### Evaluation
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-The model was evaluated using a separate portion of the vitormesaque/irisk dataset.
-
-## Bias, Risks, and Limitations
-
-While the model is effective in detecting issues, it may exhibit biases present in the training data. Users should be aware of these potential biases and consider them when interpreting results.
-
-### Recommendations
-
-Users should conduct additional evaluations in the specific context of use to ensure reliability and fairness.
-
-## Citation
-
-If you use this model in your research, please cite it as follows:
-
-**BibTeX:**
-
-```bibtex
-@misc{vitormesaque2024llama3,
-  author = {Vitor Mesaque Alves de Lima},
-  title = {iLLAMA: LLM for App Issue Detection and Prioritization Obtained by Fine-Tuning LLAMA 3},
-  year = {2024},
-  url = {https://huggingface.co/vitormesaque}
-}
-```
-
-**APA:**
-
-Mesaque, V. (2024). LLAMA 3 fine-tuned with Unsloth and vitormesaque/irisk dataset. Retrieved from https://huggingface.co/vitormesaque
-
-## License
-
-This model is licensed under the MIT License.
-
-## Contact
-
-For questions or comments, please contact [Vitor Mesaque](https://huggingface.co/vitormesaque).