--- license: mit datasets: - HuggingFaceFW/fineweb language: - ar library_name: fastai pipeline_tag: reinforcement-learning tags: - medical --- # Model Card for BrainSAIT Model This model card aims to provide detailed information about the BrainSAIT model. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description BrainSAIT is a reinforcement learning model developed for medical applications. It has been fine-tuned using the Arabic language dataset from HuggingFaceFW/fineweb. The model utilizes the fastai library for its implementation. - **Developed by:** BrainSAIT Team - **Funded by [optional]:** [Dr.Mohamed El Fadil] - **Shared by [optional]:** [Dr.Mohamed El Fadil] - **Model type:** Reinforcement Learning - **Language(s) (NLP):** Arabic - **License:** MIT - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses ### Direct Use The BrainSAIT model can be used directly for tasks related to medical data analysis and decision-making support in the Arabic language. ### Downstream Use [optional] The model can be further fine-tuned for specific medical applications or integrated into larger medical decision support systems. ### Out-of-Scope Use The model is not suitable for non-medical applications or for tasks requiring expertise in languages other than Arabic. ## Bias, Risks, and Limitations The model may have biases originating from the training data, which is specific to Arabic medical content. It may not perform well on non-Arabic data or non-medical contexts. ### Recommendations Users (both direct and downstream) should be aware of the risks, biases, and limitations of the model. Proper validation in the specific use case is recommended before deployment. ## How to Get Started with the Model Use the code below to get started with the model. ```python from fastai.text.all import * # Load the model learn = load_learner('path_to_your_model.pkl') # Use the model for prediction text = "Your input text here" prediction = learn.predict(text) print(prediction)