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.
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.
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)