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, specifically for analyzing and supporting decision-making processes in Arabic medical texts. The model is fine-tuned using the HuggingFaceFW/fineweb dataset, leveraging the fastai library for its implementation.

• Developed by: BrainSAIT Team
• Funded by [optional]: [More Information Needed]
• Shared by [optional]: BrainSAIT Organization
• Model type: Reinforcement Learning
• Language(s) (NLP): Arabic
• License: MIT
• Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

• Repository: BrainSAIT GitHub Repository
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

Direct Use

The BrainSAIT model can be used directly for tasks such as analyzing medical records, providing diagnostic suggestions, and supporting medical research in Arabic.

Downstream Use [optional]

The model can be further fine-tuned for specific medical specialties or integrated into larger healthcare decision support systems.

Out-of-Scope Use

The model is not suitable for non-medical applications, real-time emergency responses, or tasks requiring expertise in languages other than Arabic.

Bias, Risks, and Limitations

The model may exhibit biases present in the training data, including cultural or regional biases specific to Arabic medical texts. It might not generalize well to non-Arabic contexts or diverse medical conditions.

Recommendations

Users should validate the model's outputs in their specific use case, particularly for critical medical decisions. Continuous monitoring and periodic retraining with updated data are recommended to mitigate biases and improve performance.

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)