Update README.md

README.md

@@ -1,3 +1,61 @@
----
-license: mit
----
+# Model Card for Disease Symptom Recognition Model
+
+## Model Details
+
+### Model Description
+
+This model is a fine-tuned BERT-based architecture designed to recognize and classify symptoms of diseases. It has been trained on a curated dataset containing labeled descriptions of various disease symptoms and converted to ONNX for efficient inference.
+
+- **Developed by:** Mihi
+- **Funded by:** Self-funded
+- **Shared by:** Mihi
+- **Model type:** NLP Classification
+- **Language(s):** English
+- **License:** MIT
+- **Finetuned from model:** BERT base uncased
+
+### Model Sources
+
+- **Repository:** [GitHub Repository Link] (replace with actual link)
+- **Demo:** [Demo Link] (replace with actual link)
+
+---
+
+## Uses
+
+### Direct Use
+
+This model can be used directly for symptom classification in applications like:
+
+- Symptom checkers for healthcare applications
+- Medical chatbots for triage
+- Data analysis in public health studies
+
+### Downstream Use
+
+The model may be fine-tuned further or integrated into larger healthcare solutions involving disease diagnosis or prediction.
+
+### Out-of-Scope Use
+
+- The model is not designed for diagnosing diseases.
+- It should not be used as a substitute for professional medical advice.
+
+---
+
+## Bias, Risks, and Limitations
+
+- The model's performance is limited to the scope and quality of the training data. It may not perform well on symptoms outside its training domain.
+- Potential biases in the training data can lead to inaccurate predictions for underrepresented diseases or symptoms.
+
+### Recommendations
+
+- Ensure proper pre-screening of the output by medical professionals before clinical application.
+- Perform further fine-tuning or retraining if applied in domains outside the original dataset.
+
+---
+
+## How to Get Started with the Model
+
+Install the required dependencies:
+```bash
+pip install transformers onnxruntime