Symptom To Diagnosis

🚀 Symptom_to_Diagnosis

This model is a fine - tuned version of bert - base - cased for text classification tasks in diagnosing diseases from symptoms.

🚀 Quick Start

You can use a pipeline as a high - level helper to utilize this model:

Basic Usage

from transformers import pipeline

pipe = pipeline("text - classification", model="Zabihin/Symptom_to_Diagnosis")

# Example
result = pipe("I've been having headaches and migraines, and I can't sleep. My whole body shakes and twitches. Sometimes I feel lightheaded.")
print(result)
# Output: [{'label': 'drug reaction', 'score': 0.9489321112632751}]

Advanced Usage

from transformers import pipeline

# Load the model
classifier = pipeline("text - classification", model="Zabihin/Symptom_to_Diagnosis", tokenizer="Zabihin/Symptom_to_Diagnosis")

# Example input text
input_text = "I've been having headaches and migraines, and I can't sleep. My whole body shakes and twitches. Sometimes I feel lightheaded."

# Get the predicted label
result = classifier(input_text)

# Print the predicted label
predicted_label = result[0]['label']
print("Predicted Label:", predicted_label)
# Output: Predicted Label: drug reaction

✨ Features

• This model is a fine - tuned version of [bert - base - cased](https://huggingface.co/bert - base - cased) on the dataset (https://huggingface.co/datasets/gretelai/symptom_to_diagnosis).
• It is specifically designed for text classification tasks related to diagnosing diseases from symptoms.
• The model aims to analyze natural language descriptions of symptoms and predict one of 22 corresponding diagnoses.

📦 Installation

The README doesn't provide installation steps, so this section is skipped.

📚 Documentation

Model Description

This model is a fine - tuned version of the bert - base - cased architecture, specifically designed for text classification tasks related to diagnosing diseases from symptoms. The primary objective is to analyze natural language descriptions of symptoms and predict one of 22 corresponding diagnoses.

Dataset Information

The model was trained on the Gretel/symptom_to_diagnosis dataset, which consists of 1,065 symptom descriptions in the English language, each labeled with one of the 22 possible diagnoses. The dataset focuses on fine - grained single - domain diagnosis, making it suitable for tasks that require detailed classification based on symptom descriptions.

Example:

{
  "output_text": "drug reaction",
  "input_text": "I've been having headaches and migraines, and I can't sleep. My whole body shakes and twitches. Sometimes I feel lightheaded."
}

Framework Versions

• Transformers 4.35.2
• TensorFlow 2.15.0
• Datasets 2.15.0
• Tokenizers 0.15.0

🔧 Technical Details

The README doesn't provide specific technical details over 50 words, so this section is skipped.

📄 License

The model is licensed under the Apache 2.0 license.

📊 Model Information

📈 Model Results

Symptom_to_Diagnosis