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--- |
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license: mit |
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widget: |
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- text: I am having itching, skin rash, and nodal skin eruptions |
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example_title: Fungal infection example |
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- text: I feel like vomiting, breathlessness, and sweating |
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example_title: Heart Attack example |
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- text: I am feeling fatigue, weight loss, restlessness and also lethargy. |
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example_title: Diabetes example |
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--- |
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# Disease Prognosis and Precautions Text2Text Generation |
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Welcome to the Disease Prognosis and Precautions Text2Text Generation repository! Fine-tuned microsoft/GODEL-v1_1-large-seq2seq. The model is designed to generate responses for disease prognosis and recommended precautions based on given symptoms. |
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## Model Overview |
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The model in this repository is a text-to-text generation model. It takes a prompt in the form of symptoms related to a particular disease and generates a response that includes the potential disease prognosis along with recommended precautions. The columns used in the training dataset are: |
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- **Disease:** The name of the disease related to the symptoms. |
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- **Symptoms:** The list of symptoms provided in the prompt. |
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- **Precautions:** The recommended precautions for the identified disease. |
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## Examples |
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Here are some examples of how you can use the model: |
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### Example 1 |
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**Prompt:** "I am feeling continuous sneezing, shivering and chills" |
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**Response:** "Seems like allergy. You should try to avoid dust and air pollution." |
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### Example 2 |
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**Prompt:** "I am feeling itching, skin rash and patches" |
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**Response:** "Seems like fungal infection. You should bathe twice a day and use antifungal soap." |
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## How to Use |
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To use the model for generating disease prognosis and precautions based on symptoms, you can use the `generate` function provided by the Hugging Face Transformers library. Here's a basic example using Python: |
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```python |
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer |
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# Load the model and tokenizer |
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model_name = "shanover/medbot_godel_v3" |
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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# Define your symptom prompt |
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prompt = "I am feeling continuous sneezing, shivering and chills" |
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def generate_response(input_text, model, tokenizer, max_length): |
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input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=max_length, truncation=True) |
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input_ids = input_ids.to(device) |
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with torch.no_grad(): |
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output_ids = model.generate(input_ids) |
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generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True) |
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return generated_text |
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print(generate_response(prompt, model, tokenizer)) |
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``` |
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Remember to replace `"shanover/medbot_godel_v3"` with the actual name or path of the model you've downloaded or fine-tuned. |
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## Acknowledgments |
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Trained on Microsoft/Godel: https://huggingface.co/microsoft/GODEL-v1_1-large-seq2seq |
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## Issues and Contributions |
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If you encounter any issues while using the model or have suggestions for improvements, please feel free to open an issue in this repository. Contributions are also welcome! |
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## Disclaimer |
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Please note that the information generated by the model is for informational purposes only and should not be considered a substitute for professional medical advice. Always consult a medical professional for accurate diagnoses and treatments. |
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Thank you for using the Disease Prognosis and Precautions Text2Text Generation model! We hope it proves to be a helpful tool. |
