Health Analysis BioBERT Model

This model is fine-tuned on BioBERT for multi-task health analysis, predicting: BMI, Intestinal health indicators, Comparison with optimal values

Model Details

Model Type: Fine-tuned BioBERT (dmis-lab/biobert-v1.1)
Tasks: Multi-task classification and regression for health indicators
Training Data: Custom health dataset with advanced health metrics

Input Features

The model accepts the following health-related inputs:

Demographics: Height, Weight, BMI
Medical history: Conditions, medications, previous issues
Diet information: Consumption of various food groups
Lifestyle factors: Physical activity, sleep, stress
Supplement usage: Probiotics, vitamins, minerals

Output Predictions

The model predicts:

BMI: Body Mass Index calculation
Intestinal health indicators: Assessment of gut health
Comparison with optimal values: How the individual's metrics compare to ideal ranges

Usage

from transformers import AutoTokenizer, AutoModel
import torch
import json

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("Fahim18/health-analysis-biobert")
model = AutoModel.from_pretrained("Fahim18/health-analysis-biobert")

# Load preprocessing configs
with open("preprocessor_config.json", "r") as f:
    preprocessor_info = json.load(f)

# Example inference function
def predict(text_input):
    # Tokenize
    inputs = tokenizer(text_input, return_tensors="pt", padding=True, truncation=True, max_length=512)
    
    # Predict
    with torch.no_grad():
        outputs = model(**inputs)
    
    # Process outputs
    # Note: You'll need to implement task-specific output processing
    
    return outputs

Limitations

This model should be used for research purposes only and not for making actual medical decisions. Always consult healthcare professionals for medical advice.