---
language: en
tags:
- health
- biobert
- medical
- multi-task
license: mit
---

# 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

```python
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.