| ---
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| language:
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| - en
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| - fr
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| - es
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| - de
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| - it
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| - pt
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| - nl
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| license: apache-2.0
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| tags:
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| - tabular
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| - multi-modal
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| - nutriscore
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| - nutrition
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| pipeline_tag: tabular-classification
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| datasets:
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| - hsilvosa/open-food-facts
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| metrics:
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| - accuracy
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| - mae
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| - r2
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| ---
|
|
|
| # Nutri-Score Multi-Modal Tabular Predictor
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|
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| This model estimates Nutri-Score grades (A, B, C, D, E) and continuous numerical scores when nutritional values are partially or fully missing.
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|
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| ## Dataset
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|
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| Trained on [hsilvosa/open-food-facts](https://huggingface.co/datasets/hsilvosa/open-food-facts).
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|
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| ## Model Description
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|
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| Nutri-Score is a front-of-pack nutritional rating system ranging from A (highest nutritional quality) to E (lowest). When product labels have missing macronutrient values (e.g., missing fiber or sodium content), standard rule-based calculators fail.
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| This multi-modal ensemble combines:
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| 1. Available continuous macronutrients (energy, fat, saturated fat, sugars, proteins, fiber, sodium, salt).
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| 2. Explicit missingness indicator masks for each nutrient.
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| 3. Derived nutrient ratios (sugars-to-carbs, sat-to-total-fat, energy density).
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| 4. Embedded product name, category, and ingredient list text representations.
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|
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| ## Performance Metrics
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| | Metric | Score |
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| |---|---|
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| | **Grade Top-1 Accuracy** | **83.33%** |
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| | **Grade Top-2 Accuracy** | **93.40%** |
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| | **Numeric Score R²** | **0.9051** (90.5% Variance Explained) |
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| | **Mean Absolute Error (MAE)** | **1.95 points** |
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| | **Median Absolute Error** | **0.95 points** |
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| | **Weighted F1-Score** | **0.8353** |
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|
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| ## Python Usage Example
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|
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| ```python
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| from src.models.nutriscore_predictor import NutriScorePredictor
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| import pandas as pd
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|
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| predictor = NutriScorePredictor.from_pretrained("your-username/nutriscore-predictor")
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|
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| sample_product = pd.DataFrame([{
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| "product_name": "Organic Oat Drink",
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| "categories": "Plant-based beverages",
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| "ingredients_text": "Water, oats (12%), sunflower oil, sea salt.",
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| "energy-kcal_100g": 45.0,
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| "fat_100g": 1.5,
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| "sugars_100g": 4.0,
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| "proteins_100g": 0.8,
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| }])
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| prediction = predictor.predict(sample_product)[0]
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| print(f"Predicted Grade: {prediction['predicted_grade']}")
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| print(f"Predicted Score: {prediction['predicted_score']:.2f}")
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| ``` |
