Upload Nutri-Score predictor model

README.md

@@ -0,0 +1,75 @@
+---
+language:
+- en
+- fr
+- es
+- de
+- it
+- pt
+- nl
+license: apache-2.0
+tags:
+- tabular
+- multi-modal
+- nutriscore
+- nutrition
+pipeline_tag: tabular-classification
+datasets:
+- hsilvosa/open-food-facts
+metrics:
+- accuracy
+- mae
+- r2
+---
+
+# Nutri-Score Multi-Modal Tabular Predictor
+
+This model estimates Nutri-Score grades (A, B, C, D, E) and continuous numerical scores when nutritional values are partially or fully missing.
+
+## Dataset
+
+Trained on [hsilvosa/open-food-facts](https://huggingface.co/datasets/hsilvosa/open-food-facts).
+
+## Model Description
+
+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.
+
+This multi-modal ensemble combines:
+1. Available continuous macronutrients (energy, fat, saturated fat, sugars, proteins, fiber, sodium, salt).
+2. Explicit missingness indicator masks for each nutrient.
+3. Derived nutrient ratios (sugars-to-carbs, sat-to-total-fat, energy density).
+4. Embedded product name, category, and ingredient list text representations.
+
+## Performance Metrics
+
+| Metric | Score |
+|---|---|
+| **Grade Top-1 Accuracy** | **83.33%** |
+| **Grade Top-2 Accuracy** | **93.40%** |
+| **Numeric Score R²** | **0.9051** (90.5% Variance Explained) |
+| **Mean Absolute Error (MAE)** | **1.95 points** |
+| **Median Absolute Error** | **0.95 points** |
+| **Weighted F1-Score** | **0.8353** |
+
+## Python Usage Example
+
+```python
+from src.models.nutriscore_predictor import NutriScorePredictor
+import pandas as pd
+
+predictor = NutriScorePredictor.from_pretrained("your-username/nutriscore-predictor")
+
+sample_product = pd.DataFrame([{
+    "product_name": "Organic Oat Drink",
+    "categories": "Plant-based beverages",
+    "ingredients_text": "Water, oats (12%), sunflower oil, sea salt.",
+    "energy-kcal_100g": 45.0,
+    "fat_100g": 1.5,
+    "sugars_100g": 4.0,
+    "proteins_100g": 0.8,
+}])
+
+prediction = predictor.predict(sample_product)[0]
+print(f"Predicted Grade: {prediction['predicted_grade']}")
+print(f"Predicted Score: {prediction['predicted_score']:.2f}")
+```

clf_model.joblib

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config.json

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+{
+  "text_features": 64,
+  "nutrient_means": {
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+    "fat_100g": 30.74013926932252,
+    "saturated-fat_100g": 10.32354276398921,
+    "sugars_100g": 34.56173870053036,
+    "carbohydrates_100g": 68.65251908164707,
+    "proteins_100g": 13.128354332154984,
+    "fiber_100g": 5.057634242792158,
+    "salt_100g": 1.2142082413442563,
+    "sodium_100g": 0.48557621082556474,
+    "sugars_carbs_ratio": 0.5028519837786664,
+    "sat_total_fat_ratio": 0.3146704924893529,
+    "additive_count": 1.7563809523809524
+  },
+  "vocab": {
+    "enriched": 18,
+    "iron": 27,
+    "niacin": 35,
+    "mononitrate": 32,
+    "folic": 22,
+    "acid": 0,
+    "wheat": 61,
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+    "riboflavin": 45,
+    "salt": 46,
+    "sugar": 54,
+    "soy": 49,
+    "natural": 33,
+    "flavors": 20,
+    "onion": 37,
+    "powder": 43,
+    "garlic": 25,
+    "folic acid": 23,
+    "wheat flour": 62,
+    "cream": 17,
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+    "milk": 30,
+    "chocolate": 8,
+    "oil": 36,
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+    "corn": 15,
+    "syrup": 55,
+    "contains": 14,
+    "artificial": 1,
+    "gum": 26,
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+    "soy lecithin": 50,
+    "corn syrup": 16,
+    "water": 60,
+    "vegetable": 57,
+    "soybean": 51,
+    "yellow": 63,
+    "sodium": 48,
+    "cheese": 7,
+    "palm": 39,
+    "modified": 31,
+    "starch": 53,
+    "red": 44,
+    "calcium": 6,
+    "citric": 9,
+    "flavor": 19,
+    "citric acid": 10,
+    "undefined": 56,
+    "organic": 38,
+    "vinegar": 58,
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+    "soybean oil": 52,
+    "plant": 40,
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+    "beverages": 4,
+    "plant based": 41,
+    "based foods": 3,
+    "juice": 28,
+    "snacks": 47,
+    "butter": 5,
+    "potassium": 42,
+    "vitamin": 59,
+    "natural flavor": 34
+  },
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+}

pytorch_model.bin

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reg_model.joblib

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