| --- |
| tags: |
| - ml-intern |
| --- |
| # Item Weight Predictor |
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| A machine learning model that predicts the weight (in grams) of items based on their text description. Designed for food delivery platforms to estimate weights for: |
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| - **Menu items** (restaurant food portions) |
| - **Grocery items** (packaged food from supermarkets) |
| - **Non-food items** (household goods, electronics, toys, etc.) |
|
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| ## Model Architecture |
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| The model uses **per-item-type Ridge regression** on TF-IDF features of text descriptions: |
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| - Separate models trained for each item category (`menu_item`, `grocery`, `non_food`) |
| - Log-transformed target to handle wide weight range (grams to kilograms) |
| - TF-IDF with 1-3 gram features for robust text representation |
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| ## Performance |
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| | Item Type | MAE | MAPE | Training Samples | Validation Samples | |
| |------------|--------|--------|------------------|--------------------| |
| | Grocery | 23.1g | 4.4% | 17,246 | 3,044 | |
| | Menu Item | 63.1g | 136.9% | 6,786 | 1,198 | |
| | Non-Food | 479.9g | 113.6% | 7,152 | 1,263 | |
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| Grocery items perform best because most are standardized to ~100g servings. Menu items and non-food have higher variance but still useful for rough estimates. |
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| ## Usage |
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| ```python |
| import joblib |
| from huggingface_hub import hf_hub_download |
| |
| # Download and load model |
| model_path = hf_hub_download(repo_id="ZZandro/weight-predictor", filename="unified_predictor.pkl") |
| predictor = joblib.load(model_path) |
| |
| # Predict weight |
| text = "[MENU_ITEM] grilled chicken breast | food | meal ingredient | portion" |
| weight_g = predictor.predict(text, item_type="menu_item") |
| print(f"Predicted weight: {weight_g:.1f}g") |
| ``` |
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| ### Supported Item Types |
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| - `menu_item` - Restaurant menu items (e.g., "cheeseburger", "caesar salad") |
| - `grocery` - Packaged grocery products (e.g., "chocolate bar", "cereal box") |
| - `non_food` - General retail items (e.g., "laptop", "t-shirt", "toy car") |
|
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| ### Text Format |
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| Include the item type tag at the start: |
| ``` |
| [MENU_ITEM] <item description> |
| [GROCERY] <item description> |
| [NON_FOOD] <item description> |
| ``` |
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| ## Data Sources |
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| The training data combines: |
| - Amazon product data with shipping weights (non-food items) |
| - NutriBench meal descriptions with per-ingredient gram weights (menu items) |
| - USDA Foundation Food data with standard serving sizes (grocery items) |
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| ## Dataset |
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| The processed training dataset is available at: [ZZandro/item-weight-dataset](https://huggingface.co/datasets/ZZandro/item-weight-dataset) |
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| ## License |
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| Apache-2.0 |
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| <!-- ml-intern-provenance --> |
| ## Generated by ML Intern |
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| This dataset repository was generated by [ML Intern](https://github.com/huggingface/ml-intern), an agent for machine learning research and development on the Hugging Face Hub. |
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| - Try ML Intern: https://smolagents-ml-intern.hf.space |
| - Source code: https://github.com/huggingface/ml-intern |
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