ZZandro/item-weight-dataset

Item Weight Predictor

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:

• Menu items (restaurant food portions)
• Grocery items (packaged food from supermarkets)
• Non-food items (household goods, electronics, toys, etc.)

Model Architecture

The model uses per-item-type Ridge regression on TF-IDF features of text descriptions:

• 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

Performance

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

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.

Usage

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")

Supported Item Types

• 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")

Text Format

Include the item type tag at the start:

[MENU_ITEM] <item description>
[GROCERY] <item description>
[NON_FOOD] <item description>

Data Sources

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)

Dataset

The processed training dataset is available at: ZZandro/item-weight-dataset

License

Apache-2.0

Generated by ML Intern

This dataset repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

• Try ML Intern: https://smolagents-ml-intern.hf.space
• Source code: https://github.com/huggingface/ml-intern