muhammad1707/distilbert-food-category

DistilBERT Food Category Classifier (7 labels)

Model Summary

This model is a fine-tuned DistilBERT classifier that predicts a single food category for a given product/ingredient name.

Labels (7):

• vegetable
• fruit
• dairy
• meat
• beverage
• snack
• grain

Typical output:

• a category label (optionally with a confidence score)

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Eval Results

Synthetic split (train/validation/test from the same generated distribution)

• Accuracy: ~0.99
• Macro F1: ~0.988

Manual unseen set (n=20)

• Accuracy: 0.70

Note: The manual unseen set is small and intended as a quick sanity check for generalization to unseen product names.

Dataset

This model was fine-tuned on a custom product classification dataset.

The dataset consists of product/ingredient names mapped to 7 coarse food categories:

• vegetable
• fruit
• dairy
• meat
• beverage
• snack
• grain

The data includes a mix of manually labeled and generated samples. All entries are short product titles or ingredient names in English.

Note: The dataset is domain-specific (food products only) and does not cover non-food categories. Model performance depends on vocabulary coverage and label consistency.

Intended Use

• Food/product categorization for search, filtering, or analytics
• Categorizing short product titles, ingredient names, and simple food phrases
• A lightweight baseline model for food taxonomy experiments

Not Intended For

• General “anything in the world” categorization (electronics, cosmetics, etc.)
• Medical, nutrition, allergen, or dietary advice
• High-stakes decisions

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How to Use

Install

pip install transformers torch