Menu Attribute Extraction Dataset for Casual Dining Items

#attribute extraction #menu semantic understanding #structured output #supervised fine-tuning #restaurant menu digitization #menu item management #restaurant information retrieval #product catalog structuring

4320
0.4GB
JSON
CATL
MOBIUSI INC

Updated:2026-11-03

AI Analysis & Value Prop

This dataset uses casual dining menu item names and original descriptions as input, with structured attributes for item category, explicitly listed ingredients, cooking methods, portion information, and price information. It covers item details supported by the menu text and uses empty values when an attribute is not stated or cannot be reliably determined, making it suitable for training and evaluating menu understanding and structured information extraction.

Dataset Insights

Sample Examples

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Technical Specifications

Field	Type	Description
file_name	string	File name
price	object	Price information explicitly stated in the menu text; use null when no price is stated.
portion	object	Portion or serving information explicitly stated in the menu text; use null when none is stated.
category	object	A broad category for the item, supported by the menu name or description; use null when the text does not support a reliable category.
item_name	string	The original menu item name provided as input.
ingredients	array	Ingredients explicitly named in the item name or description. Do not infer unstated ingredients; use an empty array when none are listed.
cooking_methods	array	Cooking methods explicitly stated in the item name or description. Do not infer unstated methods; use an empty array when none are stated.
item_description	string	The original menu item description provided as input. Preserve the source wording.

Compliance Statement

Authorization Type	Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use	Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization	No PII, no real company names, simulated scenarios follow industry standards
Compliance System	Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What menu information does the dataset contain?: Each example is based on a menu item name and its original description. Labels cover the item category, ingredients and cooking methods explicitly mentioned in the text, and any portion or price information stated on the menu.

Are unstated ingredients or cooking methods inferred?: No. Annotations are grounded in the menu text and do not infer unstated details. Categories use a null value when they cannot be determined reliably; unlisted ingredients or cooking methods use empty arrays, and unstated portion or price information uses a null value.

What are the dataset size and quality specifications?: The information currently provided does not specify the sample count or quantitative quality metrics. The field definitions describe how input text maps to attribute labels and emphasize preserving the original description and extracting only text-supported details. Refer to the product page or delivery documentation for specifications.

How can I obtain the dataset, and what are the licensing terms?: Availability and licensing terms are determined by the product page and the applicable delivery agreement. Before use, confirm the permitted training or evaluation scenarios and any requirements for data storage, redistribution, or commercial use.

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Cite this Work

@dataset{Mobiusi2026,
  title={Menu Attribute Extraction Dataset for Casual Dining Items},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/644212e4ee79f317c738f57190192a49},
  urldate={2026-10-22},
  keywords={menu attribute extraction,restaurant menu dataset,menu item information,ingredient identification,cooking method extraction,item category classification,structured output training,casual dining data},
  version={1.0}
}

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