Update README.md

README.md

@@ -1,3 +1,75 @@
+---
+license: other
+language:
+  - en
+task_categories:
+  - text-classification
+size_categories:
+  - 10K<n<100K
+pretty_name: Restaurant Menu Ingredient & EU Allergen Dataset
+tags:
+  - food
+  - food-allergens
+  - allergens
+  - allergen-detection
+  - allergen-classification
+  - eu-allergens
+  - eu-14-allergens
+  - food-allergy
+  - food-safety
+  - restaurant
+  - restaurant-menu
+  - restaurant-data
+  - menu
+  - menu-analysis
+  - menu-intelligence
+  - recipes
+  - recipe-dataset
+  - recipenlg
+  - ingredients
+  - ingredient-extraction
+  - ingredient-normalization
+  - ingredient-understanding
+  - food-ingredients
+  - culinary
+  - culinary-nlp
+  - food-nlp
+  - nlp
+  - text-classification
+  - multi-label
+  - multi-label-classification
+  - machine-learning
+  - artificial-intelligence
+  - llm
+  - gemini
+  - gemini-3.5-flash
+  - data-cleaning
+  - data-curation
+  - data-validation
+  - rule-based
+  - regex
+  - gluten
+  - milk
+  - eggs
+  - soy
+  - peanuts
+  - tree-nuts
+  - sesame
+  - mustard
+  - celery
+  - sulphites
+  - fish
+  - crustaceans
+  - molluscs
+  - lupin
+  - vegan
+  - vegetarian
+  - seafood
+  - food-tech
+  - restaurant-tech
+  - menu-ai
+---
+
 # Restaurant Menu Ingredient & EU Allergen Dataset
 
 This dataset was created to support the development of **machine learning models and restaurant software systems for detecting the 14 EU food allergens from recipe ingredients**.
@@ -6,15 +78,15 @@ This dataset was created to support the development of **machine learning models
 
 The dataset is primarily derived from **RecipeNLG**, a large-scale collection containing approximately **2.3 million recipes**.
 
-A restaurant-oriented subset was selected from this collection. The selection was designed to represent a wide variety of menu items, including main dishes, soups, salads, appetizers, mezze, desserts, bakery products, breads, pasta and noodles, meat and poultry dishes, fish and seafood, vegetarian and vegan dishes, breakfast foods, sauces, snacks, and beverages.
+A restaurant-oriented subset was selected to represent a wide range of menu items, including main dishes, soups, salads, appetizers, mezze, desserts, bakery products, breads, pasta and noodles, meat and poultry dishes, fish and seafood, vegetarian and vegan dishes, breakfast foods, sauces, snacks, and beverages.
 
-Duplicate or highly similar recipes were reduced where appropriate. Menu item names that were unsuitable for general restaurant use were also reviewed, cleaned, renamed, or removed.
+Duplicate or highly similar recipes were reduced where appropriate. Menu item names unsuitable for general restaurant use were also reviewed, cleaned, renamed, or removed.
 
 ## Data Preparation
 
 The original RecipeNLG ingredient descriptions often contain quantities, measurement units, package sizes, and preparation instructions.
 
-A Gemini-based ingredient extraction pipeline was used to convert these raw ingredient descriptions into cleaner ingredient identities.
+A Gemini-based ingredient extraction pipeline was used to convert raw ingredient descriptions into cleaner ingredient identities.
 
 For example:
 
@@ -24,7 +96,7 @@ becomes:
 
 `cheddar cheese`
 
-The extraction process was designed to preserve the actual ingredient identity while separating:
+The extraction process preserves the actual ingredient identity while separating:
 
 - Main ingredients
 - Optional ingredients
@@ -32,18 +104,9 @@ The extraction process was designed to preserve the actual ingredient identity w
 
 After ingredient cleaning, an initial allergen detection stage was performed using a deterministic **NLP / regex-based rule system**.
 
-The rule system considered both direct allergen ingredients and established compound foods such as:
-
-- Soy sauce
-- Mayonnaise
-- Hummus
-- Tahini
-- Pesto
-- Worcestershire sauce
-- Oyster sauce
-- Fish sauce
+The system considered direct allergen ingredients as well as established compound foods such as soy sauce, mayonnaise, hummus, tahini, pesto, Worcestershire sauce, oyster sauce, and fish sauce.
 
-False-positive cases were also handled, such as coconut milk, oyster mushrooms, eggplant, nutmeg, and other potentially misleading ingredient names.
+False-positive cases such as coconut milk, oyster mushrooms, eggplant, and nutmeg were also considered.
 
 ## Final Allergen Audit