Data Normalization

Data processing and normalization tasks evaluate structured output from messy prose and different structures. This capability is essential for catalogue and product pipelines where data needs to be extracted from unstructured text and formatted consistently. Models must understand the desired output format and extract relevant information accurately.

Input Tokens

Output Tokens

Duration

Cost

Context

Input

Label the recipe with dietary and allergen information. Use the following definitions:
- gluten_intolerant: True if Dish doesn't contain bread, wheat-based sauces, or anything with gluten
- Vegetarian: Must not contain meat or fish
- Vegan: Must not contain meat, fish, dairy, or eggs
- Halal: Must not contain pork and all meat must be acceptable under halal preparation (assume bacon is not halal)
- Dairy: Contains any dairy products (e.g., cheese, butter, milk, cream)

Expected output

{
  "gluten_intolerant": false,
  "vegetarian": false,
  "vegan": false,
  "halal": false,
  "dairy": true
}

Model output

{
  "gluten_intolerant": false,
  "vegetarian": false,
  "vegan": false,
  "halal": false,
  "dairy": true
}