Description:
Given a list of ingredients, generate a recipe – similar to what the GPT3 API offers: openAI recipe generator 9. A model exists on the hub that does Recipe NLG 7, but it uses a GPT2 architecture. I’m curious if T5 or Bart will produce better results.
Model:
T5 or Bart.
Dataset:
Recipe NLG (2,231,142 recipe examples) - hugging face link 5 - download site 2
Data example:

{‘NER’: ‘[“oyster crackers”, “salad dressing”, “lemon pepper”, “dill weed”, “garlic powder”, “salad oil”]’,
‘Unnamed: 0’: 1000,
‘directions’: ‘[“Combine salad dressing mix and oil.”, “Add dill weed, garlic powder and lemon pepper.”, “Pour over crackers; stir to coat.”, “Place in warm oven.”, “Use very low temperature for 15 to 20 minutes.”]’,
‘ingredients’: ‘[“12 to 16 oz. plain oyster crackers”, “1 pkg. Hidden Valley Ranch salad dressing mix”, “1/4 tsp. lemon pepper”, “1/2 to 1 tsp. dill weed”, “1/4 tsp. garlic powder”, “3/4 to 1 c. salad oil”]’,
‘link’: ‘www.cookbooks.com/Recipe-Details.aspx?id=648947’,
‘source’: ‘Gathered’,
‘title’: ‘Hidden Valley Ranch Oyster Crackers’}

Script:
Maybe run_summarization_flax.py 2 and just treat it as seq2seq?
Expected result:
Give it a list of ingredients (e.g. sugar, flour, egg, peanut butter) and it spits out a recipe (peanut butter cookies). Useful for when you are trying to use up all those miscellaneous ingredients in your pantry and fridge!
Unfortunately I don’t think I will have the time to be able to work on this, but I thought it might be an interesting idea that someone else could run with.
Optional 1: Have it create a title for the recipe.
Optional 2: Input the amounts of each ingredient so that it has some constraints.