t5-recipe-generation
Maintainer: flax-community
52
🛠️
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The t5-recipe-generation model, developed by the Flax/Jax Community, is a Text-to-Text AI model trained to generate cooking recipes. This model is part of the Flax/Jax Community Week, organized by HuggingFace and sponsored by Google. The model was trained on the RecipeNLG: A Cooking Recipes Dataset, which contains over 2 million cooking recipes.
Model inputs and outputs
Inputs
- Ingredients: A list of ingredients to be used in the recipe
- Directions: Step-by-step instructions for preparing the dish
Outputs
- Recipe: A generated recipe text, including a title, ingredient list, and step-by-step instructions
Capabilities
The t5-recipe-generation model can be used to generate complete cooking recipes based on a set of ingredients and instructions. This can be useful for recipe recommendation systems, meal planning tools, or cooking assistants. The model is able to generate coherent and plausible recipes, drawing upon the knowledge it has learned from the training dataset.
What can I use it for?
The t5-recipe-generation model could be integrated into various applications, such as:
- Recipe Recommendation Systems: The model could be used to generate recipe suggestions based on a user's preferences or the ingredients they have on hand.
- Meal Planner Apps: The model could be used to create weekly meal plans and generate the corresponding recipes.
- Cooking Assistants: The model could be used to help users create recipes by providing suggestions and guidance based on the inputs provided.
Things to try
Some interesting things to try with the t5-recipe-generation model include:
- Exploring different input combinations: Try providing the model with different combinations of ingredients and instructions to see how it adapts the generated recipe.
- Generating recipes for specific dietary needs: Experiment with providing the model with dietary restrictions or preferences, such as vegetarian, gluten-free, or low-carb, and observe how the generated recipes accommodate those requirements.
- Combining the model with other AI tools: Explore ways to integrate the
t5-recipe-generationmodel with other AI-powered tools, such as image generation or voice interfaces, to create more comprehensive cooking assistants.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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