RecipeGPT-exp
metric-cooking
| RecipeGPT-exp | metric-cooking | |
|---|---|---|
| 1 | 1 | |
| 20 | 29 | |
| - | - | |
| 1.4 | 0.0 | |
| about 1 year ago | almost 2 years ago | |
| Jupyter Notebook | JavaScript | |
| - | GNU General Public License v3.0 only |
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RecipeGPT-exp
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Show HN: Ingredients for Change
Great question. Models for generating plausible recipes have existed in some form for about a decade now.
Arguably the first "successful" attempt at this was Chef Watson, which blew my mind when it was first released in 2014 despite it's well-documented tendency to suggest all kinds of spectacularly odd combinations of flavors and ingredients, like garlic ice cream and mayonnaise-spiked Bloody Marys[1].
It's worth noting that preprocessing the textual inputs isn't entirely necessary to produce somewhat reasonable, ML-generated recipes. For example GPT-3 is capable of generating fairly interesting zero-shot recipes, despite having been trained on raw text data without any preliminary feature selection to label (e.g.) a recipe's ingredients.[2] Still not exempt from the occasional wacky, whimsical suggestion[3], but I, for one, wouldn't want my ML-generated recipes any other way.
1. https://www.google.com/amp/s/www.newyorker.com/magazine/2016...
2. https://github.com/LARC-CMU-SMU/RecipeGPT-exp
3. https://thenextweb.com/news/ai-generated-recipes-three-cours...
metric-cooking
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Show HN: Ingredients for Change
https://github.com/falk-hueffner/metric-cooking/blob/master/...
The use case is a bit different (the task includes finding ingredients mentioned in a longer text, and it'll rather not parse something rather than parsing it wrong), but it works fairly OK and can even parse things like "3/4 cup plus 2 tablespoons packed light-brown sugar".
What are some alternatives?
gpt2bot - Your new Telegram buddy powered by transformers
ingredient-phrase-tagger - Extract structured data from ingredient phrases using conditional random fields
PyIng - Python module to parse ingredient names. Splitting them into the ingredient, unit and quantity. It is trained on a publicly available dataset using Tensorflow.
NXEnhanced - Adds "quality-of-life" features to NextDNS website for a more practical usability
prompt-extend - extending stable diffusion prompts with suitable style cues using text generation
markdown-viewer - Markdown Viewer / Browser Extension
Transformers-Tutorials - This repository contains demos I made with the Transformers library by HuggingFace.
zestful-client