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GPT-Neo 125M finetuned with beer recipes

Model Description

GPT-Neo 125M is a transformer model based on EleutherAI's replication of the GPT-3 architecture https://huggingface.co/EleutherAI/gpt-neo-125M. It generates recipes for brewing beer in a YAML-like format which can be easily used for different purposes.

Training data

This model was trained on a custom dataset of ~ 76,800 beer recipes from the internet. It includes recipes for the following styles of beer:

  • Strong American Ale
  • Pale American Ale
  • India Pale Ale (IPA)
  • Standard American Beer
  • Stout
  • English Pale Ale
  • IPA
  • American Porter and Stout
  • Sour Ale
  • Irish Beer
  • Strong British Ale
  • Belgian and French Ale
  • German Wheat and Rye Beer
  • Czech Lager
  • Spice/Herb/Vegetable Beer
  • Specialty Beer
  • American Ale
  • Pilsner
  • Belgian Ale
  • Strong Belgian Ale
  • Bock
  • Brown British Beer
  • German Wheat Beer
  • Fruit Beer
  • Amber Malty European Lager
  • Pale Malty European Lager
  • British Bitter
  • Amber and Brown American Beer
  • Light Hybrid Beer
  • Pale Commonwealth Beer
  • American Wild Ale
  • European Amber Lager
  • Belgian Strong Ale
  • International Lager
  • Amber Bitter European Lager
  • Light Lager
  • Scottish and Irish Ale
  • European Sour Ale
  • Trappist Ale
  • Strong European Beer
  • Porter
  • Historical Beer
  • Pale Bitter European Beer
  • Amber Hybrid Beer
  • Smoke Flavored/Wood-Aged Beer
  • Spiced Beer
  • Dark European Lager
  • Alternative Fermentables Beer
  • Mead
  • Strong Ale
  • Dark British Beer
  • Scottish Ale
  • Smoked Beer
  • English Brown Ale
  • Dark Lager
  • Cider or Perry
  • Wood Beer

How to use

You can use this model directly with a pipeline for text generation. This example generates a different recipe each time it's run:

>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model='b3ck1/gpt-neo-125M-finetuned-beer-recipes')
>>> generator("style: Pilsner\nbatch_size: 20\nefficiency: 75\nboil_size:", do_sample=True, min_length=50, max_length=500)
>>> print(output[0]['generated_text'])

style: Pilsner
batch_size: 20
efficiency: 70
boil_size: 24
boil_time: 60
fermentables:
- name: Pale Ale
  type: Grain
  amount: 6.5
hops:
- name: Saaz
  alpha: 3.5
  use: Boil
  time: 60
  amount: 0.06
- name: Saaz
  alpha: 3.5
  use: Boil
  time: 30
  amount: 0.06
- name: Saaz
  alpha: 3.5
  use: Boil
  time: 10
  amount: 0.06
- name: Saaz
  alpha: 3.5
  use: Boil
  time: 0
  amount: 0.06
yeasts:
- name: Safale - American Ale Yeast US-05
  amount: 0.11
  min_temperature: 12
  max_temperature: 25
primary_temp: null
mash_steps:
- step_temp: 65
  step_time: 60
miscs: []

See this model in action

This model was used to build https://beerai.net.

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