b3ck1/gpt-neo-125M-finetuned-beer-recipes

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.