metadata
language:
- en
tags:
- text generation
- pytorch
- causal-lm
license: apache-2.0
datasets:
- custom
widget:
- text: |-
style: Pilsner
batch_size: 20
efficiency: 75
boil_size:
example_title: Pilsener
- text: |-
style: IPA
batch_size: 20
efficiency: 75
boil_size:
example_title: IPA
- text: |-
style: Scottish Ale
batch_size: 20
efficiency: 75
boil_size:
example_title: Scottish Ale
inference:
parameters:
do_sample: true
top_k: 10
top_p: 0.99
max_length: 500
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.