metadata
language:
- en
license: mit
pipeline_tag: text-generation
model-index:
- name: chef-gpt-en
results: []
widget:
- text: ingredients>> salmon, lemon; recipe>>
chef-gpt-en
Test the model HERE.
Fine-tuned GPT-2 for recipe generation. This is the dataset that it's trained on.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
MODEL_ID = "auhide/chef-gpt-en"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
chef_gpt = AutoModelForCausalLM.from_pretrained(MODEL_ID)
ingredients = ", ".join([
"spaghetti",
"tomatoes",
"basel",
"salt",
"chicken",
])
prompt = f"ingredients>> {ingredients}; recipe>>"
tokens = chef_gpt.tokenizer(prompt, return_tensors="pt")
recipe = chef_gpt.generate(**tokens, max_length=124)
print(recipe)
Here is a sample result of the prompt:
ingredients>> spaghetti, tomatoes, basel, salt, chicken; recipe>>cook spaghetti according to package directions
meanwhile, place tomato slices and basel in a large pot with salted water and bring to a boil
reduce heat and