chef_gpt2_recipe_generator

Overview

chef_gpt2_recipe_generator is a Causal Language Model designed to generate cooking recipes based on a provided list of ingredients. It is fine-tuned on a large corpus of structured recipes, learning the relationship between ingredients, quantities, and instructions.

Model Architecture

This model is based on the gpt2-medium architecture (355M parameters).

• Base Model: GPT-2 Medium.
• Training Objective: Causal Language Modeling (CLP) / Next-token prediction.
• Data Format: The model was trained on data structured as: INGREDIENTS: [list of items] \n INSTRUCTIONS: [step-by-step guide] <|endoftext|>.

Intended Use

• Culinary Inspiration: Generating creative ideas for leftover ingredients in a fridge.
• Creative Writing: Assisting in generating content for food blogs or culinary fiction.
• Data Augmentation: Creating synthetic recipe datasets for downstream culinary NLP tasks.

How to use

The model works best when prompted with the specific format it was trained on. You should provide a list of ingredients following the INGREDIENTS: tag.

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_name = "your_username/chef_gpt2_recipe_generator"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

chef = pipeline('text-generation', model=model, tokenizer=tokenizer, device=-1)

# Define ingredients
prompt = "INGREDIENTS: Chicken breast, garlic, soy sauce, honey, broccoli. \n INSTRUCTIONS:"

output = chef(
    prompt,
    max_length=400,
    num_return_sequences=1,
    do_sample=True,
    top_k=50,
    top_p=0.92,
    temperature=0.8,
    pad_token_id=tokenizer.eos_token_id
)

print(output[0]['generated_text'])