|
|
--- |
|
|
language: |
|
|
- en |
|
|
tags: |
|
|
- text-generation |
|
|
- gpt2 |
|
|
- culinary |
|
|
- recipe-generation |
|
|
license: mit |
|
|
--- |
|
|
|
|
|
# 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. |
|
|
|
|
|
```python |
|
|
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']) |
