README.md

---
dataset_info:
  features:
  - name: recipes
    dtype: string
  - name: names
    dtype: string
  splits:
  - name: train
    num_bytes: 11479327
    num_examples: 20000
  download_size: 5911822
  dataset_size: 11479327
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

This is a simple recipes dataset, obtained by formatting/cleaning [this one](formido/recipes-20k), that I think it was just made by scrapping the food.com website. 
Here's the cleanup script I used to obtain it.

```python
from datasets import load_dataset

def clean_recipe(recipe):
    recipe = recipe.replace(" , ", ", ")
    recipe = recipe.replace('"', "'")
    recipe = recipe.replace("\\'", "'")
    recipe = recipe.strip("\\']")
    recipe = recipe.strip("['")
    splitted = recipe.split("\', \'")
    recipe = "\n".join(map(lambda x: "- " + (x.capitalize()), splitted))
    return recipe

def clean_name(name):
    name = name.capitalize()
    name = name.replace("  ", " ")
    return name

def preprocess_function(examples):
    recipes = examples["output"]
    names = examples["input"]

    clean_recipes = []
    clean_names = []
    for recipe, name in zip(recipes, names):
        # Sanitize the name and recipe string
        clean_recipes.append(clean_recipe(recipe))
        clean_names.append(clean_name(name))

    return {"recipes": clean_recipes, "names": clean_names}

def split_dataset():
    from transformers import set_seed
    set_seed(42)
    dataset_id = "formido/recipes-20k"
    dataset = load_dataset(dataset_id)
    dataset = dataset.map(preprocess_function, batched=True, remove_columns=dataset["train"].column_names)
    dataset.push_to_hub("simple_recipes")


if __name__ == "__main__":
    split_dataset()

```