AWeirdDev/all-recipes-sm

---
dataset_info:
features:
- name: name
dtype: string
- name: review
dtype: string
- name: rating
dtype: float64
- name: meta
struct:
- name: active_time
dtype: string
- name: additional_time
dtype: string
- name: bake_time
dtype: string
- name: chill_time
dtype: string
- name: cook_time
dtype: string
- name: cool_time
dtype: string
- name: fry_time
dtype: string
- name: marinate_time
dtype: string
- name: prep_time
dtype: string
- name: rest_time
dtype: string
- name: servings
dtype: string
- name: soak_time
dtype: string
- name: stand_time
dtype: string
- name: total_time
dtype: string
- name: yield
dtype: string
- name: ingredients
list:
- name: name
dtype: string
- name: quanity
dtype: string
- name: unit
dtype: string
- name: steps
sequence: string
- name: cooks_note
dtype: string
- name: editors_note
dtype: string
- name: nutrition_facts
struct:
- name: Calories
dtype: string
- name: Carbs
dtype: string
- name: Fat
dtype: string
- name: Protein
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 3102556
num_examples: 2000
download_size: 1262722
dataset_size: 3102556
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- food
- recip
license: mit
task_categories:
- text-classification
- text-generation
- text2text-generation
language:
- en
pretty_name: All Recipes (sm)
size_categories:
- 1K<n<10K
---

# all-recipes-xs (2000)

[All Recipes](https://allrecipes.com) dataset (small).

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("AWeirdDev/all-recipes-sm")
```

Alternatively, load with `pickle` from `_frozen.pkl`:

```python
import pickle
import requests

r = requests.get("https://huggingface.co/datasets/AWeirdDev/all-recipes-sm/resolve/main/_frozen.pkl")
dataset = pickle.loads(r.content)
```

## Features

**Note:** Empty values are presented as `"unknown"` instead of `None` (normally, unless handled by the 🤗 Datasets library).

```python
{
"name": "Dutch … Beef", # Name of the recipe
"review": "I found this recipe attached…", # Subheading
"rating": 5.0, # Overall rating 0 ~ 5
"meta": {
# Metadata. May not be present
"active_time": None,
"additional_time": None,
"bake_time": None,
"chill_time": None,
"cook_time": "4 hrs 5 mins",
"cool_time": None,
"fry_time": None,
"marinate_time": None,
"prep_time": "10 mins",
"rest_time": None,
"servings": "12",
"soak_time": None,
"stand_time": None,
"total_time": "4 hrs 15 mins",
"yield": "12 servings"
},
"ingredients": [
{
"name": "corned beef brisket with spice packet",
"quanity": "1",
"unit": "(4 pound)"
},
...
],
"steps": [
# Steps (unstripped)
" Place corned beef brisket…\n",
" Combine brown sugar, …\n",
" Preheat an outdoor grill …\n",
" Place the coated corned …\n"
],
"cooks_note": "If there is a…", # Cook's Note (if present)
"editors_note": "Nutrition data for this…", # Editor's Note (if present)
"nutrition_facts": {
"Calories": "251",
"Carbs": "16g",
"Fat": "13g",
"Protein": "13g"
},
"url": "https://www.allrecipes.com/recipe/267704/dutch-oven-crunchy-corned-beef/"
}
```

原始信息汇总

数据集概述

数据集名称

• 名称: all-recipes-xs (2000)

数据集特征

• 基本特征:
  • name: 字符串类型
  • review: 字符串类型
  • rating: 浮点数类型（64位）
  • meta: 结构化数据，包含多个时间相关的字段，如active_time, cook_time等，均为字符串类型
  • ingredients: 列表类型，包含名称、数量和单位，均为字符串类型
  • steps: 序列类型，字符串
  • cooks_note: 字符串类型
  • editors_note: 字符串类型
  • nutrition_facts: 结构化数据，包含卡路里、碳水化合物、脂肪和蛋白质，均为字符串类型
  • url: 字符串类型

数据集大小

• 训练集:
  • 字节数: 3102556
  • 示例数: 2000
• 下载大小: 1262722
• 数据集大小: 3102556

数据集配置

• 默认配置:
  • 数据文件路径: data/train-*

数据集标签

• 标签:
  • food
  • recip

许可证

• 许可证: MIT

任务类别

• 任务类别:
  • text-classification
  • text-generation
  • text2text-generation

语言

• 语言: en

数据集别名

• 别名: All Recipes (sm)

大小类别

• 大小类别: 1K<n<10K