GroceryList Dataset

Dataset Summary

The GroceryList dataset consists of grocery items and their corresponding categories. It is designed to assist in tasks such as grocery item classification, shopping list organization, and natural language understanding related to common grocery-related terms. The dataset contains only a training split and is not pre-divided into test or validation sets.

It includes two main columns:

1. Item: Contains the names of various grocery items such as "Apple," "Banana," "Milk," etc.
2. Category: Corresponds to the category of the grocery item, such as "Produce," "Dairy & Eggs," "Meat & Seafood," etc.

This dataset is ideal for machine learning tasks related to text classification, particularly in retail and grocery applications.

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Supported Tasks and Use Cases

The GroceryList dataset supports the following tasks:

• Text Classification: Classifying grocery items into predefined categories.
• Shopping List Categorization: Automatically organizing items in shopping lists into categories.
• Retail Data Analysis: Developing models for understanding customer grocery behavior.

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Dataset Structure

The dataset consists only of a training split. Users can create their own validation and test sets if required.

Columns:

• item (string): The name of the grocery item (e.g., "Apple," "Milk").
• category (string): The category to which the item belongs (e.g., "Produce," "Dairy & Eggs").

Example Entry:

Item	Category
Apple	Produce
Banana	Produce
Milk	Dairy & Eggs

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Dataset Usage

To use the dataset in your Hugging Face environment, load it as follows:

from datasets import load_dataset

dataset = load_dataset("AmirMohseni/GroceryList")

Once loaded, you can preprocess and use the data for text classification or other related tasks. Note that the dataset contains only the training data, and you may want to split it manually for validation and testing.

License

This dataset is licensed under the Apache-2.0 License.