---
license: mit
configs:
- config_name: default
  data_files:
  - split: original
    path: data/original-*
  - split: augmented
    path: data/augmented-*
dataset_info:
  features:
  - name: name
    dtype: string
  - name: fat
    dtype: float64
  - name: texture
    dtype: string
  - name: origin
    dtype: string
  - name: holed
    dtype: int64
  - name: price
    dtype: float64
  - name: protein
    dtype: float64
  splits:
  - name: original
    num_bytes: 1931
    num_examples: 30
  - name: augmented
    num_bytes: 20787
    num_examples: 300
  download_size: 14316
  dataset_size: 22718
language:
- en
tags:
- cheese
- food
- tabular
- ai
- culinary
size_categories:
- n<1K
---
# Dataset Card for Dataset Name

It is a tabular dataset describing cheese and their properties.

## Dataset Details

There are two splits. The split named "original" are real, and the split named "augmented" are generated using the real to expand the dataset.

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->

- **Curated by:** Aslan Noorghasemi
- **Language(s) (NLP):** English
- **License:** MIT

## Uses

<!-- Address questions around how the dataset is intended to be used. 
You can use this dataset to train models to practice how these models work.

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->
Best for AI/ML practicing.

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
It can be used to predict cheese properties.

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
It has two splits and each split has cheese name and its properties. The numerical values are for 100 grams of cheese.

## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->
To practice model training.

### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
It has been sourced from open-source resources on the internet.

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
All numerical values are for 100 grams of cheese.

#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
It doesn't include any personal and sensetive information.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->
The information may not be updated.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

## Dataset Card Contact

Contact me: aslann@cmu.edu