metadata

language:
  - multilingual
license:
  - cc-by-nc-sa-4.0
multilinguality:
  - multilingual
size_categories:
  - 100M<n<1B
source_datasets:
  - original
task_categories:
  - other
  - object-detection
  - text-retrieval
  - token-classification
  - text-generation
task_ids: []
pretty_name: Mario Maker 2 user likes
tags:
  - text-mining

Mario Maker 2 user likes

Part of the Mario Maker 2 Dataset Collection

Dataset Description

The Mario Maker 2 user likes dataset consists of 105.5 million user likes from Nintendo's online service totaling around 630MB of data. The dataset was created using the self-hosted Mario Maker 2 api over the course of 1 month in February 2022.

How to use it

The Mario Maker 2 user likes dataset is a very large dataset so for most use cases it is recommended to make use of the streaming API of datasets. You can load and iterate through the dataset with the following code:

from datasets import load_dataset

ds = load_dataset("TheGreatRambler/mm2_user_liked", streaming=True, split="train")
print(next(iter(ds)))

#OUTPUT:
{
 'pid': '14510618610706594411',
 'data_id': 25861713
}

Each row is a unique like in the level denoted by the data_id done by the player denoted by the pid.

You can also download the full dataset. Note that this will download ~630MB:

ds = load_dataset("TheGreatRambler/mm2_user_liked", split="train")

Data Structure

Data Instances

{
 'pid': '14510618610706594411',
 'data_id': 25861713
}

Data Fields

Field	Type	Description
pid	string	The player ID of this user, an unsigned 64 bit integer as a string
data_id	int	The data ID of the level this user liked

Data Splits

The dataset only contains a train split.

Dataset Creation

The dataset was created over a little more than a month in Febuary 2022 using the self hosted Mario Maker 2 api. As requests made to Nintendo's servers require authentication the process had to be done with upmost care and limiting download speed as to not overload the API and risk a ban. There are no intentions to create an updated release of this dataset.

Considerations for Using the Data

The dataset contains no harmful language or depictions.