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Finding high-quality datasets that are reproducible and accessible can be difficult. One of 🤗 Datasets main goals is to provide a simple way to load a dataset of any format or type. The easiest way to get started is to discover an existing dataset on the [Hugging Face Hub](https://huggingface.co/datasets) - a community-driven collection of datasets for tasks in NLP, computer vision, and audio - and use 🤗 Datasets to download and generate the dataset.
This tutorial uses the [rotten_tomatoes](https://huggingface.co/datasets/rotten_tomatoes) and [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) datasets, but feel free to load any dataset you want and follow along. Head over to the Hub now and find a dataset for your task!
## Load a dataset
Before you take the time to download a dataset, it's often helpful to quickly get some general information about a dataset. A dataset's information is stored inside [`DatasetInfo`] and can include information such as the dataset description, features, and dataset size.
Use the [`load_dataset_builder`] function to load a dataset builder and inspect a dataset's attributes without committing to downloading it:
```py
>>> from datasets import load_dataset_builder
>>> ds_builder = load_dataset_builder("rotten_tomatoes")
# Inspect dataset description
>>> ds_builder.info.description
Movie Review Dataset. This is a dataset of containing 5,331 positive and 5,331 negative processed sentences from Rotten Tomatoes movie reviews. This data was first used in Bo Pang and Lillian Lee, ``Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales.'', Proceedings of the ACL, 2005.
# Inspect dataset features
>>> ds_builder.info.features
{'label': ClassLabel(num_classes=2, names=['neg', 'pos'], id=None),
'text': Value(dtype='string', id=None)}
```
If you're happy with the dataset, then load it with [`load_dataset`]:
```py
>>> from datasets import load_dataset
>>> dataset = load_dataset("rotten_tomatoes", split="train")
```
## Splits
A split is a specific subset of a dataset like `train` and `test`. List a dataset's split names with the [`get_dataset_split_names`] function:
```py
>>> from datasets import get_dataset_split_names
>>> get_dataset_split_names("rotten_tomatoes")
['train', 'validation', 'test']
```
Then you can load a specific split with the `split` parameter. Loading a dataset `split` returns a [`Dataset`] object:
```py
>>> from datasets import load_dataset
>>> dataset = load_dataset("rotten_tomatoes", split="train")
>>> dataset
Dataset({
features: ['text', 'label'],
num_rows: 8530
})
```
If you don't specify a `split`, 🤗 Datasets returns a [`DatasetDict`] object instead:
```py
>>> from datasets import load_dataset
>>> dataset = load_dataset("rotten_tomatoes")
DatasetDict({
train: Dataset({
features: ['text', 'label'],
num_rows: 8530
})
validation: Dataset({
features: ['text', 'label'],
num_rows: 1066
})
test: Dataset({
features: ['text', 'label'],
num_rows: 1066
})
})
```
## Configurations
Some datasets contain several sub-datasets. For example, the [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) dataset has several sub-datasets, each one containing audio data in a different language. These sub-datasets are known as *configurations*, and you must explicitly select one when loading the dataset. If you don't provide a configuration name, 🤗 Datasets will raise a `ValueError` and remind you to choose a configuration.
Use the [`get_dataset_config_names`] function to retrieve a list of all the possible configurations available to your dataset:
```py
>>> from datasets import get_dataset_config_names
>>> configs = get_dataset_config_names("PolyAI/minds14")
>>> print(configs)
['cs-CZ', 'de-DE', 'en-AU', 'en-GB', 'en-US', 'es-ES', 'fr-FR', 'it-IT', 'ko-KR', 'nl-NL', 'pl-PL', 'pt-PT', 'ru-RU', 'zh-CN', 'all']
```
Then load the configuration you want:
```py
>>> from datasets import load_dataset
>>> mindsFR = load_dataset("PolyAI/minds14", "fr-FR", split="train")
```
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