Datasets documentation

Structure your repository

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Structure your repository

To host and share your dataset, create a dataset repository on the Hugging Face Hub and upload your data files.

This guide will show you how to structure your dataset repository when you upload it. A dataset with a supported structure and file format (.txt, .csv, .parquet, .jsonl, .mp3, .jpg, .zip etc.) are loaded automatically with load_dataset(), and it’ll have a dataset viewer on its dataset page on the Hub.

Main use-case

The simplest dataset structure has two files: train.csv and test.csv (this works with any supported file format).

Your repository will also contain a README.md file, the dataset card displayed on your dataset page.

my_dataset_repository/
β”œβ”€β”€ README.md
β”œβ”€β”€ train.csv
└── test.csv

In this simple case, you’ll get a dataset with two splits: train (containing examples from train.csv) and test (containing examples from test.csv).

Define your splits and subsets in YAML

Splits

If you have multiple files and want to define which file goes into which split, you can use the YAML configs field at the top of your README.md.

For example, given a repository like this one:

my_dataset_repository/
β”œβ”€β”€ README.md
β”œβ”€β”€ data.csv
└── holdout.csv

You can define your splits by adding the configs field in the YAML block at the top of your README.md:

---
configs:
- config_name: default
  data_files:
  - split: train
    path: "data.csv"
  - split: test
    path: "holdout.csv"
---

You can select multiple files per split using a list of paths:

my_dataset_repository/
β”œβ”€β”€ README.md
β”œβ”€β”€ data/
β”‚   β”œβ”€β”€ abc.csv
β”‚   └── def.csv
└── holdout/
    └── ghi.csv
---
configs:
- config_name: default
  data_files:
  - split: train
    path:
    - "data/abc.csv"
    - "data/def.csv"
  - split: test
    path: "holdout/ghi.csv"
---

Or you can use glob patterns to automatically list all the files you need:

---
configs:
- config_name: default
  data_files:
  - split: train
    path: "data/*.csv"
  - split: test
    path: "holdout/*.csv"
---

Note that config_name field is required even if you have a single configuration.

Configurations

Your dataset might have several subsets of data that you want to be able to load separately. In that case you can define a list of configurations inside the configs field in YAML:

my_dataset_repository/
β”œβ”€β”€ README.md
β”œβ”€β”€ main_data.csv
└── additional_data.csv
---
configs:
- config_name: main_data
  data_files: "main_data.csv"
- config_name: additional_data
  data_files: "additional_data.csv"
---

Each configuration is shown separately on the Hugging Face Hub, and can be loaded by passing its name as a second parameter:

from datasets import load_dataset

main_data = load_dataset("my_dataset_repository", "main_data")
additional_data = load_dataset("my_dataset_repository", "additional_data")

Builder parameters

Not only data_files, but other builder-specific parameters can be passed via YAML, allowing for more flexibility on how to load the data while not requiring any custom code. For example, define which separator to use in which configuration to load your csv files:

---
configs:
- config_name: tab
  data_files: "main_data.csv"
  sep: "\t"
- config_name: comma
  data_files: "additional_data.csv"
  sep: ","
---

Refer to specific builders’ documentation to see what configuration parameters they have.

You can set a default configuration using default: true, e.g. you can run main_data = load_dataset("my_dataset_repository") if you set

- config_name: main_data
  data_files: "main_data.csv"
  default: true

Automatic splits detection

If no YAML is provided, πŸ€— Datasets searches for certain patterns in the dataset repository to automatically infer the dataset splits. There is an order to the patterns, beginning with the custom filename split format to treating all files as a single split if no pattern is found.

Directory name

Your data files may also be placed into different directories named train, test, and validation where each directory contains the data files for that split:

my_dataset_repository/
β”œβ”€β”€ README.md
└── data/
    β”œβ”€β”€ train/
    β”‚   └── bees.csv
    β”œβ”€β”€ test/
    β”‚   └── more_bees.csv
    └── validation/
        └── even_more_bees.csv

Filename splits

If you don’t have any non-traditional splits, then you can place the split name anywhere in the data file and it is automatically inferred. The only rule is that the split name must be delimited by non-word characters, like test-file.csv for example instead of testfile.csv. Supported delimiters include underscores, dashes, spaces, dots, and numbers.

For example, the following file names are all acceptable:

  • train split: train.csv, my_train_file.csv, train1.csv
  • validation split: validation.csv, my_validation_file.csv, validation1.csv
  • test split: test.csv, my_test_file.csv, test1.csv

Here is an example where all the files are placed into a directory named data:

my_dataset_repository/
β”œβ”€β”€ README.md
└── data/
    β”œβ”€β”€ train.csv
    β”œβ”€β”€ test.csv
    └── validation.csv

Custom filename split

If your dataset splits have custom names that aren’t train, test, or validation, then you can name your data files like data/<split_name>-xxxxx-of-xxxxx.csv.

Here is an example with three splits, train, test, and random:

my_dataset_repository/
β”œβ”€β”€ README.md
└── data/
    β”œβ”€β”€ train-00000-of-00003.csv
    β”œβ”€β”€ train-00001-of-00003.csv
    β”œβ”€β”€ train-00002-of-00003.csv
    β”œβ”€β”€ test-00000-of-00001.csv
    β”œβ”€β”€ random-00000-of-00003.csv
    β”œβ”€β”€ random-00001-of-00003.csv
    └── random-00002-of-00003.csv

Single split

When πŸ€— Datasets can’t find any of the above patterns, then it’ll treat all the files as a single train split. If your dataset splits aren’t loading as expected, it may be due to an incorrect pattern.

Split name keywords

There are several ways to name splits. Validation splits are sometimes called β€œdev”, and test splits may be referred to as β€œeval”. These other split names are also supported, and the following keywords are equivalent:

  • train, training
  • validation, valid, val, dev
  • test, testing, eval, evaluation

The structure below is a valid repository:

my_dataset_repository/
β”œβ”€β”€ README.md
└── data/
    β”œβ”€β”€ training.csv
    β”œβ”€β”€ eval.csv
    └── valid.csv

Multiple files per split

If one of your splits comprises several files, πŸ€— Datasets can still infer whether it is the train, validation, and test split from the file name. For example, if your train and test splits span several files:

my_dataset_repository/
β”œβ”€β”€ README.md
β”œβ”€β”€ train_0.csv
β”œβ”€β”€ train_1.csv
β”œβ”€β”€ train_2.csv
β”œβ”€β”€ train_3.csv
β”œβ”€β”€ test_0.csv
└── test_1.csv

Make sure all the files of your train set have train in their names (same for test and validation). Even if you add a prefix or suffix to train in the file name (like my_train_file_00001.csv for example), πŸ€— Datasets can still infer the appropriate split.

For convenience, you can also place your data files into different directories. In this case, the split name is inferred from the directory name.

my_dataset_repository/
β”œβ”€β”€ README.md
└── data/
    β”œβ”€β”€ train/
    β”‚   β”œβ”€β”€ shard_0.csv
    β”‚   β”œβ”€β”€ shard_1.csv
    β”‚   β”œβ”€β”€ shard_2.csv
    β”‚   └── shard_3.csv
    └── test/
        β”œβ”€β”€ shard_0.csv
        └── shard_1.csv

For more flexibility over how to load and generate a dataset, you can also write a dataset loading script.