Configure STL Dataset (STL)

The single-task learning dataset is a single dataset which has training, validation and test data.

STL dataset is a sub-config under the experiment index config (STL). To configure a custom STL dataset, you need to create a YAML file in stl_dataset/ folder.

Below shows examples of the STL dataset config.

Example

configs
├── __init__.py
├── entrance.yaml
├── experiment
│   ├── example_stl_train.yaml
│   └── ...
├── stl_dataset
│   └── mnist.yaml
...

configs/experiment/mnist.yaml

defaults:
  ...
    - /stl_dataset: mnist.yaml
  ...

configs/mtl_dataset/multi_domain_sentiment.yaml

_target_: clarena.stl_datasets.MNIST
(TBC)

Supported MTL Datasets & Required Config Fields

In CLArena, we also implemented many STL datasets as Python classes in clarena.stl_datasets module that you can use for your experiment.

To choose a STL dataset, assign the _target_ field to the class name of the STL dataset. For example, to use the MNIST dataset, set _target_ field to clarena.stl_datasets.MNIST. Each STL dataset has its own hyperparameters and configurations, which means it has its own required fields. The required fields are the same as the arguments of the class specified by _target_. The arguments of each STL dataset class can be found in API documentation.

<i class=“bi bi-book”></i> API Reference (STL Datasets) <i class=“fa-brands fa-github”></i> Source Code (STL Datasets)

Below is the full list of supported STL datasets. We only support image classification datasets. Note that the “STL Dataset” is exactly the class name that the _target_ field is assigned.

STL Dataset	Description	Required Config Fields
MNIST (TBC)	TBC.	Same as MNIST class arguments