This release adds two no query strategies, improves the Dataset interface, and introduces optional dependencies.

Added

General:
- We now have a concept for optional dependencies which allows components to rely on soft dependencies, i.e. python dependencies which can be installed on demand (and only when certain functionality is needed).
Datasets:
- The Dataset interface now has a clone() method that creates an identical copy of the respective dataset.
Query Strategies:
- New strategies: DiscriminativeActiveLearning and SEALS.

Changed

Datasets:
- Separated the previous DatasetView implementation into interface (DatasetView) and implementation (SklearnDatasetView).
- Added clone() method which creates an identical copy of the dataset.
Query Strategies:
- EmbeddingBasedQueryStrategy now only embeds instances that are either in the label or in the unlabeled pool (and no longer the entire dataset).
Code examples:
- Code structure was unified.
- Number of iterations can now be passed via an cli argument.
small_text.integrations.pytorch.utils.data:
- Method get_class_weights() now scales the resulting multi-class weights so that the smallest class weight is equal to 1.0.