Bavard-ml-utils

Since `0.1.0`, the package has seen a lot of additions, including:
- A new [docs site](https://docs-bavard-ml-utils.web.app/).
- [`bavard_ml_utils.ml.conv_graph.ConvGraph`](https://docs-bavard-ml-utils.web.app/src/bavard_ml_utils.ml.conv_graph.html#bavard_ml_utils.ml.conv_graph.ConvGraph), which allows a graph to be built out of a chatbot conversations dataset. The graph can be used for analysis, dataset augmentation, and computing "soft" metrics. See _"Conversation Graph: Data Augmentation, Training, and Evaluation for Non-Deterministic Dialogue Management"_ by Gritta et al. 2021 for more details.
- The [`bavard_ml_utils.persistence`](https://docs-bavard-ml-utils.web.app/src/bavard_ml_utils.persistence.html) sub-package, which includes features for easily persisting [Pydantic](https://pydantic-docs.helpmanual.io/) objects, as well as versioned artifacts produced by machine learning models. Supports Pydantic objects with `numpy.ndarray` fields out of the box.
- And more!

- The Bavard in-memory data model interfaces in `bavard_ml_common.types` have been refactored to be in harmony with the latest.
- A new `bavard_ml_common.ml.dataset.LabeledDataset` abstract class has been added which can allow an arbitrarily-typed labeled list acting as a dataset to inherit nice behavior for things like cross validation, train/test splitting, and upsampling to balance by label.

- Updated dependencies
- New deep learning framework agnostic (pure numpy) one-hot utility. Can be used for arrays of arbitrary dimensionality.