v0.4.5 - niceties

Breaking changes

• There are stronger input checks to avoid silent failures. prompts cannot be empty. completions cannot be empty or a pure string (it has to be a sequence of strings)

New features

• Pass normalize=False when you want raw, unnormalized probabilities for, e.g., multi-label classification applications
• You can input a single prompt string or Example object. You no longer have to wrap it in a list and then unwrap it
• You can disable progress bars using show_progress_bar=False
• cappr.huggingface type-hints the model as a ModelForCausalLM for greater clarity

Bug fixes

• cappr.huggingface doesn't modify the model or tokenizer anymore, sorry bout that
• The jagged/inhomogenous numpy array warning from earlier numpy versions (when using _examples functions) is correctly handled