🤖 AI Summary
This work addresses the current lack of ready-to-use German literary datasets suitable for small language models, as existing corpora typically require extensive preprocessing. To bridge this gap, the authors introduce GerDraCor, a compact (2.07 MB), single-file corpus comprising 11 public-domain Schiller dramas, derived from DraCor under a CC0 license and processed through a deterministic pipeline. GerDraCor provides the first plug-and-play German literary dataset analogous to tiny_shakespeare, supporting character-level, GPT-2 BPE, and cl100k_base tokenization schemes. It further enables persona-based segmentation by dramatic character and is fully compatible with Hugging Face’s datasets library, allowing one-line loading. This resource substantially lowers the barrier to incorporating canonical German texts into training workflows for resource-constrained models.
📝 Abstract
tiny_schiller closes the small-language-model prototyping, fine-tuning, education, and research gap for German literary text, providing a single-file, drop-in counterpart to Karpathy's tiny_shakespeare. The available German literary corpora are larger and richer, but require parser engineering before a single line of training or fine-tuning code can run. tiny_schiller is a 2.07-megabyte single file of eleven public-domain Schiller dramas, sourced from DraCor's GerDraCor export (CC0) and processed by deterministic parser engineering. Character-level, GPT-2 byte-pair encoding, and cl100k_base tokenization splits, an instruction-formatted dialogue-completion split, and 89 per-character persona splits load from a single HuggingFace call. A small language model literally reaches German literary text in one line of code.