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README.md

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----
-language:
-- de
-license:
-- cc0-1.0
-- mit
-license_details: "Text content (Schiller's works) is public domain (CC0). Pipeline code is MIT licensed."
-task_categories:
-- text-generation
-pretty_name: tiny_schiller
-size_categories:
-- 1M<n<10M
----
-
-# tiny_schiller
-
-Inspired by tiny_shakespeare, this brings you the works from the, as a matter of course, best German poet and writer - Friedrich Schiller, to your language models.
-
-*"Das Leben ist Nur ein Moment, der Tod ist auch nur einer!" - Friedrich Schiller*
-
-![Friedrich Schiller](schiller_thug.gif)
-
-## Description
-- Download Size: ~1,982 KB
-- Version 1.0.0 (no release notes)
-
-```python
-# Feature Structure
-FeaturesDict({
-    'text': Text(shape=(), dtype=string),
-})
-```
-
-55,629 lines or 327,233 words or 2,028,161 characters (including spaces) of Friedrich Schiller.
-The document comprises of the following works, originally from [Projekt Gutenberg](https://www.projekt-gutenberg.org/autoren/namen/schiller.html).
-
-
-## Quick Start
-
-Pick the tokenisation that fits your use case and run the corresponding prepare script — it downloads `tiny_schiller.clean.txt` automatically if needed and writes `train.bin` / `val.bin` to the same directory.
-
-```bash
-# Character-level (96-char vocab — smallest, best for teaching)
-python schiller_char/prepare.py
-
-# GPT-2 BPE (50k vocab, uint16 ids — compatible with nanoGPT defaults)
-python schiller_bpe/prepare.py
-
-# cl100k BPE (100k vocab, uint32 ids — 25 % fewer tokens on German text)
-python schiller_cl100k/prepare.py
-```
-
-Then point your nanoGPT config at the generated files, e.g.:
-
-```python
-# in your nanoGPT train.py or config
-dataset = 'schiller_char'   # or schiller_bpe / schiller_cl100k
-```
-
-See [`DATA_CARD.md`](DATA_CARD.md) for corpus statistics, tokenizer comparison, and licensing details.
-
-
-## HuggingFace Datasets
-
-```python
-from datasets import load_dataset
-
-# Full corpus as a single text field
-ds = load_dataset("schutera/tiny_schiller")
-
-# 90/10 train / test split (contiguous tail, same convention as tiny_shakespeare)
-ds = load_dataset("schutera/tiny_schiller", "train_test")
-```
-
-
-## Fine-tuning small LLMs
-
-The corpus works as a continued pre-training or domain adaptation target for any
-causal LM. An end-to-end example using TRL `SFTTrainer` is in
-[`examples/finetune_sft.py`](examples/finetune_sft.py):
-
-```bash
-pip install transformers trl datasets accelerate
-python examples/finetune_sft.py --model TinyLlama/TinyLlama-1.1B-Chat-v1.0
-```
-
-The default context window is **2048 tokens**. Pass `--context_length` to match your model's actual window — there is no config file, just one flag:
-
-```bash
-python examples/finetune_sft.py --model microsoft/Phi-3-mini-4k-instruct --context_length 4096
-python examples/finetune_sft.py --model Qwen/Qwen2.5-0.5B --context_length 4096
-```
-
-To pre-chunk the corpus to a JSONL file instead:
-
-```bash
-python scripts/chunk.py --size 2048                  # gpt2 tokenizer, default
-python scripts/chunk.py --size 4096 --tokenizer cl100k_base
-```
-
-Tested model targets: TinyLlama 1.1B, Phi-3 Mini 3.8B, Llama 3.2 1B/3B, Qwen2.5 0.5B–3B.
-
-
-## Dramen und begleitende Schriften:
-- Briefe über Don Carlos
-- Aus dem Szenar zum »Demetrius«
-- Demetrius
-- Die Huldigung der Künste
-- Die Jungfrau von Orleans
-- Die Räuber
-- Die Verschwörung des Fiesco zu Genua
-- Don Carlos, Infant von Spanien
-- Kabale und Liebe
-- Maria Stuart
-- Wallenstein
-- Wilhelm Tell
-
-## Citation
-
-In conclusion, the works of Friedrich Schiller are a valuable contribution to literature, and this project aims to make them more accessible to language models. We hope you find this resource useful for your research and creative endeavors.
-
-If you use this work in your research or projects, please cite it as follows:
-
-```markdown
-@misc{schutera2023tinyschiller,
-  author       = {Schutera, Mark},
-  title        = {tiny\_schiller: a small German Schiller corpus for tiny language models},
-  year         = {2023},
-  howpublished = {\url{https://github.com/schutera/tiny_schiller}},
-  note         = {Source texts: Projekt Gutenberg-DE, public domain.}
-}
-```
+---
+language:
+- de
+license:
+- cc0-1.0
+- mit
+license_details: "Text content (Schiller's works) is public domain (CC0). Pipeline code is MIT licensed."
+task_categories:
+- text-generation
+pretty_name: tiny_schiller
+size_categories:
+- 1M<n<10M
+---
+
+# tiny_schiller
+
+A small (~2 MB) German-language analogue to Karpathy's [tiny_shakespeare](https://huggingface.co/datasets/tiny_shakespeare) — 12 of Friedrich Schiller's dramatic works, cleaned and tokenised for tutorial-scale language models.
+
+*"Das Leben ist nur ein Moment, der Tod ist auch nur einer."* — Friedrich Schiller
+
+![Friedrich Schiller](schiller_thug.gif)
+
+## Corpus
+
+~2.2 MB · 12 works · 2,168,278 characters · sourced from [Projekt Gutenberg](https://www.projekt-gutenberg.org/autoren/namen/schiller.html)
+
+| Tokenizer | Tokens | chars/token |
+|---|---|---|
+| character-level | 2,168,278 | 1.00 |
+| GPT-2 BPE | 913,675 | 2.37 |
+| `cl100k_base` | 681,548 | 3.18 |
+
+Use **character-level** for teaching-scale models (96-token vocab, no tokenizer needed). Use **cl100k** over GPT-2 when sequence length matters — German umlauts and compounds tokenise 25% more efficiently.
+
+## Works
+
+- Die Räuber
+- Die Verschwörung des Fiesco zu Genua
+- Kabale und Liebe
+- Don Carlos, Infant von Spanien
+- Wallenstein (trilogy)
+- Maria Stuart
+- Die Jungfrau von Orleans
+- Die Braut von Messina
+- Wilhelm Tell
+- Die Huldigung der Künste
+- Briefe über Don Carlos
+- Aus dem Szenar zum »Demetrius«
+
+## Quick Start — nanoGPT
+
+```bash
+python schiller_char/prepare.py    # char-level, 96-vocab
+python schiller_bpe/prepare.py     # GPT-2 BPE, 50k vocab
+python schiller_cl100k/prepare.py  # cl100k, 100k vocab
+```
+
+## HuggingFace Datasets
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("mrkschtr/tiny_schiller")
+print(ds["train"][0]["title"])
+print(ds["train"][0]["text"][:200])
+```
+
+10 works in train, 2 in test (Wilhelm Tell, Die Braut von Messina). Each row is one complete work with `title` and `text` fields.
+
+## Fine-tuning small LLMs
+
+```bash
+pip install transformers trl datasets accelerate
+python examples/finetune_sft.py --model TinyLlama/TinyLlama-1.1B-Chat-v1.0
+```
+
+Default context window is 2048 tokens. Match your model with `--context_length`:
+
+```bash
+python examples/finetune_sft.py --model microsoft/Phi-3-mini-4k-instruct --context_length 4096
+python examples/finetune_sft.py --model Qwen/Qwen2.5-0.5B --context_length 4096
+```
+
+Tested: TinyLlama 1.1B · Phi-3 Mini 3.8B · Llama 3.2 1B/3B · Qwen2.5 0.5B–3B.
+
+## License
+
+Text: public domain (Schiller died 1805). Pipeline code: MIT. See [LICENSING.md](LICENSING.md) for details.
+
+## Citation
+
+```bibtex
+@misc{schutera2023tinyschiller,
+  author       = {Schutera, Mark},
+  title        = {tiny\_schiller: a small German Schiller corpus for tiny language models},
+  year         = {2023},
+  howpublished = {\url{https://github.com/schutera/tiny_schiller}},
+  note         = {Source texts: Projekt Gutenberg-DE, public domain.}
+}
+```

data/test.parquet

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data/train.parquet

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