Datasets:

mrkschtr
/

tiny_schiller

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----
-license:
-  - other          # source content: public domain (Schiller, d. 1805)
-  - mit            # pipeline code (this repo)
-language:
-  - de
-multilinguality:
-  - monolingual
-size_categories:
-  - 1M<n<10M
-source_datasets:
-  - original
-task_categories:
-  - text-generation
-  - fill-mask
-task_ids:
-  - language-modeling
-  - masked-language-modeling
-pretty_name: tiny_schiller
-tags:
-  - literature
-  - drama
-  - german
-  - 19th-century
-  - public-domain
-  - schiller
-  - tiny-language-models
----
-# Dataset Card for tiny_schiller
-> *"Das Leben ist nur ein Moment, der Tod ist auch nur einer!"* — Friedrich Schiller
-## Dataset Description
-`tiny_schiller` is a small (~2 MB) plain-text corpus of Friedrich Schiller's
-dramatic works, intended as a German-language analogue to Andrej Karpathy's
-[`tiny_shakespeare`](https://huggingface.co/datasets/tiny_shakespeare). It is
-sized for tutorial-scale language models — character-level RNNs, small
-GPT-style transformers, BPE tokenizer experiments — where the goal is
-reproducibility and pedagogy, not state-of-the-art benchmarking.
-The corpus is a single concatenated file containing 13 works
-(10 dramas, the unfinished *Demetrius* scenario, plus *Briefe über Don Carlos*), drawn from Projekt
-Gutenberg-DE.
-- **Curated by:** Mark Schutera (`schutera`)
-- **Languages:** German (de), 19th-century editorial orthography
-- **License:** see *Licensing Information* below — dual: source content is
-  public domain; pipeline code is MIT
-- **Repository:** <https://github.com/schutera/tiny_schiller>
-### Intended Use
-**Tutorial and teaching**
-- Teaching language-model fundamentals on a non-English literary corpus
-- Tokenizer experiments (character-level, GPT-2 BPE, `cl100k_base`)
-- Small-scale German text-generation demos and overfitting studies
-- A drop-in replacement for `tiny_shakespeare` in lecture material
-**Fine-tuning and domain adaptation**
-- Continued pre-training or domain adaptation target for any fine-tuneable
-  causal LM (e.g. GPT-2, LLaMA, Mistral, Phi): the corpus is clean,
-  stylistically consistent, and free of web noise
-- Style transfer experiments: the corpus provides a strong, distinctive
-  register (elevated 19th-century dramatic German) that can be imprinted
-  on a base model through a short fine-tuning run
-- Persona modeling: speaker turns are tagged, so individual characters can
-  be isolated and used as instruction-tuning or RLHF preference data targets
-- Historical-German language adaptation: useful for probing how well a model
-  generalises to pre-1901-reform orthography after fine-tuning
-**Hugging Face Transformers integration**
-- The corpus loads directly with `datasets.load_dataset` and works with the
-  Transformers `Trainer` / `SFTTrainer` API for causal LM fine-tuning with
-  no preprocessing beyond tokenization
-**Evaluation**
-- Held-out perplexity reference for German literary prose and drama: the
-  `train_test` split provides a small but stylistically coherent test set
-  that is complementary to modern German benchmarks (GermanQuAD, MLQA, etc.)
-### Out-of-Scope
-- Production language modeling (size and coverage are deliberately small;
-  fine-tuning a base model on this corpus alone will not produce a
-  general-purpose German assistant)
-- Stylometry or authorship attribution (single author, single source edition)
-- Modern German NLP evaluation (orthography is 19th-century; perplexity
-  scores are not comparable to contemporary German benchmarks)
-- Critical / scholarly use (no annotations, no critical apparatus)
-- Representative German prose: the corpus is overwhelmingly dramatic dialog;
-  it is not a substitute for a balanced German text corpus
-## Languages
-German (`de`). The text reflects the orthographic conventions of the
-19th-century print editions used as source material — pre-1901 spelling
-reform conventions, German chevron quotation marks (»…«), and editorial
-punctuation that does not match contemporary Duden norms.
-## Dataset Structure
-### Data Instances
-A single `text` field containing the concatenated corpus.
-```python
-FeaturesDict({
-    'text': Text(shape=(), dtype=string),
-})
-```
-### Data Fields
-- `text` *(string)*: full concatenated text of all included works.
-### Splits
-The default release ships a single split. Two configurations are provided:
-| Config         | Split   | Bytes (UTF-8)    |
-|----------------|---------|------------------|
-| `default`      | `train` | 2,216,450 bytes  |
-| `train_test`   | `train` | ~90% of total    |
-| `train_test`   | `test`  | ~10% of total    |
-The 90/10 split is contiguous-tail (last 10% of the concatenated stream is
-held out), matching `tiny_shakespeare` convention; it is **not** a per-work
-split and therefore leaks within-work distribution between train and test.
-Treat it as a sanity-check held-out, not a benchmark split.
-### Line Organization
-After Phase 1b cleaning, the file follows the Karpathy `tiny_shakespeare`
-convention: each speaker turn is preceded by a SPEAKER tag on its own line,
-followed by the spoken text. Stage directions appear inline in parentheses
-or on their own lines, as in the source editions. Act and scene markers
-are preserved as headings.
-### Token Counts
-| Tokenizer       | Token count   | chars/token |
-|-----------------|---------------|-------------|
-| character-level | 2,168,278     | 1.00        |
-| GPT-2 BPE       | 913,675       | 2.37        |
-| `cl100k_base`   | 681,548       | 3.18        |
-### Tokenizer Choice
-**Character-level** is the right default for tutorial use. The 97-character
-vocabulary is small enough that an embedding table and output projection add
-negligible parameters to a teaching-scale model. Every codepoint is
-unambiguous — no out-of-vocabulary tokens, no merging surprises — and the
-prepare script is a single `np.frombuffer`. This is what `tiny_shakespeare`
-uses and why this corpus was designed to be a drop-in replacement.
-**GPT-2 BPE** (`gpt2` via tiktoken) is the right choice when you want to
-reuse a pre-trained tokenizer from Karpathy's nanoGPT tutorials without
-modification. The 50,257-token vocabulary encodes German with 2.37
-chars/token — noticeably worse than cl100k because GPT-2's merge table was
-learned on English text. Umlauts (ä, ö, ü, Ä, Ö, Ü) and ß appear
-frequently in Schiller and they fall outside the most common ASCII merges,
-so many compound words are split more aggressively than necessary. Token ids
-fit in `uint16` (max id 50,256), so `train.bin` / `val.bin` are compact.
-Ready-to-use files are in `schiller_bpe/`.
-**`cl100k_base`** (GPT-4 / Claude-era tokenizer) is the best choice when
-sequence length matters — 682k tokens versus 914k for GPT-2 on the same
-text means 25% more context per fixed-length window. The merge table was
-trained on multilingual data and handles German compounds and umlauts much
-more cleanly (3.18 chars/token). The trade-off: the vocabulary is ~100k
-tokens, so ids require `uint32` rather than `uint16`, and the output
-projection over 100k classes may be disproportionately large for a
-tutorial-scale model. Ready-to-use files are in `schiller_cl100k/`.
-**When to choose which:**
-| Goal | Tokenizer |
-|------|-----------|
-| Drop-in for nanoGPT / makemore tutorials | character-level |
-| Follow Karpathy's nanoGPT BPE tutorial exactly | GPT-2 BPE |
-| Minimise sequence length / maximise context | `cl100k_base` |
-| Cross-lingual transfer from a GPT-4-style model | `cl100k_base` |
-| Explore tokenizer design for German | compare all three |
-## Data Sources
-All texts are sourced from [Projekt Gutenberg-DE](https://www.projekt-gutenberg.org/autoren/namen/schiller.html)
-(projekt-gutenberg.org). Note: this is the German Projekt Gutenberg-DE,
-operated by Hille & Partner / *Der Spiegel*, and is **distinct** from the
-US-based Project Gutenberg at gutenberg.org. The two have different reuse
-terms; see *Licensing Information* below.
-Per-work source URLs are recorded in `scripts/sources.yaml`. Note: SHA-256 hashes of the original HTML are unavailable — the Projekt Gutenberg-DE site restructured in early 2026 and original chapter HTML files are no longer publicly accessible for re-verification.
-Included works:
-- 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 (trilogy as published)
-- Wilhelm Tell
-- Die Braut von Messina
-## Curation Rationale
-`tiny_shakespeare` is a teaching staple because it is small, homogeneous,
-and rich in dialog structure. There is no equally convenient German
-counterpart. Schiller is a natural choice: a single canonical author,
-multiple long dramas with explicit speaker turns, and a body of work
-entirely in the public domain. The goal was a single plain-text artifact
-that drops into existing tutorial pipelines with minimal friction, plus
-a reproducible audit trail (sources, hashes, cleaning script).
-## Considerations for Using the Data
-The corpus is a single 19th-century male author writing about idealised
-historical and political subjects; it encodes the worldview and
-historiographic assumptions of late-Enlightenment German drama. Models
-trained on it will reflect that distribution and should not be used to
-generate text presented as contemporary or neutral.
-- **Orthography:** pre-1901-reform German; downstream evaluation against
-  modern German benchmarks is unfair.
-- **Quotation marks:** German chevrons »…« are preserved; tokenizers may
-  treat them as rare characters.
-- **Dialog-heavy:** overwhelmingly dramatic dialog with speaker tags;
-  not a representative sample of German prose.
-- **Editorial layer:** old non-critical print editions; spelling and
-  punctuation reflect the digitiser's editorial choices, not Schiller's
-  manuscripts.
-- **Size:** ~2 MB / 11 works is a teaching toy. Schiller's poetry,
-  philosophical essays, and historical writing are excluded in v1.
-- **Encoding artifacts:** the cleaning pipeline normalises common HTML
-  boilerplate and mojibake; residual artifacts are tracked in the audit
-  log.
-## Licensing Information
-This dataset has **two distinct licenses**, one for the source content
-and one for the pipeline that produces it.
-### Source content — public domain
-Friedrich Schiller died on 9 May 1805. Under the EU `Schutzdauerrichtlinie`
-(life + 70 years) and equivalent rules in the United States and the
-United Kingdom, all of Schiller's works passed into the public domain
-no later than 1 January 1876, and copyright in the original works has
-not been recoverable since. The texts themselves are therefore free of
-authorial copyright.
-The print editions used as the source for the Projekt Gutenberg-DE
-digitisations are old non-critical editions; they do not carry separate
-editorial / critical-apparatus copyright that would survive in the body
-text. (Modern critical / annotated editions — e.g. the *Nationalausgabe*
-or Hanser editions — are **not** used here, and their annotations would
-remain in copyright.)
-### Pipeline code — MIT
-The scripts, configuration, and documentation in this repository are
-licensed under the MIT License (see `LICENSE`). MIT covers only the
-code, not the textual content.
-### Source-platform terms — Projekt Gutenberg-DE
-Projekt Gutenberg-DE (projekt-gutenberg.org) is an independent compilation
-operated by Hille & Partner / *Der Spiegel*, and applies its own terms
-to redistribution of *its own digitisations* — independent of the
-underlying public-domain status of the works. In paraphrase: the texts
-are made available for **personal, private, non-commercial use**;
-**redistribution of the digitised text in unaltered form requires the
-permission of the editor** (Editor: Gunter Hille). Schools and individuals
-may use the texts for personal, classroom, and research purposes.
-This dataset takes the conservative position that:
-1. The underlying works are public domain and may be re-derived from
-   any source edition, and
-2. As a courtesy and to honour the source-platform terms, attribution
-   to Projekt Gutenberg-DE is preserved at the per-work level
-   (`scripts/sources.yaml`), and the corpus is released for research
-   and educational use.
-For full details, exact wording, and the redistribution edge cases,
-see [`LICENSING.md`](LICENSING.md).
-## 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.}
-}
-```
-If you use the underlying texts, please also acknowledge Projekt
-Gutenberg-DE (<https://www.projekt-gutenberg.org/>).
-## Limitations
-- No critical apparatus, no editorial notes, no philological annotations.
-- No part-of-speech, lemma, or syntactic annotations.
-- Eleven works only at v1; Schiller's poetry, ballads, philosophical
-  essays, and historical writing are not included.
-- Encoding artifacts from the source HTML may remain after cleaning.
-- The 90/10 split is a contiguous-tail split; not suitable as a
-  benchmark.
-- Not intended for production language modeling, modern-German
-  evaluation, or stylometric work.
-## References
-- Projekt Gutenberg-DE — Schiller author page:
-  <https://www.projekt-gutenberg.org/autoren/namen/schiller.html>
-- Projekt Gutenberg-DE — Impressum:
-  <https://www.projekt-gutenberg.org/info/impressum/impressum.html>
-- `tiny_shakespeare` (structural template):
-  <https://huggingface.co/datasets/tiny_shakespeare>
-- EU Directive 2006/116/EC (term of protection of copyright):
-  <https://eur-lex.europa.eu/eli/dir/2006/116/oj>