DATA_CARD.md

DATA_CARD — tiny_schiller (agent-ready)

This file is a compact, copy/paste-friendly summary of the tiny_schiller corpus and how to load it.

What this is

• Corpus: 11 public-domain dramatic works by Friedrich Schiller (German)
• Goal: a drop-in German analogue to Karpathy’s tiny_shakespeare for small-LM prototyping, fine-tuning, and education
• Release artifact: a cleaned, single UTF-8 text file + deterministic preprocessing scripts

Quantitative summary

Corpus file (cleaned)

• File: tiny_schiller.parsed.txt
• Characters: 2,019,857
• Bytes (UTF-8): 2,067,041 (~2.07 MB)
• Encoding: UTF-8 (no BOM)
• Line endings: LF
• Unique codepoints: 88
• Guillemets « / »: 101 / 101
• Works: 11

Precomputed token streams (90/10 train/val)

• schiller_char/
  • Vocab: 88
  • Train tokens: 1,817,871
  • Val tokens: 201,986
  • chars/token: 1.00
• schiller_bpe/ (GPT-2 BPE)
  • Vocab: 50,257
  • Train tokens: 768,768
  • Val tokens: 85,844
  • Total tokens: 854,611
  • chars/token: 2.36
• schiller_cl100k/ (cl100k_base)
  • Vocab: ~100k
  • Train tokens: 578,182
  • Val tokens: 64,412
  • Total tokens: 642,593
  • chars/token: 3.14

Fine-tuning parquet files (Hub: data/)

• Whole-work split: data/train.parquet, data/test.parquet
  • Rows: 9 train works, 2 test works
  • Fields: title, text
• Instruction-formatted dialogue completion: data/instruct.parquet
  • Rows: 7,607
  • Fields: prompt, completion, work, character
• Per-character persona splits: data/char_*.parquet
  • Files: 89
  • Example row counts: char_WALLENSTEIN.parquet 264, char_CARLOS.parquet 235, char_MOOR.parquet 130

Reference fine-tune (paper)

• Model: Qwen/Qwen2.5-0.5B-Instruct (494M params)
• Hardware: NVIDIA RTX 3060 (12 GB VRAM), bf16
• Stage 1: 2 epochs on instruct.parquet, batch 4, context 512, lr 2e-4
• Stage 2: 2 epochs on char_MOOR.parquet, lr 1e-4, weight decay 0.05
• Wall-clock time: 2.7 hours total (2.6 h stage 1, <3 min stage 2)
• Reported outcome: 99.05% token accuracy, entropy 0.055 (two epochs, weight decay 0.05)
• Recipe: examples/reference_finetune.py (two-stage run)

Canonical text file

• tiny_schiller.parsed.txt
  • UTF-8, no BOM
  • LF line endings
  • Canonical speaker turns in the form SPEAKER:\ntext

Precomputed tokenization splits

These match common small-LM workflows (e.g. nanoGPT-style train.bin/val.bin).

• schiller_char/ — character-level (88 unique characters)
• schiller_bpe/ — GPT-2 BPE via tiktoken
• schiller_cl100k/ — cl100k_base via tiktoken

Build locally:

python schiller_char/prepare.py
python schiller_bpe/prepare.py
python schiller_cl100k/prepare.py

HuggingFace datasets (whole works)

Dataset id: mrkschtr/tiny_schiller

Each row is one work with at least title and text fields.

from datasets import load_dataset

ds = load_dataset("mrkschtr/tiny_schiller")
print(ds["train"][0]["title"])
print(ds["train"][0]["text"][:200])

Split policy (paper/README): train holds out Wilhelm Tell and Die Braut von Messina as test.

Instruction + per-character persona splits (parquet)

These ship on the Hub under data/.

• General dialogue-completion: data/instruct.parquet (7,607 rows)
• Per-character persona files: data/char_*.parquet (89 files)

Schema per row:

• prompt: instruction template + preceding context turns
• completion: next speaker turn in NAME:\ntext format
• work: work title
• character: normalized speaker id (uppercase, underscores)

Load examples:

from datasets import load_dataset

# Instruction-formatted dialogue completions
ins = load_dataset(
    "mrkschtr/tiny_schiller",
    data_files="data/instruct.parquet",
    split="train",
)

# One character persona dataset
moor = load_dataset(
    "mrkschtr/tiny_schiller",
    data_files="data/char_MOOR.parquet",
    split="train",
)

Rebuild locally (writes to data/ by default):

python scripts/build_instruct.py
python scripts/build_instruct.py --list-characters
python scripts/build_instruct.py --character MOOR

Intended use

• Rapid prototyping of model/training/tokenization choices under tight compute
• Stylistic fine-tuning on a homogeneous German literary register
• Per-character persona fine-tuning using the prebuilt char_*.parquet splits
• Education and reproducibility baselines on a non-English “tiny corpus”

Licensing (read this)

• Text content (tiny_schiller.txt, tiny_schiller.parsed.txt): public domain (Schiller died 1805) and redistributed from DraCor / GerDraCor under CC0
• Code + documentation (everything else, incl. scripts and this file): MIT

See LICENSING.md for details and links to the upstream CC0 claim.