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[Lane 1 · numerics] tokenizer.json parity — normalizer / pre-tokenizer survey and NFC strategy for commonMain

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Valutazione

Difficoltà
4/5
Tempo stimato
1-2 giorni
Idoneità per principianti
57/100
Tipo di issue
Funzionalità
Chiarezza
Specificata chiaramente
Stato di attività
Attiva
Stack tecnologico
huggingface, kotlin, markdown, python
Ambito
data, machine-learning

Direzione di ricerca

Start by reading the two linked ModernBERT and mmBERT tokenizer.json files and the Hugging Face tokenizers documentation, then use a small Python script with json.load to survey 15–20 checkpoint files. Record the requested tokenizer fields in a Markdown table with exact revision links, summarize normalizer and pre-tokenizer types and merge formats, and compare NFC options using the linked Unicode data. Done means posting the completed table and recommendation as a comment on #1321.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

good first issue research size:s skill:numerics sub-issue

Sub-issue of #1321 (HF tokenizer.json parity for BPE encoders).

Lane: 1 · Numerics / Research
Skill needed: Python and the Hugging Face tokenizers library. No Kotlin, no SKaiNET codebase knowledge.
Size: s (an afternoon)
Blocked by: nothing

What to do

  1. Pick 15–20 public checkpoints that ship a tokenizer.json and cover the families SKaiNET loads or plans to load: GPT-2, Qwen2.5/Qwen3, Llama 3.x, Gemma 3/4, Mistral (HF export), SmolLM2, BitNet, ModernBERT, mmBERT, BERT-base, XLM-R, T5, Whisper, Moonshine.
  2. For each file record: model.type, model.byte_fallback, model.ignore_merges, the form of model.merges[0] (string or array), the full normalizer block, the pre_tokenizer block, the decoder block, and the number of added_tokens with special: false. A 30-line script with json.load is enough — no need to run the tokenizers.
  3. Produce one Markdown table, sorted by family, and a short section answering: which normalizer types occur at all (expected: NFC, NFKC, Replace, Prepend, Sequence, BertNormalizer, Lowercase, StripAccents), which pre-tokenizer types (ByteLevel, Metaspace, Split, Sequence, BertPreTokenizer), and how many files use the array form of merges.
  4. Recommend an NFC strategy for Kotlin commonMain: (a) expect/actual with java.text.Normalizer on JVM and String.prototype.normalize on JS/Wasm, with a passthrough on Kotlin/Native; (b) a table-driven implementation (quote the table size for the composition exclusions + canonical compositions); or (c) something better. One paragraph per option, one recommendation.
  5. Post the table and recommendation as a comment on #1321.

Acceptance

  • Table with ≥ 15 checkpoints, each row linking the exact tokenizer.json revision on the Hub
  • Count of files whose merges are in array form vs string form
  • NFC recommendation with a link to the Unicode data it would depend on
  • Result reported back on #1321

Notes

The two files that triggered the parent are ModernBERT and mmBERT; include both so the Kotlin lanes can cross-check. If you find a normalizer type that cannot be implemented without a large Unicode table (e.g. NFKC), say so — that is a finding, not a blocker.

Lingua principale
Kotlin
Stelle
52
Fork
15
Merge medio
1g 15h
PR unite (30g)
36

Preparare l'ambiente

Come iniziare

  1. Leggi tutta la issue e poi la guida ai contributi del progetto.
  2. Commenta sulla issue per dire che te ne occupi tu — evita che due persone facciano lo stesso lavoro.
  3. Fai un fork del repository e lavora su un branch.
  4. Apri una pull request che faccia riferimento al numero della issue.

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