README.md

metadata

language:
  - ja
license: apache-2.0
library_name: transformers
pipeline_tag: token-classification
base_model: google-bert/bert-base-multilingual-cased
tags:
  - japanese
  - koyobun
  - orthography
  - kanji-kana
  - government-writing
  - token-classification
metrics:
  - accuracy
model-index:
  - name: scribe-usage-classifier
    results:
      - task:
          type: token-classification
          name: Context-dependent kanji/kana usage judgment
        dataset:
          type: NagaYu/scribe-koyobun-usage
          name: Scribe usage-judgment dataset (test split)
        metrics:
          - type: accuracy
            name: test span accuracy (held-out by document)
            value: 0.818
          - type: accuracy
            name: test hard-set span accuracy
            value: 0.8

scribe-usage-classifier

Token-classification model that judges context-dependent kanji/kana usage in Japanese official documents: given each occurrence of a monitored word, it decides whether the norm writes it in kana or kanji based on the word's syntactic role — a distinction that dictionary/regex replacement cannot make.

• Part of the Scribe project · 💻 GitHub · 🤗 Space · 📚 Dataset
• Base model: google-bert/bert-base-multilingual-cased (~178M params, within the 0.1–0.2B target). Chosen because its fast tokenizer returns offset_mapping, which the span-labeling pipeline needs.

Labels

Per subtoken: O / TARGET-KANA / TARGET-KANJI. Subtokens overlapping a monitored word's span receive the usage label; the input surface may be correct or wrong (the training data includes flipped errors), so the model must learn from context, not surface.

How to use

The model is meant to be driven through the Scribe pipeline, which finds the monitored words and attaches the normative clause as rationale:

from scribe import ScribeChecker
checker = ScribeChecker(model_dir="NagaYu/scribe-usage-classifier")
for f in checker.check("確認する事がある。重い物を運ぶ。"):
    print(f.surface, "→", f.recommended_surface, "|", f.message)

Raw transformers use is also possible (AutoModelForTokenClassification), but you then need to locate the target spans yourself; the Scribe repo does this with a morphological analyzer.

Training data

Fine-tuned on the Scribe usage-judgment dataset (NagaYu/scribe-koyobun-usage), a synthetic seed corpus (CC0) of official-style sentences plus flip-generated errors, split by document. The dataset separates a hard split (words appearing in both usages) as the headline diagnostic.

Evaluation

Span-level accuracy (predicted kind vs. gold kind):

split	span accuracy	hard-set span accuracy
test (held out by document)	0.818	0.800
hard (diagnostic, overlaps train)	0.947	0.947

Evaluate test for generalization; hard cross-cuts the training split and is optimistic. For the project-level comparison against uniform replacement (prh) and a jōyō-kanji checker, see the benchmarks.

Intended use & limitations

• Intended: assisting drafters of Japanese official/public documents by flagging context-dependent kanji/kana choices with a cited clause of the norm; output is phrased as "the norm would write it this way," never as a verdict on the writer.
• Proof-of-concept scale: trained on a small synthetic corpus. It demonstrates that the distinction is learnable from context, but is not production-grade on natural text. For real deployment, train on a licensed real corpus (see scribe/collect.py) and expand the monitored vocabulary (data/norms/lexicon.json).
• Coverage: limited to the monitored formal nouns and auxiliary verbs; other orthographic issues (numbers, punctuation, okurigana) are handled by Scribe's deterministic rule layer, not this model.
• The base model is multilingual; a dedicated Japanese encoder may improve quality if paired with a custom offset aligner.

License

Apache-2.0. The normative documents' rights belong to their issuers; this model references clause numbers only.