Token Classification
Transformers
Safetensors
Japanese
bert
japanese
koyobun
orthography
kanji-kana
government-writing
Eval Results (legacy)
Instructions to use NagaYu/scribe-usage-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NagaYu/scribe-usage-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="NagaYu/scribe-usage-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("NagaYu/scribe-usage-classifier") model = AutoModelForTokenClassification.from_pretrained("NagaYu/scribe-usage-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Fix model-index metadata (add required dataset field)
Browse files
README.md
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- task:
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type: token-classification
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name: Context-dependent kanji/kana usage judgment
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metrics:
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- type: accuracy
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name: test span accuracy (held-out by document)
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- task:
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type: token-classification
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name: Context-dependent kanji/kana usage judgment
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dataset:
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type: NagaYu/scribe-koyobun-usage
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name: Scribe usage-judgment dataset (test split)
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metrics:
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- type: accuracy
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name: test span accuracy (held-out by document)
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