README.md

metadata

license: mit
language:
  - ja
metrics:
  - f1
base_model:
  - tohoku-nlp/bert-large-japanese-v2
pipeline_tag: text-classification
tags:
  - japanese
  - grammar
  - classification

Model Card for Model ID

This model is a fine-tuned version of tohoku-nlp/bert-large-japanese-v2 designed to perform multi-class classification of Japanese grammar points. It was trained on labeled data sourced from the 日本語文型辞典 (grammar dictionary) and augmented with synthetic examples generated by a large language model.

Uses

Direct Use

This model takes a Japanese sentence as input and predicts the most likely grammar point(s) used in that sentence. It can be integrated into language-learning applications, grammar checkers, or reading-assistant tools.

TOC

Out-of-Scope Use

• Machine translation or text generation tasks.
• Understanding semantics beyond grammar point identification.

Finetune Details

Finetune Data

Source: 日本語文型辞典 covering ~2400 grammar points. Augmentation: Synthetic sentences generated via a large language model to balance low-frequency grammar points (minimum 20 examples per point).

Finetune Procedure

• Preprocessing: Tokenization with MeCab + Unidic lite; WordPiece subword encoding.
• Batch size: 64
• Max sequence length: 128 tokens
• Optimizer: AdamW (learning rate = 3e-5, weight decay = 0.05)
• Scheduler: Linear warmup of 20% steps, then linear decay
• Epochs: 10
• Mixed precision: Enabled (fp16)

Evaluation

• Test set: Held-out sentences from dictionary and synthetic set (10% of total).
• Metrics:
  • F1 Score (macro): 83.51%
  • Top2 F1 Score (macro): 94.96%