kpfbert-kdpii

townboy/kpfbert-kdpii

Korean PII token-classification model fine-tuned from KPF/KPF-bert-ner on a KDPII-style dialogue dataset.

Dataset

• Source file: 연대1_PII_dataset_V3.json
• Documents: 4981
• Sentences: 53778
• Positive PII sentences: 19037
• Label count: 33

Training Setup

• Max length: 128
• Epochs: 4.0
• Learning rate: 2e-05
• Train batch size: 8
• Eval batch size: 8
• Device: cuda
• GPU: NVIDIA GeForce RTX 4060 Ti
• Mixed precision: auto
• Gradient checkpointing: True

Intended Use

This model is intended for Korean personally identifiable information detection in dialogue-like text. Typical labels include names, nicknames, account numbers, mobile numbers, emails, addresses, IDs, and related sensitive entities.

Quick Inference

from transformers import pipeline

pipe = pipeline(
    "token-classification",
    model="townboy/kpfbert-kdpii",
    aggregation_strategy="simple",
)

print(pipe("Phone 010-8661-5573, ID wanderingrabbit1"))

Notes

• The classification head is reinitialized for the KDPII label space.
• This checkpoint should be validated on your target product traffic before production use.

License and Attribution

This model is released under the MIT License.

This model was fine-tuned from KPF/KPF-bert-ner, which is released under the MIT License.

The model was trained using the KDPII DATASET REVISED.

The KDPII dataset is licensed under CC BY 4.0.

Please provide appropriate attribution to the KDPII dataset when using or redistributing this model.