Klue Roberta Small Ner Identified

🚀 klue-roberta-small-ner-identified

This model is a fine - tuned version of vitus9988/klue-roberta-small-ner-identified. It offers entity recognition for personal information anonymization, achieving excellent results on the evaluation set.

This model is a fine - tuned version of vitus9988/klue-roberta-small-ner-identified on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0082
• Precision: 0.9930
• Recall: 0.9988
• F1: 0.9959
• Accuracy: 0.9988

✨ Features

This model provides named - entity recognition for the following items to anonymize personal information:

• Person's name [PS]
• Address (old and road names) [AD]
• Card number [CN]
• Bank account number [BN]
• Driver's license number [DN]
• Resident registration number [RN]
• Passport number [PN]
• Phone number [PH]
• Email address [EM]
• Date [DT]

📚 Documentation

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e - 05
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Framework versions

• Transformers 4.40.2
• Pytorch 2.3.0+cu118
• Datasets 2.19.1
• Tokenizers 0.19.1

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("vitus9988/klue-roberta-small-ner-identified")
model = AutoModelForTokenClassification.from_pretrained("vitus9988/klue-roberta-small-ner-identified")

nlp = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
example = """
저는 김철수입니다. 집은 서울특별시 강남대로이고 전화번호는 010-1234-5678, 주민등록번호는 123456-1234567입니다. 메일주소는 hugging@face.com입니다. 저는 10월 25일에 출국할 예정입니다.
"""

ner_results = nlp(example)
for i in ner_results:
    print(i)

#{'entity_group': 'PS', 'score': 0.9617835, 'word': '김철수', 'start': 3, 'end': 6}
#{'entity_group': 'AD', 'score': 0.9839702, 'word': '서울특별시 강남대로', 'start': 14, 'end': 24}
#{'entity_group': 'PH', 'score': 0.9906756, 'word': '010 - 1234 - 5678', 'start': 33, 'end': 46}
#{'entity_group': 'RN', 'score': 0.9904553, 'word': '123456 - 1234567', 'start': 56, 'end': 70}
#{'entity_group': 'EM', 'score': 0.99022245, 'word': 'hugging @ face. com', 'start': 81, 'end': 97}
#{'entity_group': 'DT', 'score': 0.985629, 'word': '10월 25일', 'start': 105, 'end': 112}