klue-roberta-small-ner-identified

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klue-roberta-small-ner-identified

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

Model description

개인정보 비식별을 위해 아래 항목에 대한 개체명 인식을 제공합니다.

• 사람이름 [PS]
• 주소 (구 주소 및 도로명 주소) [AD]
• 카드번호 [CN]
• 계좌번호 [BN]
• 운전면허번호 [DN]
• 주민등록번호 [RN]
• 여권번호 [PN]
• 전화번호 [PH]
• 이메일 주소 [EM]
• 날짜 [DT]

License

This model is a fine-tuned derivative of klue/roberta-small. Since the base model does not currently specify a license, no license is specified for this derivative model at this time.

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

Use

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 [email protected]입니다. 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}