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amoeba04/
koelectra-small-v3-privacy-ner

Name: koelectra-small-v3-privacy-ner
Availability: InStock

monologg/koelectra-small-v3-discriminator

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KoELECTRA-small-v3-privacy-ner

This model is a fine-tuned version of monologg/koelectra-small-v3-discriminator on a synthesized privacy dataset. It achieves the following results on the evaluation set:

f1 = 0.9998728608843798
loss = 0.05310981854414328
precision = 0.9999237126509853
recall = 0.9998220142897098

Model description

태깅 시스템 : BIO 시스템

-B(begin) : 개체명이 시작할 때
-I(inside) : 토큰이 개체명 중간에 있을 때
O(outside) : 토큰이 개체명이 아닐 경우

12가지 한국인 개인정보 패턴에 대한 태그셋

분류	표기	정의
PERSON	PER	한국인 이름
LOCATION	LOC	한국 주소
RESIDENT REGISTRATION NUMBER	RRN	한국인 주민등록번호
EMAIL	EMA	이메일
ID	ID	일반 로그인 ID
PASSWORD	PWD	일반 로그인 비밀번호
ORGANIZATION	ORG	소속 기관
PHONE NUMBER	PHN	전화번호
CARD NUMBER	CRD	카드번호
ACCOUNT NUMBER	ACC	계좌번호
PASSPORT NUMBER	PSP	여권번호
DRIVER'S LICENSE NUMBER	DLN	운전면허번호

How to use

You can use this model with Transformers pipeline for NER.

python

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("amoeba04/test1")
model = AutoModelForTokenClassification.from_pretrained("amoeba04/test1")
ner = pipeline("ner", model=model, tokenizer=tokenizer)

example = "지난주, 홍길동 씨는 서울특별시 강남구에 위치한 테헤란로 101빌딩에서 진행된 IT 컨퍼런스에 참석했습니다."
ner_results = ner(example)
print(ner_results)

출력: "PER-B, PER-B 씨는 LOC-BLOC-ILOC-I LOC-ILOC-I LOC-ILOC-I LOC-ILOC-I LOC-ILOC-ILOC-I에서 진행된 IT 컨퍼런스에 참석했습니다."

Training and evaluation data

자체 제작한 한국인 개인정보 패턴 기반 개체명 인식 (NER) 데이터셋

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 512
eval_batch_size: 1024
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1
mixed_precision_training: Native AMP

Framework versions

Transformers 4.40.0
Pytorch 2.2.1+cu118
Datasets 2.19.0
Tokenizers 0.19.1

Authors and Acknowledgments

This model was developed by the KNU Vision & Learning (KVL) Lab at Kyungpook National University.

For more information about our work, please visit our website.

amoeba04/koelectra-small-v3-privacy-ner