Update README.md

README.md

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 **Role**: Project Lead & Primary Researcher
 
 ## Overview
-This is a specialized **Personally Identifiable Information (PII)** masking model optimized specifically for the **Korean language**.  
-To achieve the highest possible accuracy, the model has been fine-tuned to work in combination with preprocessing scripts and **regular expressions (regex)**.
-
-**Strong Recommendation**: For production environments, we highly recommend adopting a **hybrid approach** — combining this model with robust rule-based systems (regex + post-processing) — rather than relying on the model alone.
-
-### Supported PII Types (Korean Context)
-The model is designed to detect and mask the following common Korean PII categories:
-
-- Resident Registration Number (주민등록번호)  
-- Credit/Debit Card Number (카드번호)  
-- Telephone Number (전화번호)  
-- Mobile Phone Number (휴대전화번호)  
-- Postal Code (우편번호)  
-- Detailed Address (상세주소)  
-- Bank Account Number (계좌번호)  
-- Passport Number (여권번호)  
-- Driver's License Number (운전면허번호)  
-- Email Address (전자메일)  
-- Customer Name (고객명)  
-- Login ID / Username (로그인ID)  
-- Payment Amount (결제금액)  
-- Merchant Name (가맹점명)  
-- Credit Score (신용점수)  
+
+BERT-based token classification model specialized for **Korean Personally Identifiable Information (PII) masking**.  
+It detects and masks 14 common types of Korean PII entities.
+
+For best accuracy in production, **strongly recommended**: use a **hybrid approach**  
+(pre-processing → model inference → post-processing (Regex and rule based) rather than the model alone.
+
+## Base Model & Architecture
+
+- **Base pretrained model**: KcBERT-Large
+- **Model type**: `BertForTokenClassification`  
+- **Architecture highlights**:
+  - Hidden size       : 1024  
+  - Layers            : 24  
+  - Attention heads   : 16  
+  - Intermediate size : 4096  
+  - Max position embeddings : 300  
+  - Vocab size        : 30,000  
+  - Activation        : GELU  
+  - Dropout           : 0.1 (hidden & attention)
+
+## Supported PII Types (BIO tagging)
+
+1. 가맹점명 (Business Name)  
+2. 결제금액 (Payment Amount)  
+3. 계좌번호 (Account Number)  
+4. 로그인ID (Login ID)  
+5. 상세주소 (Detailed Address)  
+6. 신용점수 (Credit Score)  
+7. 여권번호 (Passport Number)  
+8. 우편번호 (Postal Code)  
+9. 운전면허번호 (Driver's License Number)  
+10. 이름 (Name)  
+11. 전자메일 (Email)  
+12. 전화번호 (Phone Number)  
+13. 주민등록번호 (Resident Registration Number)  
+14. 카드번호 (Card Number)  
+15. 휴대전화번호 (Mobile Phone Number)
+
+## Example
+
+```
+입력: "양철용 고객님, 8월 10일 14:32에 백다방 코엑스점에서 9,910원 결제 내역 확인됩니다."
+
+출력:
+- 발견된 PII:
+  - 양철용 -> [이름]
+  - 백다방 코엑스점 -> [가맹점명]
+  - 9,910원 -> [결제금액]
+```
 
 This list focuses on the most frequently occurring and sensitive personal data types in Korean text/documents.