Update README.md

README.md

@@ -20,10 +20,10 @@ AI-Task로는 `text-classification`에 해당합니다.
 ---
 ## 2. 🧠 학습 정보
 
-- **Base Model**: KrBERT-Medium (a pre-trained Korean language model based on BERT)  
-- **Source**: [snunlp/KR-Medium](https://huggingface.co/snunlp/KR-Medium)  
+- **Base Model**: KcElectra (a pre-trained Korean language model based on Electra)
+- **Source**: [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator)  
 - **Model Type**: Casual Language Model  
-- **Pre-training (Korean)**: 약 12.37GB (91M + 1.17B words)  
+- **Pre-training (Korean)**: 약 20GB  
 - **Fine-tuning (Hate Dataset)**: 약 22.3MB (`TTA-DQA/hate_sentence`)  
 - **Learning Rate**: `5e-6`  
 - **Weight Decay**: `0.01`  
@@ -31,7 +31,7 @@ AI-Task로는 `text-classification`에 해당합니다.
 - **Batch Size**: `16`  
 - **Data Loader Workers**: `2`  
 - **Tokenizer**: `BertWordPieceTokenizer`  
-- **Model Size**: 약 `405MB`
+- **Model Size**: 약 `512MB`
 
 ---
 
@@ -76,5 +76,5 @@ results = classifier(sentences)'
 
 # 📈 Results
 - Task: binary classification (text-classification)
-- F1-score: 0.99005
-- Accuracy: 0.99005
+- F1-score: 0.9881
+- Accuracy: 0.9881