Update README.md

README.md

@@ -5,7 +5,6 @@ datasets:
 - IconicAI/DDD
 language:
 - en
-- ko
 metrics:
 - accuracy
 - f1
@@ -13,18 +12,35 @@ base_model:
 - Mango-Juice/trpg_mlm
 - microsoft/deberta-v3-large
 library_name: transformers
+model-index:
+  - name: trpg_emotion_classification
+    results:
+      - task:
+          type: text-classification
+        dataset:
+          name: IconicAI/DDD (custom subset manually labeled)
+          type: custom
+          split: test
+          config: csv
+        metrics:
+          - type: accuracy
+            value: 0.929
+          - type: f1
+            value: 0.476
+            name: f1 macro
 ---
+
 # GoEmotions Fine-tuned Model
 
-이 모델은 GoEmotions 데이터셋 및 TRPG 문장으로 훈련된 다중 감정 분류 모델입니다.
+This is a multi-label emotion classification model trained on the GoEmotions dataset and TRPG sentences.
 
-## 모델 정보
+## Model Information
 - **Base Model**: Mango-Juice/trpg_mlm
 - **Task**: Multi-label Emotion Classification
-- **Labels**: 28개의 감정 라벨
-- **Training**: 2차 파인튜닝 완료 (goEmotions 데이터 및 TRPG 문장 데이터)
+- **Labels**: 28 emotion labels
+- **Training**: Completed a two-stage fine-tuning process (1st stage: GoEmotions data, 2nd stage: TRPG sentence data)
 
-## 감정 라벨 목록
+## Emotion Labels
 - admiration
 - amusement
 - anger
@@ -54,17 +70,17 @@ library_name: transformers
 - surprise
 - neutral
 
-## 사용 방법
+## Usage
 
 ```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
-# 모델과 토크나이저 로드
+# Load model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained("Mango-Juice/trpg_emotion_classification")
 model = AutoModelForSequenceClassification.from_pretrained("Mango-Juice/trpg_emotion_classification")
 
-# 추론
+# Inference
 def predict_emotions(text):
     inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
     with torch.no_grad():
@@ -74,18 +90,18 @@ def predict_emotions(text):
     emotion_labels = ['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral']
     return {emotion: float(prob) for emotion, prob in zip(emotion_labels, probs)}
 
-# 예시
+# Example
 text = "I am so happy today!"
 emotions = predict_emotions(text)
 print(emotions)
 ```
 
-## 성능
-- Fine-tuning 완료된 모델로 향상된 감정 분류 성능 제공
-- 희소 클래스에 대한 데이터 증강 적용
+## Performance
+- The fine-tuned model provides improved performance in emotion classification.
+- Data augmentation was applied for minority classes.
 
-## 훈련 세부사항
-- 데이터 증강: 파라프레이징 및 역번역 기반 오버샘플링
-- 손실 함수: Focal Loss with Label Smoothing
-- 옵티마이저: AdamW
-- 스케줄러: ReduceLROnPlateau
+## Training Details
+- **Data Augmentation**: Oversampling based on paraphrasing and back-translation.
+- **Loss Function**: Focal Loss with Label Smoothing
+- **Optimizer**: AdamW
+- **Scheduler**: ReduceLROnPlateau