Update README.md

README.md

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 library_name: transformers
-tags: []
+base_model:
+- monologg/kobert
 ---
-
-# Model Card for Model ID
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-<!-- Provide a quick summary of what the model is/does. -->
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-## Model Details
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-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-## Uses
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-### Direct Use
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-## How to Get Started with the Model
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+# KoBERT 기반 한국어 감정 분류 모델
+
+이 프로젝트는 **한국어 텍스트의 감정을 분류**하는 KoBERT 기반의 감정 분류 모델을 학습하고 활용하는 코드를 포함합니다. 이 모델은 입력된 텍스트가 **분노(Anger), 두려움(Fear), 기쁨(Happy), 평온(Tender), 슬픔(Sad)** 중 어떤 감정에 해당하는지를 예측합니다.
+
+## 1. 모델 학습 과정
+
+### Colab 환경 설정 및 데이터 준비
+1. **필요 라이브러리 설치**:
+   `transformers`, `datasets`, `torch`, `pandas`, `scikit-learn` 라이브러리를 설치합니다.
+
+2. **데이터 불러오기**:
+   ai hub 에 등록된 한국어 감성 대화 데이터로부터 감정 분류용 CSV 파일을 불러옵니다.
+
+3. **데이터셋 준비**:
+   - **학습/검증 데이터 분할**: 80%는 학습 데이터로, 20%는 검증 데이터로 사용.
+   - **HuggingFace Dataset 형식 변환**: Pandas DataFrame을 HuggingFace `Dataset`으로 변환.
+   - **레이블 컬럼명 변경**: 감정 레이블을 나타내는 `label_int` 컬럼을 `labels`로 변경.
+   - **데이터 토큰화**: `monologg/kobert` 토크나이저를 이용해 입력 텍스트를 토큰화.
+   - **형식 변환**: `input_ids`, `attention_mask`, `labels`만 남겨 학습 준비 완료.
+
+4. **모델 및 학습 설정**:
+   - **모델**: `monologg/kobert` 모델을 불러와 5개의 감정 레이블을 분류하도록 설정.
+   - **학습 하이퍼파라미터**:
+     - `learning_rate=2e-5`, `num_train_epochs=10`, `batch_size=16`.
+     - F1 스코어를 기반으로 베스트 모델 저장.
+     - Early stopping 적용.
+   
+5. **학습 진행 및 모델 저장**:
+   - 학습 완료 후 모델을 Google Drive에 저장.
+
+### 성능 평가 및 테스트
+- **평가 지표**: Accuracy, F1 score (macro, weighted) 계산.
+- **테스트 데이터 평가**: 학습된 모델을 이용해 테스트 데이터셋 평가.
+
+## 2. 모델 사용 방법
+
+### 사전 준비
+- HuggingFace Hub에서 학습된 모델을 불러와 사용할 수 있습니다. 
+- 모델 및 토크나이저는 `monologg/kobert` 기반이며, 분류 레이블은 다음과 같습니다:
+  - **Anger**: 😡
+  - **Fear**: 😨
+  - **Happy**: 😊
+  - **Tender**: 🥰
+  - **Sad**: 😢
+
+### 사용 예시
+1. **단순 문장 입력 감정 분석**:
+   - 사용자가 입력한 텍스트에 대해 모델이 감정을 예측하고, 각 감정의 확률을 함께 출력합니다.
+
+2. **엑셀 파일에서 감정 분석**:
+   - 엑셀 파일에서 지정한 텍스트 열과 행 범위를 읽어와, 해당 텍스트들에 대해 감정을 분류하고 결과를 출력합니다.
+
+### 코드 사용 예시
+```python
+# 토크나이저 및 모델 로드
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# KoBERT 토크나이저와 모델 로드
+tokenizer = AutoTokenizer.from_pretrained("monologg/kobert", trust_remote_code=True)
+model = AutoModelForSequenceClassification.from_pretrained("rkdaldus/ko-sent5-classification")
+
+# 사용자 입력 텍스트 감정 분석
+text = "오늘 정말 행복해!"
+inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+with torch.no_grad():
+    outputs = model(**inputs)
+predicted_label = torch.argmax(outputs.logits, dim=1).item()
+
+# 감정 레이블 정의
+emotion_labels = {
+    0: ("Angry", "😡"),
+    1: ("Fear", "😨"),
+    2: ("Happy", "😊"),
+    3: ("Tender", "🥰"),
+    4: ("Sad", "😢")
+}
+
+# 예측된 감정 출력
+print(f"예측된 감정: {emotion_labels[predicted_label][0]} {emotion_labels[predicted_label][1]}")