Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- ko
+library_name: transformers
+base_model:
+- beomi/KcELECTRA-base
+model_type: electra
+pipeline_tag: text-classification
+tags:
+- korean
+- text-classification
+- multi-label-classification
+- electra
+- kcelectra
+- fine-tuned
+datasets:
+- suicide_related_news_comments_ko
+metrics:
+- micro-f1
+- macro-f1
+- subset-accuracy
+task_categories:
+- text-classification
+task_ids:
+- multi-label-classification
+model-index:
+- name: KcELECTRA-base-finetuned-suicide-comments
+  results:
+  - task:
+      type: multi-label text classification
+      name: Multi-label Text Classification
+    dataset:
+      name: suicide_related_news_comments_ko
+      type: custom
+    split: "8:1:1 (train 7119 / val 890 / test 890)"
+    metrics:
+    - type: micro-f1
+      value: 0.769
+    - type: macro-f1
+      value: 0.758
+    - type: subset-accuracy
+      value: 0.516
+---
+
+# KcELECTRA-base-finetuned-suicide-comments
+
+# Training Details
+- Data Split: 8:1:1 (Train: 7,119 / Validation: 890 / Test: 890)  
+- Tokenizer: SentencePiece (KcELECTRA tokenizer)  
+- Max Length: 256  
+- Learning Rate: 3e-5  
+- Batch Size: 16  
+- Epochs: 6  
+- Early Stopping: Patience = 2  
+- Optimizer: AdamW  
+- Threshold Optimization: Independent per-label tuning (criteria = Micro-F1, Macro-F1)  
+  - Thresholds: [0.25, 0.675, 0.8, 0.75, 0.7]  
+
+---
+
+# Result
+
+| Metric | Value |
+|--------|--------|
+| Micro-F1 | 0.769 |
+| Macro-F1 | 0.758 |
+| Subset Accuracy | 0.516 |
+
+ F1-score:
+| Emotion | F1-score |
+|----------|-----------|
+| Dislike | 0.72 |
+| Sympathy | 0.81 |
+| Sadness | 0.64 |
+| Surprised | 0.80 |
+| Angry | 0.82 |
+
+---