---
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: 0.896
    - type: macro-f1
      value: 0.892
    - 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 | 0.896 |
| Macro-F1 | 0.892 |
| Subset Accuracy | 0.516 |

 F1-score:
| Emotion | F1-score |
|----------|-----------|
| Dislike | 0.88 |
| Sympathy | 0.90 |
| Sadness | 0.684|
| Surprised | 0.92 |
| Angry | 0.93 |

---