metadata
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 |