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