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README.md
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### Method
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- Promt-Tuning/Prefix-tuning/Soft Embedding
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### References
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- Prompt Tuning: <a href="https://arxiv.org/abs/2104.08691" download>**The Power of Scale for Parameter-Efficient Prompt Tuning**</a>
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- Prompt Tuning v2: <a href="https://arxiv.org/abs/2110.07602" download>**P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks**</a>
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### Method
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- Promt-Tuning/Prefix-tuning/Soft Embedding
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### Metrics
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| | precision | recall | f1-score | support |
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|--------------|-----------|--------|----------|---------|
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| 긍정 | 0.92549 | 0.944 | 0.934653 | 500 |
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| 부정 | 0.942857 | 0.924 | 0.933333 | 500 |
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| accuracy | 0.934 | 0.934 | 0.934 | 0.934 |
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| macro avg | 0.934174 | 0.934 | 0.933993 | 1000 |
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| weighted avg | 0.934174 | 0.934 | 0.933993 | 1000 |
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### References
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- Prompt Tuning: <a href="https://arxiv.org/abs/2104.08691" download>**The Power of Scale for Parameter-Efficient Prompt Tuning**</a>
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- Prompt Tuning v2: <a href="https://arxiv.org/abs/2110.07602" download>**P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks**</a>
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