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README.md
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license: apache-2.0
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license: apache-2.0
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language:
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- ko
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tags:
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- deberta-v3
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---
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# deberta-v3-base-korean
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## Model Details
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DeBERTa는 Disentangled Attention과 Enhanced Masked Language Model을 통해 BERT의 성능을 향상시킨 모델입니다.
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그중 DeBERTa V3은 ELECTRA-Style Pre-Training에 Gradient-Disentangled Embedding Sharing을 적용사여 DeBERTA를 개선했습니다.
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이 연구는 구글의 TPU Research Cloud(TRC)를 통해 지원받은 Cloud TPU로 학습되었습니다.
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## How to Get Started with the Model
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```python
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from transformers import AutoTokenizer, DebertaV2ForSequenceClassification
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tokenizer = AutoTokenizer.from_pretrained("team-lucid/deberta-v3-base-korean")
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model = DebertaV2ForSequenceClassification.from_pretrained("team-lucid/deberta-v3-base-korean")
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inputs = tokenizer("안녕, 세상!", return_tensors="pt")
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outputs = model(**inputs)
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```
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## Evaluation
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| | Backbone<br/>Parameters(M) | **NSMC**<br/>(acc) | **PAWS**<br/>(acc) | **KorNLI**<br/>(acc) | **KorSTS**<br/>(spearman) | **Question Pair**<br/>(acc) |
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|:-------------------|:--------------------------:|:------------------:|:------------------:|:--------------------:|:-------------------------:|:---------------------------:|
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| DistilKoBERT | 22M | 88.41 | 62.55 | 70.55 | 73.21 | 92.48 |
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| KoBERT | 85M | 89.63 | 80.65 | 79.00 | 79.64 | 93.93 |
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| XLM-Roberta-Base | 85M | 89.49 | 82.95 | 79.92 | 79.09 | 93.53 |
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| KcBERT-Base | 85M | 89.62 | 66.95 | 74.85 | 75.57 | 93.93 |
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| KcBERT-Large | 302M | 90.68 | 70.15 | 76.99 | 77.49 | 94.06 |
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| KoELECTRA-Small-v3 | 9.4M | 89.36 | 77.45 | 78.60 | 80.79 | 94.85 |
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| KoELECTRA-Base-v3 | 85M | 90.63 | 84.45 | 82.24 | **85.53** | 95.25 |
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| Ours | | | | | | |
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| DeBERTa-xsmall | 22M | 91.21 | 84.40 | 82.13 | 83.90 | 95.38 |
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| DeBERTa-small | 43M | **91.34** | 83.90 | 81.61 | 82.97 | 94.98 |
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| DeBERTa-base | 86M | 91.22 | **85.5** | **82.81** | 84.46 | **95.77** |
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\* 다른 모델의 결과는 [KcBERT-Finetune](https://github.com/Beomi/KcBERT-Finetune)
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과 [KoELECTRA](https://github.com/monologg/KoELECTRA)를 참고했으며, Hyperparameter 역시 다른 모델과 유사하게 설정습니다.
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