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--- |
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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 적용하여 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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## Model Memory Requirements |
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| dtype | Largest Layer or Residual Group | Total Size | Training using Adam | |
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|:-----------------|:----------------------------------|:-------------|:----------------------| |
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| float32 | 187.79 MB | 513.77 MB | 2.01 GB | |
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| float16/bfloat16 | 93.9 MB | 256.88 MB | 1.0 GB | |
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| int8 | 46.95 MB | 128.44 MB | 513.77 MB | |
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| int4 | 23.47 MB | 64.22 MB | 256.88 MB | |
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