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1from transformers import AutoTokenizer, DebertaV2ForSequenceClassification
2
3tokenizer = AutoTokenizer.from_pretrained("team-lucid/deberta-v3-xsmall-korean")
4model = DebertaV2ForSequenceClassification.from_pretrained("team-lucid/deberta-v3-xsmall-korean")
5
6inputs = tokenizer("안녕, 세상!", return_tensors="pt")
7outputs = model(**inputs)| Backbone Parameters(M) | NSMC (acc) | PAWS (acc) | KorNLI (acc) | KorSTS (spearman) | Question Pair (acc) | |
|---|---|---|---|---|---|---|
| DistilKoBERT | 22M | 88.41 | 62.55 | 70.55 | 73.21 | 92.48 |
| KoBERT | 85M | 89.63 | 80.65 | 79.00 | 79.64 | 93.93 |
| XLM-Roberta-Base | 85M | 89.49 | 82.95 | 79.92 | 79.09 | 93.53 |
| KcBERT-Base | 85M | 89.62 | 66.95 | 74.85 | 75.57 | 93.93 |
| KcBERT-Large | 302M | 90.68 | 70.15 | 76.99 | 77.49 | 94.06 |
| KoELECTRA-Small-v3 | 9.4M | 89.36 | 77.45 | 78.60 | 80.79 | 94.85 |
| KoELECTRA-Base-v3 | 85M | 90.63 | 84.45 | 82.24 | 85.53 | 95.25 |
| Ours | ||||||
| DeBERTa-xsmall | 22M | 91.21 | 84.40 | 82.13 | 83.90 | 95.38 |
| DeBERTa-small | 43M | 91.34 | 83.90 | 81.61 | 82.97 | 94.98 |
| DeBERTa-base | 86M | 91.22 | 85.5 | 82.81 | 84.46 | 95.77 |