hyunwoo3235 commited on
Commit
a457550
1 Parent(s): 2983bd1

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +43 -0
README.md CHANGED
@@ -1,3 +1,46 @@
1
  ---
2
  license: apache-2.0
 
 
 
 
3
  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
  license: apache-2.0
3
+ language:
4
+ - ko
5
+ tags:
6
+ - deberta-v3
7
  ---
8
+ # deberta-v3-base-korean
9
+
10
+ ## Model Details
11
+
12
+ DeBERTa는 Disentangled Attention과 Enhanced Masked Language Model을 통해 BERT의 성능을 향상시킨 모델입니다.
13
+ 그중 DeBERTa V3은 ELECTRA-Style Pre-Training에 Gradient-Disentangled Embedding Sharing을 적용사여 DeBERTA를 개선했습니다.
14
+
15
+ 이 연구는 구글의 TPU Research Cloud(TRC)를 통해 지원받은 Cloud TPU로 학습되었습니다.
16
+
17
+ ## How to Get Started with the Model
18
+
19
+ ```python
20
+ from transformers import AutoTokenizer, DebertaV2ForSequenceClassification
21
+
22
+ tokenizer = AutoTokenizer.from_pretrained("team-lucid/deberta-v3-base-korean")
23
+ model = DebertaV2ForSequenceClassification.from_pretrained("team-lucid/deberta-v3-base-korean")
24
+
25
+ inputs = tokenizer("안녕, 세상!", return_tensors="pt")
26
+ outputs = model(**inputs)
27
+ ```
28
+
29
+ ## Evaluation
30
+
31
+ | | Backbone<br/>Parameters(M) | **NSMC**<br/>(acc) | **PAWS**<br/>(acc) | **KorNLI**<br/>(acc) | **KorSTS**<br/>(spearman) | **Question Pair**<br/>(acc) |
32
+ |:-------------------|:--------------------------:|:------------------:|:------------------:|:--------------------:|:-------------------------:|:---------------------------:|
33
+ | DistilKoBERT | 22M | 88.41 | 62.55 | 70.55 | 73.21 | 92.48 |
34
+ | KoBERT | 85M | 89.63 | 80.65 | 79.00 | 79.64 | 93.93 |
35
+ | XLM-Roberta-Base | 85M | 89.49 | 82.95 | 79.92 | 79.09 | 93.53 |
36
+ | KcBERT-Base | 85M | 89.62 | 66.95 | 74.85 | 75.57 | 93.93 |
37
+ | KcBERT-Large | 302M | 90.68 | 70.15 | 76.99 | 77.49 | 94.06 |
38
+ | KoELECTRA-Small-v3 | 9.4M | 89.36 | 77.45 | 78.60 | 80.79 | 94.85 |
39
+ | KoELECTRA-Base-v3 | 85M | 90.63 | 84.45 | 82.24 | **85.53** | 95.25 |
40
+ | Ours | | | | | | |
41
+ | DeBERTa-xsmall | 22M | 91.21 | 84.40 | 82.13 | 83.90 | 95.38 |
42
+ | DeBERTa-small | 43M | **91.34** | 83.90 | 81.61 | 82.97 | 94.98 |
43
+ | DeBERTa-base | 86M | 91.22 | **85.5** | **82.81** | 84.46 | **95.77** |
44
+
45
+ \* 다른 모델의 결과는 [KcBERT-Finetune](https://github.com/Beomi/KcBERT-Finetune)
46
+ 과 [KoELECTRA](https://github.com/monologg/KoELECTRA)를 참고했으며, Hyperparameter 역시 다른 모델과 유사하게 설정습니다.