许可证: mit
基础模型: pdelobelle/robbert-v2-dutch-base
标签:
- 训练生成
指标:
- 准确率
- F1值
模型索引:
- 名称: robbert-v2-dutch-base-finetuned-emotion
结果: []
robbert-v2-dutch-base-finetuned-emotion
该模型是基于pdelobelle/robbert-v2-dutch-base在None数据集上微调的版本。
在评估集上取得了以下结果:
- 损失: 3.3545
- 准确率: 0.52
- F1值: 0.5123
模型描述
需要更多信息
预期用途与限制
需要更多信息
训练与评估数据
需要更多信息
训练过程
训练超参数
训练过程中使用了以下超参数:
- 学习率: 2e-05
- 训练批次大小: 32
- 评估批次大小: 32
- 随机种子: 42
- 优化器: Adam，参数betas=(0.9,0.999)，epsilon=1e-08
- 学习率调度器类型: linear
- 训练轮数: 50
训练结果
| 训练损失 |
轮次 |
步数 |
验证损失 |
准确率 |
F1值 |
| 1.5586 |
1.0 |
25 |
1.4429 |
0.42 |
0.2485 |
| 1.4425 |
2.0 |
50 |
1.3576 |
0.46 |
0.3533 |
| 1.2834 |
3.0 |
75 |
1.3207 |
0.5 |
0.4369 |
| 1.1051 |
4.0 |
100 |
1.3228 |
0.48 |
0.4217 |
| 0.9053 |
5.0 |
125 |
1.3705 |
0.49 |
0.4302 |
| 0.7326 |
6.0 |
150 |
1.4522 |
0.53 |
0.5019 |
| 0.5724 |
7.0 |
175 |
1.5445 |
0.53 |
0.5064 |
| 0.4411 |
8.0 |
200 |
1.6560 |
0.54 |
0.5120 |
| 0.3476 |
9.0 |
225 |
1.7233 |
0.51 |
0.4845 |
| 0.2324 |
10.0 |
250 |
1.9150 |
0.52 |
0.5056 |
| 0.1866 |
11.0 |
275 |
2.0207 |
0.52 |
0.4975 |
| 0.165 |
12.0 |
300 |
2.0863 |
0.52 |
0.5094 |
| 0.1291 |
13.0 |
325 |
2.1584 |
0.5 |
0.4833 |
| 0.0762 |
14.0 |
350 |
2.2296 |
0.55 |
0.5332 |
| 0.0577 |
15.0 |
375 |
2.3171 |
0.5 |
0.4986 |
| 0.0424 |
16.0 |
400 |
2.4509 |
0.5 |
0.4795 |
| 0.0253 |
17.0 |
425 |
2.5444 |
0.49 |
0.4917 |
| 0.0191 |
18.0 |
450 |
2.5894 |
0.51 |
0.5031 |
| 0.0123 |
19.0 |
475 |
2.7144 |
0.5 |
0.4995 |
| 0.01 |
20.0 |
500 |
2.7358 |
0.53 |
0.5231 |
| 0.0086 |
21.0 |
525 |
2.8282 |
0.48 |
0.4825 |
| 0.0064 |
22.0 |
550 |
2.8421 |
0.52 |
0.5244 |
| 0.0059 |
23.0 |
575 |
2.9267 |
0.53 |
0.5200 |
| 0.005 |
24.0 |
600 |
2.9568 |
0.52 |
0.5074 |
| 0.0044 |
25.0 |
625 |
3.0420 |
0.47 |
0.4755 |
| 0.0066 |
26.0 |
650 |
3.0421 |
0.48 |
0.4881 |
| 0.0039 |
27.0 |
675 |
3.1039 |
0.51 |
0.4960 |
| 0.0033 |
28.0 |
700 |
3.1226 |
0.51 |
0.4955 |
| 0.0033 |
29.0 |
725 |
3.1215 |
0.51 |
0.4999 |
| 0.003 |
30.0 |
750 |
3.1649 |
0.51 |
0.4980 |
| 0.0025 |
31.0 |
775 |
3.1716 |
0.5 |
0.4921 |
| 0.0028 |
32.0 |
800 |
3.2371 |
0.5 |
0.4956 |
| 0.0028 |
33.0 |
825 |
3.1730 |
0.52 |
0.5154 |
| 0.0055 |
34.0 |
850 |
3.1842 |
0.49 |
0.4884 |
| 0.0022 |
35.0 |
875 |
3.2324 |
0.51 |
0.4955 |
| 0.0023 |
36.0 |
900 |
3.2221 |
0.52 |
0.5089 |
| 0.002 |
37.0 |
925 |
3.2756 |
0.51 |
0.4981 |
| 0.0021 |
38.0 |
950 |
3.2866 |
0.51 |
0.5010 |
| 0.0019 |
39.0 |
975 |
3.2882 |
0.51 |
0.5010 |
| 0.0018 |
40.0 |
1000 |
3.2864 |
0.51 |
0.4967 |
| 0.0017 |
41.0 |
1025 |
3.3101 |
0.51 |
0.4967 |
| 0.0017 |
42.0 |
1050 |
3.3215 |
0.52 |
0.5089 |
| 0.0016 |
43.0 |
1075 |
3.3253 |
0.51 |
0.5043 |
| 0.0056 |
44.0 |
1100 |
3.3118 |
0.51 |
0.5043 |
| 0.0016 |
45.0 |
1125 |
3.3566 |
0.51 |
0.4981 |
| 0.0016 |
46.0 |
1150 |
3.3593 |
0.51 |
0.4981 |
| 0.0016 |
47.0 |
1175 |
3.3638 |
0.51 |
0.4981 |
| 0.0017 |
48.0 |
1200 |
3.3605 |
0.52 |
0.5089 |
| 0.0017 |
49.0 |
1225 |
3.3526 |
0.52 |
0.5123 |
| 0.0016 |
50.0 |
1250 |
3.3545 |
0.52 |
0.5123 |
框架版本
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
Transformers%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20none;%20}%20.st1%20{%20fill:%20%235ce6cf;%20}%20.st2%20{%20fill:%20%230080ff;%20}%20.st3%20{%20clip-path:%20url(%23clippath);%20}%20%3c/style%3e%3cclipPath%20id='clippath'%3e%3crect%20class='st0'%20y='0'%20width='120'%20height='32'/%3e%3c/clipPath%3e%3c/defs%3e%3cg%20class='st3'%3e%3cg%3e%3cpath%20class='st1'%20d='M22.4,32H3.2c-1,0-2-.5-2.6-1.4-.6-.9-.7-2-.4-2.9L9.8,2.1C10.3.8,11.5,0,12.8,0s2.5.8,3,2.1l9.6,25.6c.4,1,.2,2.1-.4,2.9-.6.9-1.6,1.4-2.6,1.4ZM7.8,25.6h10l-5-13.3-5,13.3Z'/%3e%3cpath%20class='st2'%20d='M33.6,32c-1.3,0-2.5-.8-3-2.1L21,4.3c-.6-1.7.2-3.5,1.9-4.1,1.7-.6,3.5.2,4.1,1.9l9.6,25.6c.6,1.7-.2,3.5-1.9,4.1-.4.1-.8.2-1.1.2h0Z'/%3e%3cpath%20d='M71.8,9.1h-6.9v4.6h6.9c2.5,0,4.6,2,4.6,4.6h-6.9c-3.8,0-6.9,3.1-6.9,6.9s3.1,6.9,6.9,6.9h2.3c1.7,0,3.2-.4,4.6-1.2v1.2h4.6v-13.7c0-5-4.1-9.1-9.1-9.1h0ZM71.8,27.4h-2.3c-1.3,0-2.3-1-2.3-2.3s1-2.3,2.3-2.3h6.9c0,2.5-2,4.6-4.6,4.6h0Z'/%3e%3cpath%20d='M52.3,9.1h-4.6V0h-4.6V32h9.1c5,0,9.1-4.1,9.1-9.1v-4.6c0-5.1-4.1-9.1-9.1-9.1h0ZM56.9,22.9c0,2.5-2,4.6-4.6,4.6h-4.6v-13.7h4.6c2.5,0,4.6,2,4.6,4.6v4.6h0Z'/%3e%3cpath%20d='M110.9,13.7h9.1v-4.6h-9.1c-5.1,0-9.1,4.1-9.1,9.1v4.6c0,5.1,4.1,9.1,9.1,9.1h9.1v-4.6h-9.1c-2.5,0-4.6-2-4.6-4.6h13.7v-4.6h-13.7c0-2.5,2-4.6,4.6-4.6h0Z'/%3e%3cpath%20d='M93.6,18.3h-4.6c-1.3,0-2.3-1-2.3-2.3s1-2.3,2.3-2.3h10.3v-4.6h-10.3c-3.8,0-6.9,3.1-6.9,6.9s3.1,6.9,6.9,6.9h4.6c1.3,0,2.3,1,2.3,2.3s-1,2.3-2.3,2.3h-10.3v4.6h10.3c3.8,0,6.9-3.1,6.9-6.9s-3.1-6.9-6.9-6.9Z'/%3e%3c/g%3e%3c/g%3e%3c/svg%3e)