许可证: MIT
基础模型: pdelobelle/robbert-v2-dutch-base
标签:
- 训练生成
模型索引:
- 名称: robbert-v2-dutch-base-finetuned-emotion-arousal
结果: []
robbert-v2-dutch-base-finetuned-emotion-arousal
该模型是基于pdelobelle/robbert-v2-dutch-base在特定数据集上微调的版本。
在评估集上取得了以下结果:
- 损失值: 0.0230
- 均方根误差(RMSE): 0.1517
模型描述
需补充更多信息
预期用途与限制
需补充更多信息
训练与评估数据
需补充更多信息
训练流程
训练超参数
训练过程中使用的超参数如下:
- 学习率: 2e-05
- 训练批大小: 32
- 评估批大小: 32
- 随机种子: 42
- 优化器: Adam (β1=0.9, β2=0.999, ε=1e-08)
- 学习率调度器类型: 线性
- 训练轮次: 50
训练结果
| 训练损失 |
轮次 |
步数 |
验证损失 |
RMSE |
| 0.0581 |
1.0 |
25 |
0.0349 |
0.1868 |
| 0.0355 |
2.0 |
50 |
0.0340 |
0.1845 |
| 0.0295 |
3.0 |
75 |
0.0271 |
0.1645 |
| 0.0288 |
4.0 |
100 |
0.0279 |
0.1670 |
| 0.0277 |
5.0 |
125 |
0.0316 |
0.1777 |
| 0.0254 |
6.0 |
150 |
0.0263 |
0.1620 |
| 0.0193 |
7.0 |
175 |
0.0247 |
0.1571 |
| 0.0173 |
8.0 |
200 |
0.0304 |
0.1745 |
| 0.0179 |
9.0 |
225 |
0.0239 |
0.1547 |
| 0.0149 |
10.0 |
250 |
0.0244 |
0.1563 |
| 0.0134 |
11.0 |
275 |
0.0248 |
0.1576 |
| 0.0113 |
12.0 |
300 |
0.0256 |
0.1601 |
| 0.0112 |
13.0 |
325 |
0.0265 |
0.1627 |
| 0.0114 |
14.0 |
350 |
0.0299 |
0.1730 |
| 0.0111 |
15.0 |
375 |
0.0268 |
0.1638 |
| 0.0098 |
16.0 |
400 |
0.0256 |
0.1599 |
| 0.009 |
17.0 |
425 |
0.0252 |
0.1588 |
| 0.0078 |
18.0 |
450 |
0.0256 |
0.1601 |
| 0.0093 |
19.0 |
475 |
0.0235 |
0.1532 |
| 0.009 |
20.0 |
500 |
0.0246 |
0.1568 |
| 0.0084 |
21.0 |
525 |
0.0238 |
0.1543 |
| 0.0083 |
22.0 |
550 |
0.0255 |
0.1598 |
| 0.0074 |
23.0 |
575 |
0.0250 |
0.1582 |
| 0.0079 |
24.0 |
600 |
0.0248 |
0.1574 |
| 0.0077 |
25.0 |
625 |
0.0261 |
0.1616 |
| 0.0073 |
26.0 |
650 |
0.0261 |
0.1615 |
| 0.0071 |
27.0 |
675 |
0.0247 |
0.1571 |
| 0.0068 |
28.0 |
700 |
0.0254 |
0.1593 |
| 0.0062 |
29.0 |
725 |
0.0250 |
0.1581 |
| 0.006 |
30.0 |
750 |
0.0255 |
0.1597 |
| 0.0066 |
31.0 |
775 |
0.0241 |
0.1553 |
| 0.0064 |
32.0 |
800 |
0.0242 |
0.1555 |
| 0.006 |
33.0 |
825 |
0.0240 |
0.1549 |
| 0.0055 |
34.0 |
850 |
0.0244 |
0.1561 |
| 0.0055 |
35.0 |
875 |
0.0235 |
0.1533 |
| 0.0053 |
36.0 |
900 |
0.0241 |
0.1551 |
| 0.0056 |
37.0 |
925 |
0.0238 |
0.1542 |
| 0.0052 |
38.0 |
950 |
0.0248 |
0.1576 |
| 0.0055 |
39.0 |
975 |
0.0247 |
0.1570 |
| 0.0054 |
40.0 |
1000 |
0.0233 |
0.1526 |
| 0.0052 |
41.0 |
1025 |
0.0233 |
0.1525 |
| 0.0048 |
42.0 |
1050 |
0.0231 |
0.1519 |
| 0.0051 |
43.0 |
1075 |
0.0237 |
0.1538 |
| 0.0051 |
44.0 |
1100 |
0.0231 |
0.1520 |
| 0.0053 |
45.0 |
1125 |
0.0234 |
0.1531 |
| 0.0046 |
46.0 |
1150 |
0.0230 |
0.1517 |
| 0.0049 |
47.0 |
1175 |
0.0230 |
0.1518 |
| 0.005 |
48.0 |
1200 |
0.0230 |
0.1518 |
| 0.0047 |
49.0 |
1225 |
0.0237 |
0.1540 |
| 0.0047 |
50.0 |
1250 |
0.0230 |
0.1517 |
框架版本
- Transformers 4.42.4
- PyTorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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