语言:
- 韩语
标签:
- 通过训练生成
评估指标:
- 精确率
- 召回率
- F1值
- 准确率
示例输入:
- 文本: "纳斯达克投资证券引发的波动性价值状态效应为投资者提供了良好机会。"
示例标题: 示例01
- 文本: "TM资金正在柏林证券交易所交易美国保险期货。"
示例标题: 示例02
基础模型: klue/roberta-small
模型索引:
- 名称: ko_fin_ner_roberta_small_model
结果: []
ko_fin_ner_roberta_small_model
该模型是基于klue/roberta-small在None数据集上微调的版本。在评估集上取得了以下结果:
- 损失值: 0.2873
- 精确率: 0.7436
- 召回率: 0.8774
- F1值: 0.8050
- 准确率: 0.9374
模型描述
需要补充更多信息
使用范围和限制
需要补充更多信息
训练和评估数据
需要补充更多信息
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 (beta1=0.9, beta2=0.999, epsilon=1e-08)
- 学习率调度器类型: 线性
- 训练轮数: 30
训练结果
| 训练损失 |
训练轮数 |
步数 |
验证损失 |
精确率 |
召回率 |
F1值 |
准确率 |
| 无记录 |
1.0 |
25 |
1.0272 |
0.1215 |
0.1662 |
0.1404 |
0.7237 |
| 无记录 |
2.0 |
50 |
0.7136 |
0.2360 |
0.4033 |
0.2978 |
0.7695 |
| 无记录 |
3.0 |
75 |
0.5289 |
0.3422 |
0.5586 |
0.4244 |
0.8285 |
| 无记录 |
4.0 |
100 |
0.4404 |
0.4184 |
0.6076 |
0.4956 |
0.8730 |
| 无记录 |
5.0 |
125 |
0.3768 |
0.4124 |
0.6540 |
0.5058 |
0.8866 |
| 无记录 |
6.0 |
150 |
0.3484 |
0.4758 |
0.6975 |
0.5657 |
0.8953 |
| 无记录 |
7.0 |
175 |
0.3236 |
0.5477 |
0.7357 |
0.6279 |
0.9039 |
| 无记录 |
8.0 |
200 |
0.3097 |
0.5702 |
0.7520 |
0.6486 |
0.9015 |
| 无记录 |
9.0 |
225 |
0.3168 |
0.6167 |
0.7629 |
0.6821 |
0.9096 |
| 无记录 |
10.0 |
250 |
0.2950 |
0.6176 |
0.8011 |
0.6975 |
0.9145 |
| 无记录 |
11.0 |
275 |
0.2806 |
0.6674 |
0.8147 |
0.7337 |
0.9195 |
| 无记录 |
12.0 |
300 |
0.2749 |
0.6853 |
0.8365 |
0.7534 |
0.9266 |
| 无记录 |
13.0 |
325 |
0.2743 |
0.7002 |
0.8338 |
0.7612 |
0.9292 |
| 无记录 |
14.0 |
350 |
0.2862 |
0.6774 |
0.8011 |
0.7341 |
0.9238 |
| 无记录 |
15.0 |
375 |
0.2703 |
0.6879 |
0.8529 |
0.7616 |
0.9276 |
| 无记录 |
16.0 |
400 |
0.2752 |
0.7036 |
0.8474 |
0.7689 |
0.9293 |
| 无记录 |
17.0 |
425 |
0.2721 |
0.6998 |
0.8447 |
0.7654 |
0.9305 |
| 无记录 |
18.0 |
450 |
0.2831 |
0.6979 |
0.8311 |
0.7587 |
0.9299 |
| 无记录 |
19.0 |
475 |
0.2857 |
0.7252 |
0.8556 |
0.7850 |
0.9319 |
| 0.2786 |
20.0 |
500 |
0.2792 |
0.7260 |
0.8665 |
0.7901 |
0.9319 |
| 0.2786 |
21.0 |
525 |
0.2604 |
0.7355 |
0.8638 |
0.7945 |
0.9349 |
| 0.2786 |
22.0 |
550 |
0.2603 |
0.7092 |
0.8638 |
0.7789 |
0.9359 |
| 0.2786 |
23.0 |
575 |
0.3026 |
0.7227 |
0.8665 |
0.7881 |
0.9342 |
| 0.2786 |
24.0 |
600 |
0.2800 |
0.7431 |
0.8747 |
0.8035 |
0.9375 |
| 0.2786 |
25.0 |
625 |
0.2838 |
0.7283 |
0.8692 |
0.7925 |
0.9361 |
| 0.2786 |
26.0 |
650 |
0.2813 |
0.7339 |
0.8719 |
0.7970 |
0.9371 |
| 0.2786 |
27.0 |
675 |
0.2881 |
0.7407 |
0.8719 |
0.8010 |
0.9358 |
| 0.2786 |
28.0 |
700 |
0.2894 |
0.7379 |
0.8747 |
0.8005 |
0.9362 |
| 0.2786 |
29.0 |
725 |
0.2889 |
0.7483 |
0.8747 |
0.8065 |
0.9368 |
| 0.2786 |
30.0 |
750 |
0.2873 |
0.7436 |
0.8774 |
0.8050 |
0.9374 |
框架版本
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
PyTorch
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