library_name: transformers
base_model: MHGanainy/roberta-base-legal-multi
tags:
- generated_from_trainer
metrics:
- 精确率
- 召回率
- F1值
- 准确率
model-index:
- name: roberta-base-legal-multi-downstream-indian-ner
results: []
roberta-base-legal-multi-downstream-indian-ner
本模型是基于MHGanainy/roberta-base-legal-multi在未知数据集上微调的版本，其评估集表现如下：
- 损失值：0.2526
- 精确率：0.6406
- 召回率：0.8244
- F1值：0.7210
- 准确率：0.9663
模型描述
需补充更多信息
使用场景与限制
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：3e-05
- 训练批大小：64
- 评估批大小：64
- 随机种子：1
- 优化器：Adam（beta1=0.9，beta2=0.999，epsilon=1e-08）
- 学习率调度器类型：线性
- 训练轮次：20.0
- 混合精度训练：原生AMP
训练结果
| 训练损失 |
轮次 |
步数 |
验证损失 |
精确率 |
召回率 |
F1值 |
准确率 |
| 无记录 |
1.0 |
172 |
0.2310 |
0.1233 |
0.4904 |
0.1971 |
0.8307 |
| 无记录 |
2.0 |
344 |
0.1929 |
0.1983 |
0.5393 |
0.2900 |
0.8765 |
| 0.4324 |
3.0 |
516 |
0.1667 |
0.1773 |
0.4897 |
0.2604 |
0.8738 |
| 0.4324 |
4.0 |
688 |
0.1836 |
0.2957 |
0.6059 |
0.3975 |
0.9081 |
| 0.4324 |
5.0 |
860 |
0.2005 |
0.2855 |
0.5623 |
0.3787 |
0.9137 |
| 0.1106 |
6.0 |
1032 |
0.2003 |
0.3858 |
0.6974 |
0.4968 |
0.9323 |
| 0.1106 |
7.0 |
1204 |
0.2224 |
0.4182 |
0.6719 |
0.5155 |
0.9428 |
| 0.1106 |
8.0 |
1376 |
0.2221 |
0.3347 |
0.6147 |
0.4334 |
0.9312 |
| 0.0589 |
9.0 |
1548 |
0.1960 |
0.4067 |
0.7026 |
0.5152 |
0.9404 |
| 0.0589 |
10.0 |
1720 |
0.1904 |
0.5049 |
0.7410 |
0.6006 |
0.9524 |
| 0.0589 |
11.0 |
1892 |
0.2274 |
0.5337 |
0.7707 |
0.6307 |
0.9565 |
| 0.0359 |
12.0 |
2064 |
0.2471 |
0.5525 |
0.7696 |
0.6432 |
0.9575 |
| 0.0359 |
13.0 |
2236 |
0.2352 |
0.5649 |
0.7675 |
0.6508 |
0.9591 |
| 0.0359 |
14.0 |
2408 |
0.2297 |
0.5530 |
0.7661 |
0.6424 |
0.9586 |
| 0.0224 |
15.0 |
2580 |
0.2349 |
0.5702 |
0.7923 |
0.6632 |
0.9597 |
| 0.0224 |
16.0 |
2752 |
0.2465 |
0.6033 |
0.8052 |
0.6898 |
0.9624 |
| 0.0224 |
17.0 |
2924 |
0.2428 |
0.6100 |
0.8098 |
0.6959 |
0.9647 |
| 0.0143 |
18.0 |
3096 |
0.2543 |
0.6238 |
0.8154 |
0.7068 |
0.9646 |
| 0.0143 |
19.0 |
3268 |
0.2526 |
0.6305 |
0.8161 |
0.7114 |
0.9651 |
| 0.0143 |
20.0 |
3440 |
0.2526 |
0.6406 |
0.8244 |
0.7210 |
0.9663 |
框架版本
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
PyTorch
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)