license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: Log-Analysis-Model-DistilBert
results: []
日志分析模型-DistilBert
该模型是基于distilbert-base-uncased在未知数据集上微调的版本。在评估集上取得了如下结果：
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
训练过程中使用了以下超参数：
- 学习率：5e-05
- 训练批次大小：4
- 评估批次大小：4
- 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：5
训练结果
| 训练损失 |
训练轮次 |
步数 |
验证损失 |
| 0.0736 |
0.0982 |
500 |
0.0615 |
| 0.0561 |
0.1964 |
1000 |
0.0625 |
| 0.0547 |
0.2946 |
1500 |
0.0549 |
| 0.0655 |
0.3929 |
2000 |
0.0593 |
| 0.0605 |
0.4911 |
2500 |
0.0541 |
| 0.0739 |
0.5893 |
3000 |
0.0547 |
| 0.0474 |
0.6875 |
3500 |
0.0629 |
| 0.051 |
0.7857 |
4000 |
0.0563 |
| 0.0758 |
0.8839 |
4500 |
0.0607 |
| 0.0676 |
0.9821 |
5000 |
0.0509 |
| 0.0645 |
1.0803 |
5500 |
0.0564 |
| 0.0531 |
1.1786 |
6000 |
0.0561 |
| 0.0409 |
1.2768 |
6500 |
0.0596 |
| 0.0297 |
1.3750 |
7000 |
0.0703 |
| 0.058 |
1.4732 |
7500 |
0.0613 |
| 0.0486 |
1.5714 |
8000 |
0.0532 |
| 0.0459 |
1.6696 |
8500 |
0.0599 |
| 0.0846 |
1.7678 |
9000 |
0.0583 |
| 0.0586 |
1.8660 |
9500 |
0.0560 |
| 0.099 |
1.9643 |
10000 |
0.0503 |
| 0.0576 |
2.0625 |
10500 |
0.0573 |
| 0.049 |
2.1607 |
11000 |
0.0505 |
| 0.0489 |
2.2589 |
11500 |
0.0490 |
| 0.0611 |
2.3571 |
12000 |
0.0494 |
| 0.056 |
2.4553 |
12500 |
0.0476 |
| 0.03 |
2.5535 |
13000 |
0.0540 |
| 0.0536 |
2.6517 |
13500 |
0.0478 |
| 0.0752 |
2.7500 |
14000 |
0.0521 |
| 0.0476 |
2.8482 |
14500 |
0.0590 |
| 0.0402 |
2.9464 |
15000 |
0.0601 |
| 0.041 |
3.0446 |
15500 |
0.0520 |
| 0.053 |
3.1428 |
16000 |
0.0480 |
| 0.0315 |
3.2410 |
16500 |
0.0494 |
| 0.0326 |
3.3392 |
17000 |
0.0511 |
| 0.044 |
3.4374 |
17500 |
0.0520 |
| 0.0681 |
3.5357 |
18000 |
0.0467 |
| 0.0406 |
3.6339 |
18500 |
0.0479 |
| 0.0505 |
3.7321 |
19000 |
0.0480 |
| 0.0539 |
3.8303 |
19500 |
0.0453 |
| 0.025 |
3.9285 |
20000 |
0.0504 |
| 0.0598 |
4.0267 |
20500 |
0.0477 |
| 0.039 |
4.1249 |
21000 |
0.0498 |
| 0.0474 |
4.2231 |
21500 |
0.0494 |
| 0.037 |
4.3214 |
22000 |
0.0489 |
| 0.0303 |
4.4196 |
22500 |
0.0503 |
| 0.0545 |
4.5178 |
23000 |
0.0485 |
| 0.0466 |
4.6160 |
23500 |
0.0484 |
| 0.0461 |
4.7142 |
24000 |
0.0478 |
| 0.0478 |
4.8124 |
24500 |
0.0478 |
| 0.0473 |
4.9106 |
25000 |
0.0477 |
框架版本
- Transformers 4.40.2
- Pytorch 2.3.0+cpu
- Datasets 2.19.1
- Tokenizers 0.19.1
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