标签:
- 从训练器自动生成
指标:
- 精确率
- 召回率
- F1值
- 准确率
模型索引:
- 名称: convnext-tiny-224_finetuned_on_unlabelled_IA_with_snorkel_labels
结果: []
convnext-tiny-224_finetuned_on_unlabelled_IA_with_snorkel_labels
该模型是基于未知数据集微调的版本。在评估集上取得了以下结果:
- 损失值: 0.4381
- 精确率: 0.8239
- 召回率: 0.7919
- F1值: 0.8058
- 准确率: 0.8629
模型描述
需要提供更多信息
预期用途与限制
需要提供更多信息
训练与评估数据
需要提供更多信息
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: 0.001
- 训练批次大小: 256
- 评估批次大小: 256
- 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
- 训练轮数: 30
- 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 |
112 |
0.5589 |
0.7547 |
0.5380 |
0.5097 |
0.7679 |
| 无记录 |
2.0 |
224 |
0.5578 |
0.7691 |
0.5387 |
0.5103 |
0.7690 |
| 无记录 |
3.0 |
336 |
0.4812 |
0.8513 |
0.7371 |
0.7709 |
0.8555 |
| 无记录 |
4.0 |
448 |
0.4387 |
0.8734 |
0.6539 |
0.6835 |
0.8259 |
| 0.482 |
5.0 |
560 |
0.4427 |
0.8322 |
0.6250 |
0.6449 |
0.8085 |
| 0.482 |
6.0 |
672 |
0.6234 |
0.8219 |
0.5702 |
0.5635 |
0.7848 |
| 0.482 |
7.0 |
784 |
0.6187 |
0.8791 |
0.6070 |
0.6196 |
0.8054 |
| 0.482 |
8.0 |
896 |
0.3953 |
0.8683 |
0.7134 |
0.7507 |
0.8502 |
| 0.3656 |
9.0 |
1008 |
0.4381 |
0.8239 |
0.7919 |
0.8058 |
0.8629 |
| 0.3656 |
10.0 |
1120 |
0.5346 |
0.7794 |
0.7900 |
0.7844 |
0.8370 |
| 0.3656 |
11.0 |
1232 |
0.3685 |
0.8678 |
0.7600 |
0.7943 |
0.8681 |
| 0.3656 |
12.0 |
1344 |
0.6900 |
0.6244 |
0.6667 |
0.6099 |
0.6435 |
| 0.3656 |
13.0 |
1456 |
0.6097 |
0.6832 |
0.7149 |
0.6931 |
0.7511 |
| 0.2987 |
14.0 |
1568 |
0.5435 |
0.8746 |
0.6754 |
0.7096 |
0.8354 |
| 0.2987 |
15.0 |
1680 |
0.5525 |
0.7277 |
0.7690 |
0.7411 |
0.7890 |
| 0.2987 |
16.0 |
1792 |
0.5003 |
0.8086 |
0.7694 |
0.7856 |
0.8507 |
| 0.2987 |
17.0 |
1904 |
0.8172 |
0.6183 |
0.6576 |
0.6074 |
0.6450 |
| 0.2598 |
18.0 |
2016 |
0.6102 |
0.6977 |
0.7489 |
0.7070 |
0.75 |
| 0.2598 |
19.0 |
2128 |
0.4260 |
0.8523 |
0.7497 |
0.7822 |
0.8602 |
| 0.2598 |
20.0 |
2240 |
0.5503 |
0.8276 |
0.6770 |
0.7079 |
0.8281 |
| 0.2598 |
21.0 |
2352 |
0.4574 |
0.7994 |
0.7785 |
0.7879 |
0.8481 |
| 0.2598 |
22.0 |
2464 |
0.6307 |
0.8620 |
0.6353 |
0.6592 |
0.8165 |
| 0.2111 |
23.0 |
2576 |
0.4605 |
0.8196 |
0.7697 |
0.7894 |
0.8555 |
| 0.2111 |
24.0 |
2688 |
0.5290 |
0.8152 |
0.7320 |
0.7592 |
0.8434 |
| 0.2111 |
25.0 |
2800 |
0.4754 |
0.8755 |
0.7216 |
0.7599 |
0.8550 |
| 0.2111 |
26.0 |
2912 |
0.5161 |
0.8428 |
0.7436 |
0.7750 |
0.8555 |
| 0.1638 |
27.0 |
3024 |
0.5753 |
0.7358 |
0.7278 |
0.7316 |
0.8043 |
| 0.1638 |
28.0 |
3136 |
0.6403 |
0.8468 |
0.7016 |
0.7360 |
0.8412 |
| 0.1638 |
29.0 |
3248 |
0.5418 |
0.7912 |
0.7473 |
0.7647 |
0.8381 |
| 0.1638 |
30.0 |
3360 |
0.5651 |
0.8240 |
0.7315 |
0.7607 |
0.8460 |
框架版本
- Transformers 4.23.1
- PyTorch 1.12.1+cu113
- Datasets 2.6.0
- Tokenizers 0.13.1
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