🚀 distilhubert-finetuned-babycry-v7
该模型是基于ntu-spml/distilhubert在未知数据集上微调得到的版本。它在评估集上取得了以下成绩：
- 损失值：0.5864
- 准确率：{'accuracy': 0.8695652173913043}
- F1值：0.8089
- 精确率：0.7561
- 召回率：0.8696
📚 详细文档
模型信息
| 属性 |
详情 |
| 库名称 |
transformers |
| 许可证 |
apache-2.0 |
| 基础模型 |
ntu-spml/distilhubert |
| 标签 |
generated_from_trainer |
| 评估指标 |
准确率、F1值、精确率、召回率 |
| 模型名称 |
distilhubert-finetuned-babycry-v7 |
| 数据集 |
Nooon/Donate_a_cry |
训练和评估
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：4
- 评估批次大小：4
- 随机种子：42
- 梯度累积步数：2
- 总训练批次大小：8
- 优化器：Adam（β1=0.9，β2=0.999，ε=1e-08）
- 学习率调度器类型：余弦退火
- 学习率调度器预热比例：0.03
- 训练轮数：8
训练结果
| 训练损失 |
轮数 |
步数 |
验证损失 |
准确率 |
F1值 |
精确率 |
召回率 |
| 0.7417 |
0.5435 |
25 |
0.5925 |
{'accuracy': 0.8695652173913043} |
0.8089 |
0.7561 |
0.8696 |
| 0.7226 |
1.0870 |
50 |
0.6167 |
{'accuracy': 0.8695652173913043} |
0.8089 |
0.7561 |
0.8696 |
| 0.5606 |
1.6304 |
75 |
0.6808 |
{'accuracy': 0.8695652173913043} |
0.8089 |
0.7561 |
0.8696 |
| 0.8858 |
2.1739 |
100 |
0.5850 |
{'accuracy': 0.8695652173913043} |
0.8089 |
0.7561 |
0.8696 |
| 0.6573 |
2.7174 |
125 |
0.5968 |
{'accuracy': 0.8695652173913043} |
0.8089 |
0.7561 |
0.8696 |
| 0.7942 |
3.2609 |
150 |
0.6142 |
{'accuracy': 0.8695652173913043} |
0.8089 |
0.7561 |
0.8696 |
| 0.7497 |
3.8043 |
175 |
0.5915 |
{'accuracy': 0.8695652173913043} |
0.8089 |
0.7561 |
0.8696 |
| 0.7408 |
4.3478 |
200 |
0.5899 |
{'accuracy': 0.8695652173913043} |
0.8089 |
0.7561 |
0.8696 |
| 0.6499 |
4.8913 |
225 |
0.5989 |
{'accuracy': 0.8695652173913043} |
0.8089 |
0.7561 |
0.8696 |
| 0.6725 |
5.4348 |
250 |
0.5865 |
{'accuracy': 0.8695652173913043} |
0.8089 |
0.7561 |
0.8696 |
| 0.6797 |
5.9783 |
275 |
0.5852 |
{'accuracy': 0.8695652173913043} |
0.8089 |
0.7561 |
0.8696 |
| 0.6553 |
6.5217 |
300 |
0.5861 |
{'accuracy': 0.8695652173913043} |
0.8089 |
0.7561 |
0.8696 |
| 0.6535 |
7.0652 |
325 |
0.5863 |
{'accuracy': 0.8695652173913043} |
0.8089 |
0.7561 |
0.8696 |
| 0.7297 |
7.6087 |
350 |
0.5865 |
{'accuracy': 0.8695652173913043} |
0.8089 |
0.7561 |
0.8696 |
框架版本
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Tokenizers 0.19.1
📄 许可证
本模型使用的许可证为apache-2.0。
PyTorch
Transformers 支持多种语言%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)