🚀 xtreme_s_xlsr_minds14
该模型是在None数据集上对facebook/wav2vec2-xls-r-300m进行微调后的版本。它在评估集上取得了如下结果：
- 损失值：0.2566
- F1值：{'f1': 0.9460569664921582, 'accuracy': 0.9468540012217471}
🚀 快速开始
此模型是基于预训练模型facebook/wav2vec2-xls-r-300m在特定数据集上微调得到，可用于相关语音任务。
📚 详细文档
模型描述
该模型是对facebook/wav2vec2-xls-r-300m的微调版本，但更多详细信息待补充。
预期用途与限制
更多相关信息待补充。
训练和评估数据
更多相关信息待补充。
🔧 技术细节
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：0.0003
- 训练批次大小：32
- 评估批次大小：8
- 随机种子：42
- 分布式类型：多GPU
- 设备数量：2
- 总训练批次大小：64
- 总评估批次大小：16
- 优化器：Adam（β1 = 0.9，β2 = 0.999，ε = 1e-08）
- 学习率调度器类型：线性
- 学习率调度器热身步数：1500
- 训练轮数：50.0
- 混合精度训练：Native AMP
训练结果
| 训练损失 |
轮数 |
步数 |
验证损失 |
F1 |
| 2.551 |
2.7 |
200 |
2.5921 |
{'f1': 0.03454307545755678, 'accuracy': 0.1148442272449603} |
| 1.6934 |
5.41 |
400 |
1.5353 |
{'f1': 0.5831241711045994, 'accuracy': 0.6053756872327428} |
| 0.5914 |
8.11 |
600 |
0.7337 |
{'f1': 0.7990425247664236, 'accuracy': 0.7947464874770922} |
| 0.3896 |
10.81 |
800 |
0.5076 |
{'f1': 0.8738199236080776, 'accuracy': 0.872327428222358} |
| 0.5052 |
13.51 |
1000 |
0.4917 |
{'f1': 0.8744760456867134, 'accuracy': 0.8747709224190593} |
| 0.4806 |
16.22 |
1200 |
0.4751 |
{'f1': 0.8840798740258787, 'accuracy': 0.8845448992058644} |
| 0.2103 |
18.92 |
1400 |
0.5228 |
{'f1': 0.8721632556623751, 'accuracy': 0.8729383017715333} |
| 0.4198 |
21.62 |
1600 |
0.5910 |
{'f1': 0.8755207264572983, 'accuracy': 0.8766035430665852} |
| 0.11 |
24.32 |
1800 |
0.4464 |
{'f1': 0.896423086249818, 'accuracy': 0.8955406230910201} |
| 0.1233 |
27.03 |
2000 |
0.3760 |
{'f1': 0.9012283567348968, 'accuracy': 0.9016493585827734} |
| 0.1827 |
29.73 |
2200 |
0.4178 |
{'f1': 0.9042381720184095, 'accuracy': 0.9059254734270006} |
| 0.1235 |
32.43 |
2400 |
0.4152 |
{'f1': 0.9063257163259107, 'accuracy': 0.9071472205253512} |
| 0.1873 |
35.14 |
2600 |
0.2903 |
{'f1': 0.9369340598806323, 'accuracy': 0.9376908979841173} |
| 0.017 |
37.84 |
2800 |
0.3046 |
{'f1': 0.9300781160576355, 'accuracy': 0.9303604153940135} |
| 0.0436 |
40.54 |
3000 |
0.3111 |
{'f1': 0.9315034391389341, 'accuracy': 0.9321930360415394} |
| 0.0455 |
43.24 |
3200 |
0.2748 |
{'f1': 0.9417365311433034, 'accuracy': 0.9425778863775198} |
| 0.046 |
45.95 |
3400 |
0.2800 |
{'f1': 0.9390712658440112, 'accuracy': 0.9395235186316433} |
| 0.0042 |
48.65 |
3600 |
0.2566 |
{'f1': 0.9460569664921582, 'accuracy': 0.9468540012217471} |
框架版本
- Transformers 4.18.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4.dev0
- Tokenizers 0.11.6
📄 许可证
本项目采用Apache-2.0许可证。
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