🚀 distilhubert-finetuned-babycry-v7
該模型是基於ntu-spml/distilhubert在未知數據集上微調得到的版本。它在評估集上取得了以下成績：
- 損失值：0.5864
- 準確率：{'accuracy': 0.8695652173913043}
- F1值：0.8089
- 精確率：0.7561
- 召回率：0.8696
📚 詳細文檔
模型信息
| 屬性 |
詳情 |
| 庫名稱 |
transformers |
| 許可證 |
apache-2.0 |
| 基礎模型 |
ntu-spml/distilhubert |
| 標籤 |
generated_from_trainer |
| 評估指標 |
準確率、F1值、精確率、召回率 |
| 模型名稱 |
distilhubert-finetuned-babycry-v7 |
| 數據集 |
Nooon/Donate_a_cry |
訓練和評估
訓練超參數
訓練過程中使用了以下超參數：
- 學習率：0.001
- 訓練批次大小：4
- 評估批次大小：4
- 隨機種子：42
- 梯度累積步數：2
- 總訓練批次大小：8
- 優化器：Adam（β1=0.9，β2=0.999，ε=1e-08）
- 學習率調度器類型：餘弦退火
- 學習率調度器預熱比例：0.03
- 訓練輪數：8
訓練結果
| 訓練損失 |
輪數 |
步數 |
驗證損失 |
準確率 |
F1值 |
精確率 |
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
📄 許可證
本模型使用的許可證為apache-2.0。
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