Self_Discipline_binary
This model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.1799
- Accuracy: 0.6301
- Precision: 0.6012
- Recall: 0.7706
- F1: 0.6754
- Auc: 0.6748
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 1234
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Auc |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 118 | 0.6715 | 0.5679 | 0.5413 | 0.8828 | 0.6711 | 0.6604 |
| No log | 2.0 | 236 | 0.6719 | 0.6314 | 0.5939 | 0.8279 | 0.6917 | 0.6914 |
| No log | 3.0 | 354 | 0.6720 | 0.6451 | 0.6107 | 0.7980 | 0.6919 | 0.7059 |
| No log | 4.0 | 472 | 0.7635 | 0.6376 | 0.6007 | 0.8180 | 0.6927 | 0.6999 |
| 0.5205 | 5.0 | 590 | 0.9848 | 0.6301 | 0.6061 | 0.7406 | 0.6667 | 0.6793 |
| 0.5205 | 6.0 | 708 | 1.1799 | 0.6301 | 0.6012 | 0.7706 | 0.6754 | 0.6748 |
Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1