Orderliness_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: 0.9065
- Accuracy: 0.6463
- Precision: 0.6275
- Recall: 0.7182
- F1: 0.6698
- Auc: 0.6949
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 | 0.6405 | 0.6413 | 0.6119 | 0.7706 | 0.6821 | 0.6834 |
| No log | 2.0 | 236 | 0.6950 | 0.5890 | 0.5515 | 0.9476 | 0.6972 | 0.7132 |
| No log | 3.0 | 354 | 0.7146 | 0.6164 | 0.5737 | 0.9027 | 0.7016 | 0.7210 |
| No log | 4.0 | 472 | 0.7390 | 0.6563 | 0.6356 | 0.7307 | 0.6798 | 0.7132 |
| 0.548 | 5.0 | 590 | 0.9065 | 0.6463 | 0.6275 | 0.7182 | 0.6698 | 0.6949 |
Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1