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mydataset-repo
This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the mydataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.0000
- Total-str: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}
- Total-val: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}
- Overall Precision: 1.0
- Overall Recall: 1.0
- Overall F1: 1.0
- Overall Accuracy: 1.0
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Total-str | Total-val | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|
| 0.0674 | 28.57 | 200 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 57.14 | 400 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 85.71 | 600 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 114.29 | 800 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 142.86 | 1000 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 171.43 | 1200 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 200.0 | 1400 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 228.57 | 1600 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 257.14 | 1800 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 285.71 | 2000 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 314.29 | 2200 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 342.86 | 2400 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 371.43 | 2600 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 400.0 | 2800 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 428.57 | 3000 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 457.14 | 3200 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 485.71 | 3400 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 514.29 | 3600 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 542.86 | 3800 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 571.43 | 4000 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 600.0 | 4200 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 628.57 | 4400 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 657.14 | 4600 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 685.71 | 4800 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 714.29 | 5000 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | 1.0 | 1.0 | 1.0 | 1.0 |
Framework versions
- Transformers 4.29.2
- Pytorch 1.13.1
- Datasets 2.12.0
- Tokenizers 0.13.2
