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bert-base-Daichi_support
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.7348
- F1: 0.5408
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| No log | 1.0 | 7 | 1.7976 | 0.3806 |
| No log | 2.0 | 14 | 1.6849 | 0.3806 |
| No log | 3.0 | 21 | 1.5963 | 0.3806 |
| No log | 4.0 | 28 | 1.4947 | 0.3806 |
| No log | 5.0 | 35 | 1.4645 | 0.3806 |
| No log | 6.0 | 42 | 1.4063 | 0.3806 |
| No log | 7.0 | 49 | 1.4314 | 0.4935 |
| No log | 8.0 | 56 | 1.2979 | 0.5274 |
| No log | 9.0 | 63 | 1.3582 | 0.4626 |
| No log | 10.0 | 70 | 1.5711 | 0.5164 |
| No log | 11.0 | 77 | 1.2483 | 0.5881 |
| No log | 12.0 | 84 | 1.1974 | 0.5860 |
| No log | 13.0 | 91 | 1.2582 | 0.5426 |
| No log | 14.0 | 98 | 1.7688 | 0.4504 |
| No log | 15.0 | 105 | 1.3278 | 0.5557 |
| No log | 16.0 | 112 | 1.6230 | 0.5119 |
| No log | 17.0 | 119 | 1.4229 | 0.5536 |
| No log | 18.0 | 126 | 1.4000 | 0.5536 |
| No log | 19.0 | 133 | 1.4614 | 0.5408 |
| No log | 20.0 | 140 | 1.4676 | 0.5536 |
| No log | 21.0 | 147 | 1.7174 | 0.555 |
| No log | 22.0 | 154 | 1.5338 | 0.5536 |
| No log | 23.0 | 161 | 1.6979 | 0.6179 |
| No log | 24.0 | 168 | 1.7075 | 0.5408 |
| No log | 25.0 | 175 | 1.6655 | 0.5408 |
| No log | 26.0 | 182 | 1.6043 | 0.6179 |
| No log | 27.0 | 189 | 1.6945 | 0.6051 |
| No log | 28.0 | 196 | 1.7289 | 0.5408 |
| 1.1079 | 29.0 | 203 | 1.7329 | 0.5408 |
| 1.1079 | 30.0 | 210 | 1.7348 | 0.5408 |
Framework versions
- Transformers 4.27.3
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2
