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bert-10
This model is a fine-tuned version of deepset/bert-base-cased-squad2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 9.5797
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-07
- 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 10.8556 | 0.05 | 5 | 12.3235 |
| 10.8413 | 0.09 | 10 | 12.2591 |
| 11.0649 | 0.14 | 15 | 12.1778 |
| 11.6408 | 0.18 | 20 | 12.0989 |
| 11.3732 | 0.23 | 25 | 12.0213 |
| 10.5122 | 0.28 | 30 | 11.9458 |
| 10.6594 | 0.32 | 35 | 11.8691 |
| 10.745 | 0.37 | 40 | 11.7928 |
| 10.8256 | 0.41 | 45 | 11.7163 |
| 10.1627 | 0.46 | 50 | 11.6430 |
| 10.9907 | 0.5 | 55 | 11.5703 |
| 10.1394 | 0.55 | 60 | 11.4997 |
| 9.6059 | 0.6 | 65 | 11.4287 |
| 9.4972 | 0.64 | 70 | 11.3621 |
| 10.2252 | 0.69 | 75 | 11.2949 |
| 10.4887 | 0.73 | 80 | 11.2288 |
| 9.9616 | 0.78 | 85 | 11.1638 |
| 9.5775 | 0.83 | 90 | 11.1003 |
| 9.5971 | 0.87 | 95 | 11.0381 |
| 9.5745 | 0.92 | 100 | 10.9773 |
| 9.3218 | 0.96 | 105 | 10.9178 |
| 9.4906 | 1.01 | 110 | 10.8597 |
| 9.1168 | 1.06 | 115 | 10.8030 |
| 9.8009 | 1.1 | 120 | 10.7465 |
| 9.3632 | 1.15 | 125 | 10.6915 |
| 8.9858 | 1.19 | 130 | 10.6399 |
| 9.2904 | 1.24 | 135 | 10.5874 |
| 9.5344 | 1.28 | 140 | 10.5370 |
| 9.0034 | 1.33 | 145 | 10.4871 |
| 9.3024 | 1.38 | 150 | 10.4384 |
| 8.7905 | 1.42 | 155 | 10.3920 |
| 8.9329 | 1.47 | 160 | 10.3465 |
| 8.9834 | 1.51 | 165 | 10.3027 |
| 8.7307 | 1.56 | 170 | 10.2607 |
| 8.6729 | 1.61 | 175 | 10.2200 |
| 9.1849 | 1.65 | 180 | 10.1794 |
| 9.1618 | 1.7 | 185 | 10.1400 |
| 8.9048 | 1.74 | 190 | 10.1023 |
| 8.9427 | 1.79 | 195 | 10.0655 |
| 9.1052 | 1.83 | 200 | 10.0294 |
| 9.1123 | 1.88 | 205 | 9.9938 |
| 9.0476 | 1.93 | 210 | 9.9604 |
| 8.5532 | 1.97 | 215 | 9.9285 |
| 8.7871 | 2.02 | 220 | 9.8977 |
| 8.5984 | 2.06 | 225 | 9.8690 |
| 8.7009 | 2.11 | 230 | 9.8414 |
| 8.9376 | 2.16 | 235 | 9.8146 |
| 8.3535 | 2.2 | 240 | 9.7906 |
| 8.5805 | 2.25 | 245 | 9.7675 |
| 8.4641 | 2.29 | 250 | 9.7463 |
| 8.3975 | 2.34 | 255 | 9.7263 |
| 8.7698 | 2.39 | 260 | 9.7070 |
| 8.3541 | 2.43 | 265 | 9.6901 |
| 8.5443 | 2.48 | 270 | 9.6743 |
| 8.1539 | 2.52 | 275 | 9.6595 |
| 7.9856 | 2.57 | 280 | 9.6459 |
| 8.2532 | 2.61 | 285 | 9.6333 |
| 8.2116 | 2.66 | 290 | 9.6221 |
| 8.9557 | 2.71 | 295 | 9.6119 |
| 8.0754 | 2.75 | 300 | 9.6032 |
| 7.9534 | 2.8 | 305 | 9.5956 |
| 8.5578 | 2.84 | 310 | 9.5899 |
| 8.6403 | 2.89 | 315 | 9.5848 |
| 8.1103 | 2.94 | 320 | 9.5817 |
| 8.3785 | 2.98 | 325 | 9.5797 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
