<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
predict-perception-bertino-focus-object
This model is a fine-tuned version of indigo-ai/BERTino on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2766
- R2: 0.5460
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: 0.0001
- train_batch_size: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 47
Training results
| Training Loss | Epoch | Step | Validation Loss | R2 |
|---|---|---|---|---|
| 0.4798 | 1.0 | 14 | 0.4519 | 0.2581 |
| 0.2481 | 2.0 | 28 | 0.3042 | 0.5007 |
| 0.12 | 3.0 | 42 | 0.3746 | 0.3851 |
| 0.0969 | 4.0 | 56 | 0.3186 | 0.4770 |
| 0.0907 | 5.0 | 70 | 0.3727 | 0.3882 |
| 0.0673 | 6.0 | 84 | 0.2847 | 0.5327 |
| 0.0457 | 7.0 | 98 | 0.3141 | 0.4844 |
| 0.0431 | 8.0 | 112 | 0.3369 | 0.4470 |
| 0.028 | 9.0 | 126 | 0.3039 | 0.5012 |
| 0.0244 | 10.0 | 140 | 0.2964 | 0.5135 |
| 0.0201 | 11.0 | 154 | 0.3072 | 0.4958 |
| 0.0153 | 12.0 | 168 | 0.3049 | 0.4995 |
| 0.0155 | 13.0 | 182 | 0.2924 | 0.5201 |
| 0.015 | 14.0 | 196 | 0.2585 | 0.5757 |
| 0.0181 | 15.0 | 210 | 0.3258 | 0.4652 |
| 0.0136 | 16.0 | 224 | 0.3142 | 0.4842 |
| 0.0105 | 17.0 | 238 | 0.2536 | 0.5837 |
| 0.0104 | 18.0 | 252 | 0.2407 | 0.6050 |
| 0.0107 | 19.0 | 266 | 0.2727 | 0.5524 |
| 0.0084 | 20.0 | 280 | 0.3117 | 0.4883 |
| 0.0102 | 21.0 | 294 | 0.2999 | 0.5078 |
| 0.0074 | 22.0 | 308 | 0.3018 | 0.5047 |
| 0.0068 | 23.0 | 322 | 0.2826 | 0.5361 |
| 0.0054 | 24.0 | 336 | 0.2804 | 0.5398 |
| 0.0044 | 25.0 | 350 | 0.2912 | 0.5220 |
| 0.0048 | 26.0 | 364 | 0.2813 | 0.5382 |
| 0.005 | 27.0 | 378 | 0.2933 | 0.5186 |
| 0.0046 | 28.0 | 392 | 0.2820 | 0.5371 |
| 0.004 | 29.0 | 406 | 0.2717 | 0.5541 |
| 0.0054 | 30.0 | 420 | 0.2717 | 0.5540 |
| 0.0042 | 31.0 | 434 | 0.2699 | 0.5570 |
| 0.0033 | 32.0 | 448 | 0.2630 | 0.5684 |
| 0.0038 | 33.0 | 462 | 0.2578 | 0.5767 |
| 0.0032 | 34.0 | 476 | 0.2687 | 0.5589 |
| 0.004 | 35.0 | 490 | 0.2737 | 0.5507 |
| 0.0031 | 36.0 | 504 | 0.2753 | 0.5481 |
| 0.0037 | 37.0 | 518 | 0.2819 | 0.5373 |
| 0.0034 | 38.0 | 532 | 0.2759 | 0.5471 |
| 0.0034 | 39.0 | 546 | 0.2835 | 0.5347 |
| 0.0029 | 40.0 | 560 | 0.2814 | 0.5381 |
| 0.0033 | 41.0 | 574 | 0.2801 | 0.5403 |
| 0.0025 | 42.0 | 588 | 0.2759 | 0.5472 |
| 0.0029 | 43.0 | 602 | 0.2790 | 0.5421 |
| 0.0028 | 44.0 | 616 | 0.2801 | 0.5401 |
| 0.003 | 45.0 | 630 | 0.2772 | 0.5451 |
| 0.0028 | 46.0 | 644 | 0.2764 | 0.5463 |
| 0.0026 | 47.0 | 658 | 0.2766 | 0.5460 |
Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
