estimation-model-v3
This model is a fine-tuned version of google/gemma-2-2b-jpn-it on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0232
- Accuracy: 0.5739
- Spearmanr: 0.3397
- Kendalltau: 0.2651
- Pearsonr: 0.3174
- Rmse: 1.4493
- Mae: 1.0966
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine_with_min_lr
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
Training results
| Training Loss |
Epoch |
Step |
Validation Loss |
Accuracy |
Spearmanr |
Kendalltau |
Pearsonr |
Rmse |
Mae |
| 5.4448 |
0.1567 |
500 |
5.2447 |
0.3697 |
0.1294 |
0.0999 |
0.1030 |
1.5116 |
1.1748 |
| 3.5747 |
0.3135 |
1000 |
3.6366 |
0.4647 |
0.1303 |
0.1009 |
0.0898 |
1.6046 |
1.2388 |
| 3.2813 |
0.4702 |
1500 |
3.0585 |
0.4625 |
0.1429 |
0.1108 |
0.1173 |
1.5283 |
1.1646 |
| 3.1132 |
0.6270 |
2000 |
2.5819 |
0.5048 |
0.1707 |
0.1324 |
0.1416 |
1.5356 |
1.1687 |
| 2.5897 |
0.7837 |
2500 |
2.3855 |
0.5182 |
0.1987 |
0.1541 |
0.1669 |
1.5235 |
1.1543 |
| 2.0304 |
0.9404 |
3000 |
2.2636 |
0.5293 |
0.2220 |
0.1729 |
0.1969 |
1.4558 |
1.0804 |
| 1.9453 |
1.0972 |
3500 |
2.2381 |
0.5338 |
0.2413 |
0.1876 |
0.2098 |
1.5274 |
1.1662 |
| 2.4994 |
1.2539 |
4000 |
2.1722 |
0.5419 |
0.2640 |
0.2057 |
0.2346 |
1.4880 |
1.1202 |
| 2.3851 |
1.4107 |
4500 |
2.1235 |
0.5419 |
0.2764 |
0.2164 |
0.2521 |
1.4205 |
1.0493 |
| 2.1885 |
1.5674 |
5000 |
2.0991 |
0.5464 |
0.2864 |
0.2241 |
0.2689 |
1.4017 |
1.0326 |
| 1.8545 |
1.7241 |
5500 |
2.0855 |
0.5486 |
0.3038 |
0.2368 |
0.2769 |
1.4451 |
1.0769 |
| 1.9475 |
1.8809 |
6000 |
2.0571 |
0.5627 |
0.3133 |
0.2445 |
0.2914 |
1.4352 |
1.0740 |
| 1.5089 |
2.0376 |
6500 |
2.0469 |
0.5635 |
0.3228 |
0.2519 |
0.3009 |
1.4361 |
1.0791 |
| 2.0828 |
2.1944 |
7000 |
2.0393 |
0.5687 |
0.3290 |
0.2568 |
0.3054 |
1.4403 |
1.0836 |
| 1.7599 |
2.3511 |
7500 |
2.0405 |
0.5679 |
0.3300 |
0.2575 |
0.3058 |
1.4577 |
1.1024 |
| 2.1807 |
2.5078 |
8000 |
2.0301 |
0.5679 |
0.3332 |
0.2601 |
0.3104 |
1.4349 |
1.0783 |
| 1.9166 |
2.6646 |
8500 |
2.0201 |
0.5642 |
0.3360 |
0.2626 |
0.3177 |
1.4096 |
1.0529 |
| 2.1982 |
2.8213 |
9000 |
2.0223 |
0.5709 |
0.3384 |
0.2640 |
0.3171 |
1.4399 |
1.0868 |
| 2.1137 |
2.9781 |
9500 |
2.0232 |
0.5739 |
0.3397 |
0.2651 |
0.3174 |
1.4493 |
1.0966 |
Framework versions
- PEFT 0.15.0
- Transformers 4.49.0
- Pytorch 2.4.1+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
