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estimation-model-v3

PEFT
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metadata
library_name: peft
license: gemma
base_model: google/gemma-2-2b-jpn-it
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - spearmanr
  - pearsonr
model-index:
  - name: estimation-model-v3
    results: []

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