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predict-perception-bert-blame-victim

This model is a fine-tuned version of dbmdz/bert-base-italian-xxl-cased on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5075
  • Rmse: 0.4599
  • Rmse Blame::a La vittima: 0.4599
  • Mae: 0.3607
  • Mae Blame::a La vittima: 0.3607
  • R2: -0.1848
  • R2 Blame::a La vittima: -0.1848
  • Cos: 0.2174
  • Pair: 0.0
  • Rank: 0.5
  • Neighbors: 0.2924
  • Rsa: nan

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: 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: 30

Training results

Training Loss Epoch Step Validation Loss Rmse Rmse Blame::a La vittima Mae Mae Blame::a La vittima R2 R2 Blame::a La vittima Cos Pair Rank Neighbors Rsa
1.0264 1.0 15 0.4334 0.4250 0.4250 0.3666 0.3666 -0.0119 -0.0119 0.1304 0.0 0.5 0.2703 nan
0.9814 2.0 30 0.4505 0.4333 0.4333 0.3744 0.3744 -0.0517 -0.0517 0.2174 0.0 0.5 0.2751 nan
0.9283 3.0 45 0.4349 0.4257 0.4257 0.3627 0.3627 -0.0152 -0.0152 0.1304 0.0 0.5 0.2779 nan
0.8904 4.0 60 0.4662 0.4408 0.4408 0.3773 0.3773 -0.0884 -0.0884 -0.0435 0.0 0.5 0.2681 nan
0.836 5.0 75 0.4188 0.4177 0.4177 0.3609 0.3609 0.0223 0.0223 0.2174 0.0 0.5 0.3051 nan
0.8293 6.0 90 0.4142 0.4155 0.4155 0.3512 0.3512 0.0330 0.0330 0.2174 0.0 0.5 0.3220 nan
0.7629 7.0 105 0.3837 0.3999 0.3999 0.3387 0.3387 0.1041 0.1041 0.2174 0.0 0.5 0.3051 nan
0.7266 8.0 120 0.3664 0.3907 0.3907 0.3250 0.3250 0.1446 0.1446 0.3043 0.0 0.5 0.3409 nan
0.6121 9.0 135 0.3718 0.3936 0.3936 0.3312 0.3312 0.1320 0.1320 0.3043 0.0 0.5 0.3983 nan
0.5694 10.0 150 0.3679 0.3915 0.3915 0.3197 0.3197 0.1411 0.1411 0.3913 0.0 0.5 0.3518 nan
0.4647 11.0 165 0.3868 0.4015 0.4015 0.3340 0.3340 0.0970 0.0970 0.2174 0.0 0.5 0.3285 nan
0.4212 12.0 180 0.3717 0.3936 0.3936 0.3188 0.3188 0.1322 0.1322 0.3913 0.0 0.5 0.3518 nan
0.3605 13.0 195 0.3437 0.3784 0.3784 0.3066 0.3066 0.1976 0.1976 0.3043 0.0 0.5 0.3423 nan
0.2759 14.0 210 0.3892 0.4027 0.4027 0.3230 0.3230 0.0914 0.0914 0.3913 0.0 0.5 0.3518 nan
0.2868 15.0 225 0.3720 0.3937 0.3937 0.3218 0.3218 0.1315 0.1315 0.3913 0.0 0.5 0.3440 nan
0.2467 16.0 240 0.3881 0.4022 0.4022 0.3291 0.3291 0.0939 0.0939 0.3043 0.0 0.5 0.3363 nan
0.2013 17.0 255 0.4121 0.4144 0.4144 0.3373 0.3373 0.0380 0.0380 0.3043 0.0 0.5 0.3363 nan
0.1966 18.0 270 0.4808 0.4476 0.4476 0.3506 0.3506 -0.1224 -0.1224 0.3913 0.0 0.5 0.3214 nan
0.177 19.0 285 0.4263 0.4215 0.4215 0.3398 0.3398 0.0046 0.0046 0.2174 0.0 0.5 0.2924 nan
0.1589 20.0 300 0.4274 0.4220 0.4220 0.3363 0.3363 0.0022 0.0022 0.2174 0.0 0.5 0.2924 nan
0.1488 21.0 315 0.4548 0.4353 0.4353 0.3431 0.3431 -0.0618 -0.0618 0.3043 0.0 0.5 0.2924 nan
0.1428 22.0 330 0.4405 0.4285 0.4285 0.3417 0.3417 -0.0285 -0.0285 0.3043 0.0 0.5 0.3363 nan
0.1294 23.0 345 0.4955 0.4544 0.4544 0.3565 0.3565 -0.1568 -0.1568 0.3913 0.0 0.5 0.3440 nan
0.1291 24.0 360 0.4861 0.4501 0.4501 0.3529 0.3529 -0.1348 -0.1348 0.2174 0.0 0.5 0.2924 nan
0.1187 25.0 375 0.4752 0.4450 0.4450 0.3518 0.3518 -0.1095 -0.1095 0.2174 0.0 0.5 0.2924 nan
0.1141 26.0 390 0.5131 0.4624 0.4624 0.3598 0.3598 -0.1978 -0.1978 0.3043 0.0 0.5 0.2924 nan
0.1094 27.0 405 0.4863 0.4502 0.4502 0.3547 0.3547 -0.1353 -0.1353 0.2174 0.0 0.5 0.2924 nan
0.0925 28.0 420 0.4900 0.4519 0.4519 0.3564 0.3564 -0.1439 -0.1439 0.2174 0.0 0.5 0.2924 nan
0.108 29.0 435 0.5019 0.4573 0.4573 0.3590 0.3590 -0.1719 -0.1719 0.2174 0.0 0.5 0.2924 nan
0.1054 30.0 450 0.5075 0.4599 0.4599 0.3607 0.3607 -0.1848 -0.1848 0.2174 0.0 0.5 0.2924 nan

Framework versions

  • Transformers 4.16.2
  • Pytorch 1.10.2+cu113
  • Datasets 1.18.3
  • Tokenizers 0.11.0
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