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predict-perception-bert-focus-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: 0.2466
  • Rmse: 0.6201
  • Rmse Focus::a Sulla vittima: 0.6201
  • Mae: 0.4936
  • Mae Focus::a Sulla vittima: 0.4936
  • R2: 0.7293
  • R2 Focus::a Sulla vittima: 0.7293
  • Cos: 0.8261
  • Pair: 0.0
  • Rank: 0.5
  • Neighbors: 0.8155
  • 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 Focus::a Sulla vittima Mae Mae Focus::a Sulla vittima R2 R2 Focus::a Sulla vittima Cos Pair Rank Neighbors Rsa
1.0247 1.0 15 1.0286 1.2665 1.2665 1.0280 1.0280 -0.1292 -0.1292 0.1304 0.0 0.5 0.3685 nan
0.9912 2.0 30 1.0039 1.2512 1.2512 1.0347 1.0347 -0.1020 -0.1020 0.0435 0.0 0.5 0.3333 nan
0.9147 3.0 45 0.9338 1.2067 1.2067 0.9770 0.9770 -0.0251 -0.0251 0.1304 0.0 0.5 0.3685 nan
0.8194 4.0 60 0.7641 1.0916 1.0916 0.8476 0.8476 0.1612 0.1612 0.4783 0.0 0.5 0.5284 nan
0.6636 5.0 75 0.6618 1.0159 1.0159 0.8012 0.8012 0.2735 0.2735 0.6522 0.0 0.5 0.4741 nan
0.523 6.0 90 0.5176 0.8984 0.8984 0.7044 0.7044 0.4318 0.4318 0.6522 0.0 0.5 0.4741 nan
0.402 7.0 105 0.3804 0.7702 0.7702 0.6042 0.6042 0.5824 0.5824 0.6522 0.0 0.5 0.5395 nan
0.3401 8.0 120 0.3594 0.7487 0.7487 0.5703 0.5703 0.6054 0.6054 0.7391 0.0 0.5 0.6920 nan
0.2615 9.0 135 0.3429 0.7312 0.7312 0.6049 0.6049 0.6236 0.6236 0.7391 0.0 0.5 0.6920 nan
0.1928 10.0 150 0.2889 0.6712 0.6712 0.5487 0.5487 0.6828 0.6828 0.7391 0.0 0.5 0.6920 nan
0.1703 11.0 165 0.2675 0.6458 0.6458 0.5188 0.5188 0.7064 0.7064 0.7391 0.0 0.5 0.6920 nan
0.1209 12.0 180 0.2826 0.6639 0.6639 0.5475 0.5475 0.6897 0.6897 0.7391 0.0 0.5 0.6920 nan
0.1428 13.0 195 0.2978 0.6815 0.6815 0.5777 0.5777 0.6731 0.6731 0.7391 0.0 0.5 0.6920 nan
0.1038 14.0 210 0.2924 0.6753 0.6753 0.5865 0.5865 0.6790 0.6790 0.6522 0.0 0.5 0.2760 nan
0.0951 15.0 225 0.2905 0.6731 0.6731 0.5750 0.5750 0.6811 0.6811 0.7391 0.0 0.5 0.6920 nan
0.0809 16.0 240 0.2676 0.6460 0.6460 0.5552 0.5552 0.7062 0.7062 0.7391 0.0 0.5 0.6920 nan
0.0811 17.0 255 0.2770 0.6572 0.6572 0.5543 0.5543 0.6959 0.6959 0.7391 0.0 0.5 0.6920 nan
0.0703 18.0 270 0.2634 0.6409 0.6409 0.5251 0.5251 0.7108 0.7108 0.8261 0.0 0.5 0.8155 nan
0.0595 19.0 285 0.2638 0.6413 0.6413 0.5196 0.5196 0.7104 0.7104 0.8261 0.0 0.5 0.8155 nan
0.0651 20.0 300 0.2520 0.6268 0.6268 0.4970 0.4970 0.7234 0.7234 0.8261 0.0 0.5 0.8155 nan
0.0637 21.0 315 0.2668 0.6451 0.6451 0.4965 0.4965 0.7071 0.7071 0.8261 0.0 0.5 0.8155 nan
0.0582 22.0 330 0.2455 0.6188 0.6188 0.4759 0.4759 0.7305 0.7305 0.8261 0.0 0.5 0.8155 nan
0.0616 23.0 345 0.2509 0.6255 0.6255 0.5084 0.5084 0.7246 0.7246 0.8261 0.0 0.5 0.8155 nan
0.0492 24.0 360 0.2510 0.6256 0.6256 0.4985 0.4985 0.7244 0.7244 0.8261 0.0 0.5 0.8155 nan
0.0504 25.0 375 0.2512 0.6259 0.6259 0.4849 0.4849 0.7242 0.7242 0.8261 0.0 0.5 0.8155 nan
0.0501 26.0 390 0.2585 0.6350 0.6350 0.5140 0.5140 0.7162 0.7162 0.8261 0.0 0.5 0.8155 nan
0.0411 27.0 405 0.2544 0.6299 0.6299 0.5148 0.5148 0.7207 0.7207 0.8261 0.0 0.5 0.8155 nan
0.044 28.0 420 0.2466 0.6201 0.6201 0.4964 0.4964 0.7293 0.7293 0.8261 0.0 0.5 0.8155 nan
0.042 29.0 435 0.2466 0.6201 0.6201 0.4836 0.4836 0.7293 0.7293 0.8261 0.0 0.5 0.8155 nan
0.0446 30.0 450 0.2466 0.6201 0.6201 0.4936 0.4936 0.7293 0.7293 0.8261 0.0 0.5 0.8155 nan

Framework versions

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