Edit model card

predict-perception-bert-blame-concept

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: 0.7359
  • Rmse: 0.6962
  • Rmse Blame::a Un concetto astratto o un'emozione: 0.6962
  • Mae: 0.5010
  • Mae Blame::a Un concetto astratto o un'emozione: 0.5010
  • R2: 0.3974
  • R2 Blame::a Un concetto astratto o un'emozione: 0.3974
  • Cos: 0.3913
  • Pair: 0.0
  • Rank: 0.5
  • Neighbors: 0.5507
  • 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 Un concetto astratto o un'emozione Mae Mae Blame::a Un concetto astratto o un'emozione R2 R2 Blame::a Un concetto astratto o un'emozione Cos Pair Rank Neighbors Rsa
1.0979 1.0 15 1.2387 0.9033 0.9033 0.6603 0.6603 -0.0144 -0.0144 0.0435 0.0 0.5 0.3432 nan
1.0172 2.0 30 1.1498 0.8703 0.8703 0.5964 0.5964 0.0584 0.0584 0.0435 0.0 0.5 0.2935 nan
0.9879 3.0 45 1.2139 0.8942 0.8942 0.6197 0.6197 0.0060 0.0060 0.2174 0.0 0.5 0.4582 nan
0.9723 4.0 60 1.1152 0.8571 0.8571 0.5982 0.5982 0.0867 0.0867 0.2174 0.0 0.5 0.3921 nan
0.9584 5.0 75 1.0607 0.8358 0.8358 0.5959 0.5959 0.1314 0.1314 0.0435 0.0 0.5 0.4165 nan
0.9023 6.0 90 1.0031 0.8128 0.8128 0.5827 0.5827 0.1786 0.1786 -0.0435 0.0 0.5 0.3862 nan
0.8745 7.0 105 0.9715 0.7999 0.7999 0.5796 0.5796 0.2044 0.2044 0.3043 0.0 0.5 0.3665 nan
0.8082 8.0 120 0.8984 0.7692 0.7692 0.5699 0.5699 0.2643 0.2643 0.1304 0.0 0.5 0.3390 nan
0.7475 9.0 135 0.8532 0.7497 0.7497 0.5849 0.5849 0.3013 0.3013 0.0435 0.0 0.5 0.3100 nan
0.6599 10.0 150 0.8737 0.7586 0.7586 0.5822 0.5822 0.2846 0.2846 0.3043 0.0 0.5 0.3830 nan
0.5867 11.0 165 0.8159 0.7331 0.7331 0.5752 0.5752 0.3318 0.3318 0.2174 0.0 0.5 0.4439 nan
0.5081 12.0 180 0.8367 0.7424 0.7424 0.6071 0.6071 0.3148 0.3148 0.0435 0.0 0.5 0.3561 nan
0.4801 13.0 195 0.8353 0.7417 0.7417 0.5567 0.5567 0.3160 0.3160 0.3913 0.0 0.5 0.5850 nan
0.3714 14.0 210 0.8050 0.7282 0.7282 0.5824 0.5824 0.3408 0.3408 0.1304 0.0 0.5 0.3975 nan
0.3306 15.0 225 0.7833 0.7183 0.7183 0.5570 0.5570 0.3585 0.3585 0.2174 0.0 0.5 0.4604 nan
0.2674 16.0 240 0.8148 0.7326 0.7326 0.5475 0.5475 0.3328 0.3328 0.3043 0.0 0.5 0.4891 nan
0.2129 17.0 255 0.8715 0.7576 0.7576 0.5537 0.5537 0.2863 0.2863 0.4783 0.0 0.5 0.5017 nan
0.1924 18.0 270 0.7944 0.7234 0.7234 0.5276 0.5276 0.3495 0.3495 0.4783 0.0 0.5 0.5797 nan
0.1984 19.0 285 0.7885 0.7207 0.7207 0.5208 0.5208 0.3543 0.3543 0.3913 0.0 0.5 0.5507 nan
0.1623 20.0 300 0.7682 0.7113 0.7113 0.5132 0.5132 0.3709 0.3709 0.4783 0.0 0.5 0.5797 nan
0.1409 21.0 315 0.7653 0.7100 0.7100 0.5215 0.5215 0.3733 0.3733 0.3043 0.0 0.5 0.5415 nan
0.1386 22.0 330 0.7688 0.7116 0.7116 0.5124 0.5124 0.3704 0.3704 0.3913 0.0 0.5 0.5507 nan
0.123 23.0 345 0.7756 0.7148 0.7148 0.5144 0.5144 0.3648 0.3648 0.3913 0.0 0.5 0.5507 nan
0.1175 24.0 360 0.7423 0.6993 0.6993 0.5015 0.5015 0.3921 0.3921 0.3913 0.0 0.5 0.5507 nan
0.1188 25.0 375 0.7255 0.6913 0.6913 0.5063 0.5063 0.4059 0.4059 0.2174 0.0 0.5 0.4604 nan
0.1155 26.0 390 0.7635 0.7091 0.7091 0.5083 0.5083 0.3748 0.3748 0.4783 0.0 0.5 0.5797 nan
0.0981 27.0 405 0.7128 0.6852 0.6852 0.5020 0.5020 0.4163 0.4163 0.3043 0.0 0.5 0.5415 nan
0.1109 28.0 420 0.7430 0.6996 0.6996 0.5023 0.5023 0.3915 0.3915 0.3913 0.0 0.5 0.5507 nan
0.1081 29.0 435 0.7367 0.6966 0.6966 0.5007 0.5007 0.3967 0.3967 0.3913 0.0 0.5 0.5507 nan
0.0953 30.0 450 0.7359 0.6962 0.6962 0.5010 0.5010 0.3974 0.3974 0.3913 0.0 0.5 0.5507 nan

Framework versions

  • Transformers 4.16.2
  • Pytorch 1.10.2+cu113
  • Datasets 1.18.3
  • Tokenizers 0.11.0
Downloads last month
365
Hosted inference API
Text Classification
Examples
Examples
This model can be loaded on the Inference API on-demand.