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predict-perception-xlmr-cause-object

This model is a fine-tuned version of xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3069
  • Rmse: 0.8927
  • Rmse Cause::a Causata da un oggetto (es. una pistola): 0.8927
  • Mae: 0.5854
  • Mae Cause::a Causata da un oggetto (es. una pistola): 0.5854
  • R2: 0.5410
  • R2 Cause::a Causata da un oggetto (es. una pistola): 0.5410
  • Cos: 0.4783
  • Pair: 0.0
  • Rank: 0.5
  • Neighbors: 0.6177
  • 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 Cause::a Causata da un oggetto (es. una pistola) Mae Mae Cause::a Causata da un oggetto (es. una pistola) R2 R2 Cause::a Causata da un oggetto (es. una pistola) Cos Pair Rank Neighbors Rsa
1.0329 1.0 15 0.8168 1.4564 1.4564 1.2947 1.2947 -0.2216 -0.2216 -0.5652 0.0 0.5 0.5993 nan
1.0096 2.0 30 0.7432 1.3893 1.3893 1.1883 1.1883 -0.1116 -0.1116 -0.3913 0.0 0.5 0.6499 nan
0.9323 3.0 45 0.6879 1.3366 1.3366 1.1054 1.1054 -0.0289 -0.0289 -0.1304 0.0 0.5 0.5471 nan
0.8636 4.0 60 0.6378 1.2870 1.2870 1.0477 1.0477 0.0461 0.0461 0.2174 0.0 0.5 0.3007 nan
0.8041 5.0 75 0.5494 1.1945 1.1945 0.9499 0.9499 0.1783 0.1783 0.6522 0.0 0.5 0.6695 nan
0.7413 6.0 90 0.5526 1.1980 1.1980 0.9503 0.9503 0.1735 0.1735 0.5652 0.0 0.5 0.3898 nan
0.6397 7.0 105 0.4726 1.1078 1.1078 0.7826 0.7826 0.2932 0.2932 0.5652 0.0 0.5 0.3257 nan
0.5556 8.0 120 0.7728 1.4167 1.4167 1.1528 1.1528 -0.1558 -0.1558 0.1304 0.0 0.5 0.4027 nan
0.4972 9.0 135 0.4375 1.0659 1.0659 0.7577 0.7577 0.3457 0.3457 0.5652 0.0 0.5 0.5683 nan
0.3691 10.0 150 0.4990 1.1383 1.1383 0.8272 0.8272 0.2537 0.2537 0.4783 0.0 0.5 0.4781 nan
0.3381 11.0 165 0.4401 1.0690 1.0690 0.7319 0.7319 0.3418 0.3418 0.5652 0.0 0.5 0.5683 nan
0.2966 12.0 180 0.4794 1.1158 1.1158 0.7835 0.7835 0.2830 0.2830 0.5652 0.0 0.5 0.5683 nan
0.2324 13.0 195 0.4013 1.0208 1.0208 0.6873 0.6873 0.3998 0.3998 0.4783 0.0 0.5 0.5796 nan
0.1848 14.0 210 0.4305 1.0574 1.0574 0.7372 0.7372 0.3561 0.3561 0.4783 0.0 0.5 0.5796 nan
0.1621 15.0 225 0.3652 0.9738 0.9738 0.6164 0.6164 0.4538 0.4538 0.4783 0.0 0.5 0.6177 nan
0.1762 16.0 240 0.3335 0.9307 0.9307 0.6458 0.6458 0.5012 0.5012 0.4783 0.0 0.5 0.5796 nan
0.1404 17.0 255 0.3420 0.9424 0.9424 0.6599 0.6599 0.4886 0.4886 0.3913 0.0 0.5 0.5831 nan
0.1379 18.0 270 0.2853 0.8608 0.8608 0.6063 0.6063 0.5733 0.5733 0.3913 0.0 0.5 0.5831 nan
0.1322 19.0 285 0.3261 0.9203 0.9203 0.6548 0.6548 0.5123 0.5123 0.4783 0.0 0.5 0.5796 nan
0.1067 20.0 300 0.3328 0.9296 0.9296 0.5535 0.5535 0.5023 0.5023 0.6522 0.0 0.5 0.6695 nan
0.1038 21.0 315 0.3066 0.8924 0.8924 0.6266 0.6266 0.5414 0.5414 0.4783 0.0 0.5 0.5796 nan
0.094 22.0 330 0.2924 0.8714 0.8714 0.5792 0.5792 0.5626 0.5626 0.4783 0.0 0.5 0.6177 nan
0.1078 23.0 345 0.3161 0.9060 0.9060 0.6022 0.6022 0.5272 0.5272 0.3913 0.0 0.5 0.5831 nan
0.0976 24.0 360 0.3118 0.8998 0.8998 0.6011 0.6011 0.5337 0.5337 0.3913 0.0 0.5 0.5831 nan
0.0911 25.0 375 0.3123 0.9005 0.9005 0.5811 0.5811 0.5330 0.5330 0.4783 0.0 0.5 0.6177 nan
0.1039 26.0 390 0.3122 0.9005 0.9005 0.5956 0.5956 0.5330 0.5330 0.4783 0.0 0.5 0.6177 nan
0.0775 27.0 405 0.3191 0.9103 0.9103 0.6124 0.6124 0.5228 0.5228 0.3913 0.0 0.5 0.5831 nan
0.0789 28.0 420 0.3135 0.9023 0.9023 0.5825 0.5825 0.5311 0.5311 0.4783 0.0 0.5 0.6177 nan
0.0778 29.0 435 0.3075 0.8936 0.8936 0.5837 0.5837 0.5401 0.5401 0.4783 0.0 0.5 0.6177 nan
0.082 30.0 450 0.3069 0.8927 0.8927 0.5854 0.5854 0.5410 0.5410 0.4783 0.0 0.5 0.6177 nan

Framework versions

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