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predict-perception-xlmr-cause-concept

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: 0.3933
  • Rmse: 0.5992
  • Rmse Cause::a Causata da un concetto astratto (es. gelosia): 0.5992
  • Mae: 0.4566
  • Mae Cause::a Causata da un concetto astratto (es. gelosia): 0.4566
  • R2: 0.5588
  • R2 Cause::a Causata da un concetto astratto (es. gelosia): 0.5588
  • Cos: 0.3043
  • Pair: 0.0
  • Rank: 0.5
  • Neighbors: 0.4340
  • 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 concetto astratto (es. gelosia) Mae Mae Cause::a Causata da un concetto astratto (es. gelosia) R2 R2 Cause::a Causata da un concetto astratto (es. gelosia) Cos Pair Rank Neighbors Rsa
1.0114 1.0 15 0.9088 0.9109 0.9109 0.6455 0.6455 -0.0195 -0.0195 -0.0435 0.0 0.5 0.4027 nan
1.0 2.0 30 0.8833 0.8980 0.8980 0.6104 0.6104 0.0090 0.0090 0.2174 0.0 0.5 0.3681 nan
0.9533 3.0 45 0.8453 0.8785 0.8785 0.6072 0.6072 0.0517 0.0517 0.1304 0.0 0.5 0.3748 nan
0.9113 4.0 60 0.7797 0.8437 0.8437 0.6024 0.6024 0.1253 0.1253 0.0435 0.0 0.5 0.3028 nan
0.8312 5.0 75 0.5756 0.7249 0.7249 0.5128 0.5128 0.3542 0.3542 0.4783 0.0 0.5 0.4572 nan
0.7224 6.0 90 0.4977 0.6741 0.6741 0.5114 0.5114 0.4416 0.4416 0.2174 0.0 0.5 0.4009 nan
0.5789 7.0 105 0.6338 0.7607 0.7607 0.5059 0.5059 0.2889 0.2889 0.3043 0.0 0.5 0.4340 nan
0.4978 8.0 120 0.3342 0.5524 0.5524 0.4298 0.4298 0.6250 0.6250 0.2174 0.0 0.5 0.4274 nan
0.4572 9.0 135 0.3210 0.5413 0.5413 0.4343 0.4343 0.6399 0.6399 0.3043 0.0 0.5 0.4340 nan
0.3346 10.0 150 0.3456 0.5617 0.5617 0.4198 0.4198 0.6123 0.6123 0.3043 0.0 0.5 0.4340 nan
0.3046 11.0 165 0.3840 0.5921 0.5921 0.4312 0.4312 0.5692 0.5692 0.3043 0.0 0.5 0.4340 nan
0.3035 12.0 180 0.3929 0.5989 0.5989 0.4147 0.4147 0.5592 0.5592 0.3043 0.0 0.5 0.4340 nan
0.2199 13.0 195 0.3165 0.5376 0.5376 0.4065 0.4065 0.6449 0.6449 0.3043 0.0 0.5 0.4340 nan
0.2376 14.0 210 0.3108 0.5326 0.5326 0.3937 0.3937 0.6514 0.6514 0.3913 0.0 0.5 0.4286 nan
0.1639 15.0 225 0.3645 0.5769 0.5769 0.4094 0.4094 0.5911 0.5911 0.3913 0.0 0.5 0.4286 nan
0.1884 16.0 240 0.3762 0.5860 0.5860 0.4398 0.4398 0.5779 0.5779 0.3043 0.0 0.5 0.4340 nan
0.1767 17.0 255 0.3805 0.5894 0.5894 0.4540 0.4540 0.5732 0.5732 0.2174 0.0 0.5 0.4298 nan
0.1329 18.0 270 0.3555 0.5697 0.5697 0.4281 0.4281 0.6011 0.6011 0.2174 0.0 0.5 0.4298 nan
0.1834 19.0 285 0.4337 0.6292 0.6292 0.4402 0.4402 0.5135 0.5135 0.3913 0.0 0.5 0.4286 nan
0.1538 20.0 300 0.3554 0.5696 0.5696 0.4236 0.4236 0.6013 0.6013 0.3043 0.0 0.5 0.4340 nan
0.1459 21.0 315 0.3592 0.5726 0.5726 0.4348 0.4348 0.5971 0.5971 0.3043 0.0 0.5 0.4066 nan
0.1038 22.0 330 0.3732 0.5837 0.5837 0.4382 0.4382 0.5813 0.5813 0.3913 0.0 0.5 0.4664 nan
0.1432 23.0 345 0.3635 0.5760 0.5760 0.4394 0.4394 0.5922 0.5922 0.3913 0.0 0.5 0.4664 nan
0.1354 24.0 360 0.4359 0.6308 0.6308 0.4793 0.4793 0.5110 0.5110 0.3043 0.0 0.5 0.4340 nan
0.1404 25.0 375 0.3919 0.5982 0.5982 0.4650 0.4650 0.5603 0.5603 0.3913 0.0 0.5 0.4664 nan
0.103 26.0 390 0.4223 0.6209 0.6209 0.4691 0.4691 0.5263 0.5263 0.3043 0.0 0.5 0.4340 nan
0.1733 27.0 405 0.3972 0.6021 0.6021 0.4591 0.4591 0.5544 0.5544 0.3043 0.0 0.5 0.4340 nan
0.1019 28.0 420 0.3958 0.6011 0.6011 0.4593 0.4593 0.5559 0.5559 0.3043 0.0 0.5 0.4340 nan
0.1076 29.0 435 0.4015 0.6054 0.6054 0.4589 0.4589 0.5496 0.5496 0.3043 0.0 0.5 0.4340 nan
0.0999 30.0 450 0.3933 0.5992 0.5992 0.4566 0.4566 0.5588 0.5588 0.3043 0.0 0.5 0.4340 nan

Framework versions

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