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predict-perception-xlmr-focus-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: 0.8296
  • Rmse: 1.0302
  • Rmse Focus::a Su un concetto astratto o un'emozione: 1.0302
  • Mae: 0.7515
  • Mae Focus::a Su un concetto astratto o un'emozione: 0.7515
  • R2: 0.1804
  • R2 Focus::a Su un concetto astratto o un'emozione: 0.1804
  • Cos: 0.4783
  • Pair: 0.0
  • Rank: 0.5
  • Neighbors: 0.3415
  • 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 Su un concetto astratto o un'emozione Mae Mae Focus::a Su un concetto astratto o un'emozione R2 R2 Focus::a Su un concetto astratto o un'emozione Cos Pair Rank Neighbors Rsa
1.0355 1.0 15 0.9822 1.1209 1.1209 0.9649 0.9649 0.0296 0.0296 0.2174 0.0 0.5 0.3706 nan
1.0083 2.0 30 1.1378 1.2065 1.2065 0.9954 0.9954 -0.1241 -0.1241 0.2174 0.0 0.5 0.3309 nan
0.9823 3.0 45 0.9669 1.1121 1.1121 0.9315 0.9315 0.0448 0.0448 0.3043 0.0 0.5 0.3810 nan
0.9468 4.0 60 0.8856 1.0644 1.0644 0.8584 0.8584 0.1251 0.1251 0.3913 0.0 0.5 0.3803 nan
0.9294 5.0 75 0.8136 1.0202 1.0202 0.8396 0.8396 0.1963 0.1963 0.6522 0.0 0.5 0.4727 nan
0.881 6.0 90 0.7634 0.9882 0.9882 0.8192 0.8192 0.2458 0.2458 0.6522 0.0 0.5 0.4727 nan
0.7589 7.0 105 0.8139 1.0204 1.0204 0.8136 0.8136 0.1960 0.1960 0.5652 0.0 0.5 0.4120 nan
0.7217 8.0 120 0.9105 1.0792 1.0792 0.9394 0.9394 0.1005 0.1005 0.3913 0.0 0.5 0.4108 nan
0.8059 9.0 135 1.0322 1.1491 1.1491 0.9115 0.9115 -0.0197 -0.0197 0.5652 0.0 0.5 0.3738 nan
0.6483 10.0 150 0.7989 1.0109 1.0109 0.7899 0.7899 0.2108 0.2108 0.6522 0.0 0.5 0.4727 nan
0.5725 11.0 165 0.7175 0.9581 0.9581 0.7011 0.7011 0.2912 0.2912 0.5652 0.0 0.5 0.3738 nan
0.5091 12.0 180 0.8818 1.0621 1.0621 0.8775 0.8775 0.1289 0.1289 0.5652 0.0 0.5 0.4063 nan
0.4526 13.0 195 0.8451 1.0398 1.0398 0.7990 0.7990 0.1651 0.1651 0.5652 0.0 0.5 0.4063 nan
0.361 14.0 210 0.8632 1.0508 1.0508 0.8124 0.8124 0.1472 0.1472 0.4783 0.0 0.5 0.3699 nan
0.3582 15.0 225 0.8461 1.0404 1.0404 0.7923 0.7923 0.1641 0.1641 0.3913 0.0 0.5 0.3672 nan
0.2945 16.0 240 0.9142 1.0814 1.0814 0.8125 0.8125 0.0968 0.0968 0.3913 0.0 0.5 0.3672 nan
0.2891 17.0 255 0.8377 1.0352 1.0352 0.7718 0.7718 0.1724 0.1724 0.4783 0.0 0.5 0.3415 nan
0.2569 18.0 270 0.8106 1.0183 1.0183 0.7481 0.7481 0.1992 0.1992 0.4783 0.0 0.5 0.3415 nan
0.2583 19.0 285 0.8239 1.0266 1.0266 0.7597 0.7597 0.1861 0.1861 0.4783 0.0 0.5 0.3415 nan
0.2217 20.0 300 0.8485 1.0419 1.0419 0.7663 0.7663 0.1617 0.1617 0.4783 0.0 0.5 0.3415 nan
0.1927 21.0 315 0.8304 1.0307 1.0307 0.7536 0.7536 0.1797 0.1797 0.4783 0.0 0.5 0.3415 nan
0.176 22.0 330 0.8321 1.0317 1.0317 0.7539 0.7539 0.1780 0.1780 0.4783 0.0 0.5 0.3415 nan
0.1639 23.0 345 0.7914 1.0062 1.0062 0.7460 0.7460 0.2182 0.2182 0.4783 0.0 0.5 0.3415 nan
0.177 24.0 360 0.8619 1.0500 1.0500 0.7725 0.7725 0.1486 0.1486 0.4783 0.0 0.5 0.3415 nan
0.1473 25.0 375 0.8101 1.0180 1.0180 0.7587 0.7587 0.1997 0.1997 0.4783 0.0 0.5 0.3415 nan
0.181 26.0 390 0.8038 1.0141 1.0141 0.7433 0.7433 0.2059 0.2059 0.4783 0.0 0.5 0.3415 nan
0.1679 27.0 405 0.7982 1.0105 1.0105 0.7248 0.7248 0.2115 0.2115 0.4783 0.0 0.5 0.3415 nan
0.1529 28.0 420 0.8282 1.0293 1.0293 0.7454 0.7454 0.1818 0.1818 0.4783 0.0 0.5 0.3415 nan
0.1822 29.0 435 0.8310 1.0311 1.0311 0.7512 0.7512 0.1790 0.1790 0.4783 0.0 0.5 0.3415 nan
0.1442 30.0 450 0.8296 1.0302 1.0302 0.7515 0.7515 0.1804 0.1804 0.4783 0.0 0.5 0.3415 nan

Framework versions

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