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predict-perception-bert-focus-object

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: 0.2271
  • Rmse: 0.5965
  • Rmse Focus::a Su un oggetto: 0.5965
  • Mae: 0.4372
  • Mae Focus::a Su un oggetto: 0.4372
  • R2: 0.4957
  • R2 Focus::a Su un oggetto: 0.4957
  • Cos: 0.6522
  • Pair: 0.0
  • Rank: 0.5
  • Neighbors: 0.6622
  • 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 oggetto Mae Mae Focus::a Su un oggetto R2 R2 Focus::a Su un oggetto Cos Pair Rank Neighbors Rsa
1.0371 1.0 15 0.4358 0.8263 0.8263 0.7132 0.7132 0.0323 0.0323 0.3043 0.0 0.5 0.3510 nan
0.9574 2.0 30 0.4420 0.8321 0.8321 0.7175 0.7175 0.0186 0.0186 0.3043 0.0 0.5 0.4627 nan
0.9137 3.0 45 0.4208 0.8119 0.8119 0.6955 0.6955 0.0657 0.0657 0.3913 0.0 0.5 0.3928 nan
0.8465 4.0 60 0.3356 0.7251 0.7251 0.6237 0.6237 0.2548 0.2548 0.5652 0.0 0.5 0.6247 nan
0.6864 5.0 75 0.2876 0.6712 0.6712 0.5624 0.5624 0.3616 0.3616 0.5652 0.0 0.5 0.6247 nan
0.5804 6.0 90 0.3148 0.7022 0.7022 0.5577 0.5577 0.3011 0.3011 0.5652 0.0 0.5 0.6247 nan
0.4983 7.0 105 0.4068 0.7983 0.7983 0.6606 0.6606 0.0968 0.0968 0.3913 0.0 0.5 0.4519 nan
0.3584 8.0 120 0.2567 0.6342 0.6342 0.4883 0.4883 0.4300 0.4300 0.5652 0.0 0.5 0.6247 nan
0.2771 9.0 135 0.2130 0.5777 0.5777 0.4193 0.4193 0.5270 0.5270 0.6522 0.0 0.5 0.6622 nan
0.2135 10.0 150 0.2522 0.6285 0.6285 0.4572 0.4572 0.4401 0.4401 0.6522 0.0 0.5 0.6622 nan
0.1654 11.0 165 0.2662 0.6457 0.6457 0.4603 0.4603 0.4090 0.4090 0.6522 0.0 0.5 0.6622 nan
0.1554 12.0 180 0.2459 0.6207 0.6207 0.4778 0.4778 0.4540 0.4540 0.6522 0.0 0.5 0.6622 nan
0.1195 13.0 195 0.2385 0.6113 0.6113 0.4618 0.4618 0.4704 0.4704 0.5652 0.0 0.5 0.5693 nan
0.1046 14.0 210 0.2296 0.5997 0.5997 0.4544 0.4544 0.4903 0.4903 0.6522 0.0 0.5 0.6622 nan
0.089 15.0 225 0.2520 0.6283 0.6283 0.4974 0.4974 0.4404 0.4404 0.6522 0.0 0.5 0.6622 nan
0.083 16.0 240 0.2297 0.5998 0.5998 0.4635 0.4635 0.4901 0.4901 0.5652 0.0 0.5 0.5610 nan
0.0701 17.0 255 0.2207 0.5879 0.5879 0.4442 0.4442 0.5101 0.5101 0.6522 0.0 0.5 0.6622 nan
0.0585 18.0 270 0.2397 0.6128 0.6128 0.4617 0.4617 0.4678 0.4678 0.6522 0.0 0.5 0.6622 nan
0.0652 19.0 285 0.2284 0.5981 0.5981 0.4449 0.4449 0.4929 0.4929 0.6522 0.0 0.5 0.6622 nan
0.059 20.0 300 0.2491 0.6247 0.6247 0.4599 0.4599 0.4469 0.4469 0.6522 0.0 0.5 0.6622 nan
0.0464 21.0 315 0.2306 0.6010 0.6010 0.4373 0.4373 0.4880 0.4880 0.6522 0.0 0.5 0.6622 nan
0.0529 22.0 330 0.2370 0.6093 0.6093 0.4480 0.4480 0.4738 0.4738 0.6522 0.0 0.5 0.6622 nan
0.0555 23.0 345 0.2361 0.6082 0.6082 0.4474 0.4474 0.4757 0.4757 0.6522 0.0 0.5 0.6622 nan
0.0447 24.0 360 0.2283 0.5980 0.5980 0.4399 0.4399 0.4932 0.4932 0.6522 0.0 0.5 0.6622 nan
0.046 25.0 375 0.2259 0.5948 0.5948 0.4413 0.4413 0.4985 0.4985 0.6522 0.0 0.5 0.6622 nan
0.0379 26.0 390 0.2263 0.5953 0.5953 0.4402 0.4402 0.4977 0.4977 0.6522 0.0 0.5 0.6622 nan
0.0438 27.0 405 0.2270 0.5963 0.5963 0.4378 0.4378 0.4961 0.4961 0.6522 0.0 0.5 0.6622 nan
0.0354 28.0 420 0.2211 0.5886 0.5886 0.4379 0.4379 0.5090 0.5090 0.6522 0.0 0.5 0.6622 nan
0.0363 29.0 435 0.2269 0.5962 0.5962 0.4362 0.4362 0.4961 0.4961 0.6522 0.0 0.5 0.6622 nan
0.0451 30.0 450 0.2271 0.5965 0.5965 0.4372 0.4372 0.4957 0.4957 0.6522 0.0 0.5 0.6622 nan

Framework versions

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