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predict-perception-bert-focus-assassin

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: 0.2964
  • Rmse: 0.8992
  • Rmse Focus::a Sull'assassino: 0.8992
  • Mae: 0.7331
  • Mae Focus::a Sull'assassino: 0.7331
  • R2: 0.6500
  • R2 Focus::a Sull'assassino: 0.6500
  • Cos: 0.7391
  • Pair: 0.0
  • Rank: 0.5
  • Neighbors: 0.6131
  • 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 Sull'assassino Mae Mae Focus::a Sull'assassino R2 R2 Focus::a Sull'assassino Cos Pair Rank Neighbors Rsa
1.0674 1.0 15 0.9851 1.6393 1.6393 1.5316 1.5316 -0.1633 -0.1633 0.1304 0.0 0.5 0.2457 nan
1.0099 2.0 30 0.8921 1.5601 1.5601 1.4317 1.4317 -0.0535 -0.0535 0.5652 0.0 0.5 0.4734 nan
0.9295 3.0 45 0.7345 1.4155 1.4155 1.3113 1.3113 0.1327 0.1327 0.5652 0.0 0.5 0.3596 nan
0.8485 4.0 60 0.7282 1.4094 1.4094 1.2678 1.2678 0.1401 0.1401 0.7391 0.0 0.5 0.5367 nan
0.7551 5.0 75 0.5966 1.2758 1.2758 1.1144 1.1144 0.2955 0.2955 0.6522 0.0 0.5 0.3911 nan
0.5563 6.0 90 0.4578 1.1175 1.1175 0.9105 0.9105 0.4594 0.4594 0.6522 0.0 0.5 0.3911 nan
0.4048 7.0 105 0.3539 0.9826 0.9826 0.7770 0.7770 0.5821 0.5821 0.6522 0.0 0.5 0.5522 nan
0.3319 8.0 120 0.2938 0.8953 0.8953 0.7110 0.7110 0.6530 0.6530 0.6522 0.0 0.5 0.6021 nan
0.2224 9.0 135 0.3455 0.9708 0.9708 0.7607 0.7607 0.5921 0.5921 0.6522 0.0 0.5 0.3911 nan
0.1794 10.0 150 0.2719 0.8612 0.8612 0.6768 0.6768 0.6790 0.6790 0.7391 0.0 0.5 0.6131 nan
0.1553 11.0 165 0.2855 0.8826 0.8826 0.7053 0.7053 0.6628 0.6628 0.7391 0.0 0.5 0.6131 nan
0.1008 12.0 180 0.3000 0.9046 0.9046 0.7255 0.7255 0.6458 0.6458 0.6522 0.0 0.5 0.5261 nan
0.1121 13.0 195 0.2817 0.8766 0.8766 0.7236 0.7236 0.6674 0.6674 0.7391 0.0 0.5 0.6131 nan
0.08 14.0 210 0.3504 0.9777 0.9777 0.7631 0.7631 0.5863 0.5863 0.7391 0.0 0.5 0.6131 nan
0.0802 15.0 225 0.3031 0.9094 0.9094 0.7565 0.7565 0.6420 0.6420 0.7391 0.0 0.5 0.6131 nan
0.0685 16.0 240 0.3041 0.9109 0.9109 0.7409 0.7409 0.6408 0.6408 0.7391 0.0 0.5 0.6131 nan
0.0592 17.0 255 0.3496 0.9767 0.9767 0.7812 0.7812 0.5871 0.5871 0.7391 0.0 0.5 0.6131 nan
0.0625 18.0 270 0.3260 0.9430 0.9430 0.7757 0.7757 0.6151 0.6151 0.7391 0.0 0.5 0.6131 nan
0.0589 19.0 285 0.3118 0.9222 0.9222 0.7442 0.7442 0.6318 0.6318 0.7391 0.0 0.5 0.6131 nan
0.0518 20.0 300 0.3062 0.9140 0.9140 0.7459 0.7459 0.6384 0.6384 0.7391 0.0 0.5 0.6131 nan
0.0456 21.0 315 0.3200 0.9344 0.9344 0.7592 0.7592 0.6221 0.6221 0.7391 0.0 0.5 0.6131 nan
0.0477 22.0 330 0.3132 0.9244 0.9244 0.7532 0.7532 0.6301 0.6301 0.7391 0.0 0.5 0.6131 nan
0.0448 23.0 345 0.3006 0.9056 0.9056 0.7321 0.7321 0.6450 0.6450 0.6522 0.0 0.5 0.5261 nan
0.0494 24.0 360 0.2985 0.9024 0.9024 0.7463 0.7463 0.6475 0.6475 0.7391 0.0 0.5 0.6131 nan
0.0369 25.0 375 0.3039 0.9105 0.9105 0.7359 0.7359 0.6412 0.6412 0.7391 0.0 0.5 0.6131 nan
0.0456 26.0 390 0.2989 0.9030 0.9030 0.7210 0.7210 0.6471 0.6471 0.7391 0.0 0.5 0.6131 nan
0.044 27.0 405 0.2997 0.9042 0.9042 0.7418 0.7418 0.6461 0.6461 0.7391 0.0 0.5 0.6131 nan
0.0352 28.0 420 0.2970 0.9001 0.9001 0.7346 0.7346 0.6493 0.6493 0.7391 0.0 0.5 0.6131 nan
0.0429 29.0 435 0.2970 0.9001 0.9001 0.7281 0.7281 0.6493 0.6493 0.7391 0.0 0.5 0.6131 nan
0.0378 30.0 450 0.2964 0.8992 0.8992 0.7331 0.7331 0.6500 0.6500 0.7391 0.0 0.5 0.6131 nan

Framework versions

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