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predict-perception-bertino-cause-none

This model is a fine-tuned version of indigo-ai/BERTino on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1988
  • R2: 0.4467

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: 0.0001
  • 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: 47

Training results

Training Loss Epoch Step Validation Loss R2
0.56 1.0 14 0.3460 0.0372
0.3752 2.0 28 0.3082 0.1423
0.147 3.0 42 0.2299 0.3603
0.0961 4.0 56 0.3254 0.0944
0.0859 5.0 70 0.2650 0.2625
0.0735 6.0 84 0.2430 0.3237
0.042 7.0 98 0.2567 0.2856
0.0328 8.0 112 0.2092 0.4180
0.028 9.0 126 0.2262 0.3706
0.0237 10.0 140 0.2170 0.3960
0.0235 11.0 154 0.2137 0.4054
0.0195 12.0 168 0.2009 0.4409
0.0217 13.0 182 0.2001 0.4431
0.0176 14.0 196 0.2123 0.4091
0.0226 15.0 210 0.2076 0.4224
0.019 16.0 224 0.1920 0.4657
0.0122 17.0 238 0.2301 0.3598
0.0121 18.0 252 0.2092 0.4178
0.0112 19.0 266 0.2038 0.4329
0.0081 20.0 280 0.2008 0.4411
0.0079 21.0 294 0.1930 0.4631
0.0083 22.0 308 0.2076 0.4222
0.0061 23.0 322 0.2036 0.4334
0.0057 24.0 336 0.1986 0.4472
0.0059 25.0 350 0.2079 0.4215
0.0082 26.0 364 0.2125 0.4087
0.0093 27.0 378 0.2096 0.4168
0.0061 28.0 392 0.2129 0.4076
0.005 29.0 406 0.2054 0.4284
0.0058 30.0 420 0.2024 0.4368
0.006 31.0 434 0.1999 0.4437
0.0047 32.0 448 0.1917 0.4666
0.0046 33.0 462 0.2000 0.4435
0.005 34.0 476 0.2003 0.4425
0.0041 35.0 490 0.2057 0.4276
0.0037 36.0 504 0.1985 0.4476
0.0049 37.0 518 0.2029 0.4353
0.0031 38.0 532 0.1963 0.4539
0.0031 39.0 546 0.1957 0.4554
0.0031 40.0 560 0.1962 0.4540
0.0029 41.0 574 0.2000 0.4433
0.0028 42.0 588 0.1986 0.4473
0.0035 43.0 602 0.1972 0.4514
0.0029 44.0 616 0.1984 0.4479
0.0036 45.0 630 0.2005 0.4422
0.0033 46.0 644 0.1994 0.4452
0.0029 47.0 658 0.1988 0.4467

Framework versions

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