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cahmetcan
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06-12-14-46

Text Classification
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bert
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06-12-14-46

This model is a fine-tuned version of dbmdz/bert-base-turkish-cased on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0870
  • Hate Precision: 1.0
  • Hate Recall: 0.6923
  • Hate F1: 0.8182
  • Sexual Precision: 0.0
  • Sexual Recall: 0.0
  • Sexual F1: 0.0
  • Threat Precision: 0.0
  • Threat Recall: 0.0
  • Threat F1: 0.0
  • Illicit Precision: 0.0
  • Illicit Recall: 0.0
  • Illicit F1: 0.0
  • Bad Habits Precision: 0.0
  • Bad Habits Recall: 0.0
  • Bad Habits F1: 0.0
  • Self Harm Precision: 0.0
  • Self Harm Recall: 0.0
  • Self Harm F1: 0.0
  • Neutral Precision: 0.0
  • Neutral Recall: 0.0
  • Neutral F1: 0.0
  • Macro Precision: 0.1429
  • Macro Recall: 0.0989
  • Macro F1: 0.1169

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: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Hate Precision Hate Recall Hate F1 Sexual Precision Sexual Recall Sexual F1 Threat Precision Threat Recall Threat F1 Illicit Precision Illicit Recall Illicit F1 Bad Habits Precision Bad Habits Recall Bad Habits F1 Self Harm Precision Self Harm Recall Self Harm F1 Neutral Precision Neutral Recall Neutral F1 Macro Precision Macro Recall Macro F1
0.563 0.8333 10 0.4105 0.375 0.2308 0.2857 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0536 0.0330 0.0408
0.3308 1.6667 20 0.2523 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.2122 2.5 30 0.1615 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.178 3.3333 40 0.1422 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.1487 4.1667 50 0.1318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.1676 5.0 60 0.1248 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.1434 5.8333 70 0.1204 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.1341 6.6667 80 0.1157 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.1451 7.5 90 0.1146 0.6429 0.6923 0.6667 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0918 0.0989 0.0952
0.1052 8.3333 100 0.1055 0.8 0.6154 0.6957 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1143 0.0879 0.0994
0.1167 9.1667 110 0.1078 0.6429 0.6923 0.6667 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0918 0.0989 0.0952
0.0813 10.0 120 0.0964 0.8182 0.6923 0.75 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1169 0.0989 0.1071
0.0799 10.8333 130 0.0995 0.75 0.6923 0.72 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1071 0.0989 0.1029
0.0717 11.6667 140 0.0933 0.8182 0.6923 0.75 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1169 0.0989 0.1071
0.0639 12.5 150 0.0939 0.9 0.6923 0.7826 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1286 0.0989 0.1118
0.0612 13.3333 160 0.0911 0.9 0.6923 0.7826 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1286 0.0989 0.1118
0.0571 14.1667 170 0.0920 0.9 0.6923 0.7826 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1286 0.0989 0.1118
0.0492 15.0 180 0.0904 1.0 0.6923 0.8182 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1429 0.0989 0.1169
0.05 15.8333 190 0.0906 0.9 0.6923 0.7826 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1286 0.0989 0.1118
0.0481 16.6667 200 0.0876 1.0 0.6923 0.8182 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1429 0.0989 0.1169
0.0327 17.5 210 0.0881 0.9 0.6923 0.7826 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1286 0.0989 0.1118
0.0514 18.3333 220 0.0918 0.9 0.6923 0.7826 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1286 0.0989 0.1118
0.0397 19.1667 230 0.0877 1.0 0.6923 0.8182 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1429 0.0989 0.1169
0.0429 20.0 240 0.0895 0.9 0.6923 0.7826 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1286 0.0989 0.1118
0.0391 20.8333 250 0.0876 1.0 0.6923 0.8182 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1429 0.0989 0.1169
0.0351 21.6667 260 0.0874 1.0 0.6923 0.8182 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1429 0.0989 0.1169
0.0325 22.5 270 0.0879 1.0 0.6923 0.8182 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1429 0.0989 0.1169
0.0368 23.3333 280 0.0895 0.9 0.6923 0.7826 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1286 0.0989 0.1118
0.0305 24.1667 290 0.0869 1.0 0.6923 0.8182 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1429 0.0989 0.1169
0.0335 25.0 300 0.0875 1.0 0.6923 0.8182 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1429 0.0989 0.1169
0.0328 25.8333 310 0.0879 1.0 0.6923 0.8182 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1429 0.0989 0.1169
0.0352 26.6667 320 0.0886 1.0 0.6923 0.8182 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1429 0.0989 0.1169
0.0314 27.5 330 0.0871 1.0 0.6923 0.8182 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1429 0.0989 0.1169
0.0337 28.3333 340 0.0869 1.0 0.6923 0.8182 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1429 0.0989 0.1169
0.0314 29.1667 350 0.0870 1.0 0.6923 0.8182 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1429 0.0989 0.1169
0.0278 30.0 360 0.0870 1.0 0.6923 0.8182 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1429 0.0989 0.1169

Framework versions

  • Transformers 4.47.0
  • Pytorch 2.5.1+cu124
  • Datasets 3.1.0
  • Tokenizers 0.21.0
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