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Grietas_10k-Fine-tuning

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metadata
license: apache-2.0
base_model: thezeivier/Grietas_10k
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: Grietas_10k-Fine-tuning
    results: []

Grietas_10k-Fine-tuning

This model is a fine-tuned version of thezeivier/Grietas_10k on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3864
  • Accuracy: 0.8860

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: 5e-05
  • train_batch_size: 80
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 320
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 100

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.8 2 1.3737 0.3679
No log 2.0 5 1.0234 0.6218
No log 2.8 7 0.8146 0.7254
1.0488 4.0 10 0.6621 0.7772
1.0488 4.8 12 0.6295 0.8031
1.0488 6.0 15 0.5390 0.8083
1.0488 6.8 17 0.4902 0.8290
0.4981 8.0 20 0.4645 0.8290
0.4981 8.8 22 0.4484 0.8497
0.4981 10.0 25 0.4543 0.8446
0.4981 10.8 27 0.4325 0.8394
0.3669 12.0 30 0.4210 0.8497
0.3669 12.8 32 0.4303 0.8342
0.3669 14.0 35 0.4170 0.8497
0.3669 14.8 37 0.3861 0.8601
0.2811 16.0 40 0.3629 0.8705
0.2811 16.8 42 0.3982 0.8653
0.2811 18.0 45 0.4492 0.8290
0.2811 18.8 47 0.4216 0.8342
0.2026 20.0 50 0.4614 0.8394
0.2026 20.8 52 0.4325 0.8446
0.2026 22.0 55 0.4755 0.8342
0.2026 22.8 57 0.4175 0.8394
0.1709 24.0 60 0.4175 0.8497
0.1709 24.8 62 0.4105 0.8446
0.1709 26.0 65 0.4140 0.8601
0.1709 26.8 67 0.4641 0.8394
0.1293 28.0 70 0.4214 0.8394
0.1293 28.8 72 0.3802 0.8808
0.1293 30.0 75 0.4875 0.8290
0.1293 30.8 77 0.3972 0.8705
0.1167 32.0 80 0.4853 0.8394
0.1167 32.8 82 0.4082 0.8549
0.1167 34.0 85 0.3917 0.8601
0.1167 34.8 87 0.3573 0.8653
0.1034 36.0 90 0.4312 0.8497
0.1034 36.8 92 0.4035 0.8497
0.1034 38.0 95 0.4413 0.8238
0.1034 38.8 97 0.4728 0.8446
0.0782 40.0 100 0.3977 0.8808
0.0782 40.8 102 0.3449 0.8912
0.0782 42.0 105 0.4146 0.8808
0.0782 42.8 107 0.4380 0.8601
0.083 44.0 110 0.4579 0.8497
0.083 44.8 112 0.5234 0.8549
0.083 46.0 115 0.4053 0.8756
0.083 46.8 117 0.4724 0.8394
0.0741 48.0 120 0.4631 0.8549
0.0741 48.8 122 0.4351 0.8653
0.0741 50.0 125 0.4191 0.8756
0.0741 50.8 127 0.3772 0.8964
0.067 52.0 130 0.3960 0.8808
0.067 52.8 132 0.3749 0.8964
0.067 54.0 135 0.4395 0.8653
0.067 54.8 137 0.5284 0.8342
0.0632 56.0 140 0.3332 0.8808
0.0632 56.8 142 0.4342 0.8497
0.0632 58.0 145 0.3986 0.8756
0.0632 58.8 147 0.4771 0.8549
0.063 60.0 150 0.4505 0.8497
0.063 60.8 152 0.4023 0.8653
0.063 62.0 155 0.5208 0.8290
0.063 62.8 157 0.4915 0.8601
0.0571 64.0 160 0.4412 0.8756
0.0571 64.8 162 0.4554 0.8653
0.0571 66.0 165 0.4318 0.8653
0.0571 66.8 167 0.4317 0.8549
0.0608 68.0 170 0.4509 0.8653
0.0608 68.8 172 0.4176 0.8705
0.0608 70.0 175 0.5203 0.8394
0.0608 70.8 177 0.4375 0.8756
0.0478 72.0 180 0.4196 0.8601
0.0478 72.8 182 0.4744 0.8601
0.0478 74.0 185 0.4362 0.8808
0.0478 74.8 187 0.4804 0.8653
0.0519 76.0 190 0.4861 0.8446
0.0519 76.8 192 0.4605 0.8601
0.0519 78.0 195 0.4730 0.8394
0.0519 78.8 197 0.4650 0.8705
0.0553 80.0 200 0.3864 0.8860

Framework versions

  • Transformers 4.33.1
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.13.3