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metadata
license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: distilbert-base-uncased-finetuned-IAM
    results: []

distilbert-base-uncased-finetuned-IAM

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.9814
  • Accuracy: 0.5103
  • F1: 0.4950

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: 10
  • eval_batch_size: 10
  • seed: 42
  • 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 Accuracy F1
1.5871 1.0 15 1.4971 0.3379 0.1821
1.4995 2.0 30 1.4588 0.3379 0.1707
1.464 3.0 45 1.4251 0.3655 0.2870
1.4105 4.0 60 1.4027 0.3793 0.2899
1.4269 5.0 75 1.3798 0.3793 0.2899
1.3835 6.0 90 1.3425 0.3724 0.3087
1.3885 7.0 105 1.3041 0.4069 0.3515
1.3286 8.0 120 1.3004 0.4621 0.4450
1.3572 9.0 135 1.2621 0.4345 0.3903
1.3176 10.0 150 1.2033 0.4552 0.4250
1.2509 11.0 165 1.1942 0.5034 0.4755
1.2781 12.0 180 1.1689 0.4828 0.4651
1.2156 13.0 195 1.1438 0.5034 0.4837
1.1518 14.0 210 1.1187 0.5034 0.4844
1.161 15.0 225 1.1013 0.5034 0.4858
1.1377 16.0 240 1.0882 0.5034 0.4796
1.1634 17.0 255 1.0692 0.5034 0.4860
1.0666 18.0 270 1.0591 0.5034 0.4772
1.1358 19.0 285 1.0455 0.5034 0.4736
1.1118 20.0 300 1.0313 0.5034 0.4872
1.0367 21.0 315 1.0228 0.5034 0.4853
1.0781 22.0 330 1.0106 0.5034 0.4857
1.0346 23.0 345 1.0034 0.5034 0.4935
1.1015 24.0 360 1.0032 0.5034 0.4806
1.0147 25.0 375 0.9911 0.5103 0.4903
1.0144 26.0 390 0.9856 0.5103 0.4972
1.022 27.0 405 0.9835 0.5103 0.4982
1.0218 28.0 420 0.9821 0.5103 0.4955
1.0173 29.0 435 0.9811 0.5103 0.4950
1.0241 30.0 450 0.9814 0.5103 0.4950

Framework versions

  • Transformers 4.24.0
  • Pytorch 2.0.0
  • Datasets 2.10.1
  • Tokenizers 0.11.0