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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: 0.9616
  • Accuracy: 0.5103
  • F1: 0.4983

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
1.5782 1.0 15 1.4989 0.3448 0.2657
1.5021 2.0 30 1.4732 0.3655 0.2645
1.4674 3.0 45 1.4384 0.3448 0.2525
1.4277 4.0 60 1.4140 0.3517 0.2751
1.4341 5.0 75 1.3905 0.3379 0.2546
1.3698 6.0 90 1.3697 0.3724 0.2936
1.4233 7.0 105 1.3196 0.3862 0.3073
1.3112 8.0 120 1.3048 0.4552 0.3958
1.372 9.0 135 1.2548 0.4138 0.3385
1.3284 10.0 150 1.2020 0.4759 0.4287
1.2412 11.0 165 1.1672 0.4966 0.4594
1.2508 12.0 180 1.1453 0.4897 0.4740
1.1843 13.0 195 1.1172 0.4966 0.4784
1.1694 14.0 210 1.1006 0.4966 0.4785
1.1438 15.0 225 1.0763 0.5034 0.4851
1.1066 16.0 240 1.0603 0.5034 0.4815
1.1357 17.0 255 1.0435 0.5034 0.4821
1.0352 18.0 270 1.0358 0.5034 0.4803
1.1355 19.0 285 1.0183 0.5103 0.4941
1.063 20.0 300 1.0063 0.5103 0.4957
1.0329 21.0 315 0.9960 0.5103 0.4989
1.063 22.0 330 0.9867 0.5103 0.4989
1.0289 23.0 345 0.9821 0.5103 0.4980
1.0624 24.0 360 0.9816 0.5103 0.4942
1.0404 25.0 375 0.9723 0.5103 0.4939
0.9791 26.0 390 0.9693 0.5103 0.4985
1.0365 27.0 405 0.9663 0.5103 0.4980
1.0129 28.0 420 0.9637 0.5103 0.5002
0.9844 29.0 435 0.9617 0.5103 0.4997
1.0049 30.0 450 0.9616 0.5103 0.4983

Framework versions

  • Transformers 4.24.0
  • Pytorch 1.13.1
  • Datasets 2.6.1
  • Tokenizers 0.11.0