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bert-keyword-extractor_oknashar

This model is a fine-tuned version of yanekyuk/bert-keyword-extractor on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2965
  • Precision: 0.5862
  • Recall: 0.6937
  • F1: 0.6354
  • Accuracy: 0.9555

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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.204 1.0 1357 0.1717 0.3345 0.3419 0.3382 0.9375
0.161 2.0 2714 0.1547 0.3949 0.4845 0.4352 0.9426
0.1333 3.0 4071 0.1506 0.4369 0.5342 0.4807 0.9481
0.1147 4.0 5428 0.1461 0.5121 0.5424 0.5268 0.9522
0.0925 5.0 6785 0.1514 0.4864 0.5868 0.5319 0.9506
0.0788 6.0 8142 0.1477 0.5173 0.6043 0.5574 0.9529
0.0676 7.0 9499 0.1765 0.5126 0.6277 0.5644 0.9543
0.0542 8.0 10856 0.1638 0.5066 0.6704 0.5771 0.9500
0.0453 9.0 12213 0.1746 0.5158 0.6598 0.5790 0.9517
0.0366 10.0 13570 0.1905 0.5876 0.6429 0.6140 0.9569
0.0327 11.0 14927 0.2215 0.5709 0.6593 0.6119 0.9557
0.0296 12.0 16284 0.2109 0.5648 0.6598 0.6086 0.9557
0.0269 13.0 17641 0.2226 0.5632 0.6745 0.6138 0.9543
0.0203 14.0 18998 0.2464 0.5682 0.6861 0.6217 0.9543
0.0163 15.0 20355 0.2738 0.5747 0.6791 0.6226 0.9541
0.016 16.0 21712 0.2805 0.5868 0.6832 0.6314 0.9557
0.0115 17.0 23069 0.2923 0.5798 0.6879 0.6292 0.9551
0.0107 18.0 24426 0.2923 0.5943 0.6926 0.6397 0.9558
0.0105 19.0 25783 0.2992 0.5890 0.6902 0.6356 0.9553
0.0087 20.0 27140 0.2965 0.5862 0.6937 0.6354 0.9555

Framework versions

  • Transformers 4.34.1
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.1
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