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distilbert-bpmn

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

  • Loss: 0.3311
  • Precision: 0.7852
  • Recall: 0.8375
  • F1: 0.8105
  • Accuracy: 0.9275

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: 15

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
2.0392 1.0 12 1.5999 0.2162 0.2333 0.2244 0.5017
1.3439 2.0 24 1.0197 0.3786 0.4875 0.4262 0.7133
0.8403 3.0 36 0.6398 0.5664 0.675 0.6160 0.8333
0.4941 4.0 48 0.4637 0.6775 0.7792 0.7248 0.8765
0.3227 5.0 60 0.3701 0.7262 0.7958 0.7594 0.9041
0.2206 6.0 72 0.3286 0.75 0.8125 0.78 0.9231
0.1762 7.0 84 0.3330 0.7597 0.8167 0.7871 0.9180
0.1261 8.0 96 0.3159 0.7952 0.825 0.8098 0.9266
0.1121 9.0 108 0.3205 0.7860 0.8417 0.8129 0.9275
0.0902 10.0 120 0.3090 0.8071 0.8542 0.8300 0.9326
0.08 11.0 132 0.3200 0.7821 0.8375 0.8089 0.9266
0.0789 12.0 144 0.3226 0.7915 0.8542 0.8216 0.9283
0.0654 13.0 156 0.3311 0.7852 0.8375 0.8105 0.9275

Framework versions

  • Transformers 4.26.1
  • Pytorch 1.13.1+cu116
  • Datasets 2.10.1
  • Tokenizers 0.13.2
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