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group3_non_all_zero_notEqualWeights

This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 2.3167
  • Precision: 0.0476
  • Recall: 0.2642
  • F1: 0.0807
  • Accuracy: 0.9145

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: 3e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • 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
No log 1.0 55 1.3844 0.0068 0.2579 0.0133 0.6506
No log 2.0 110 1.1245 0.0107 0.2342 0.0205 0.7285
No log 3.0 165 1.2261 0.0103 0.2120 0.0196 0.7286
No log 4.0 220 1.1828 0.0099 0.1693 0.0188 0.7551
No log 5.0 275 1.2474 0.0141 0.2152 0.0265 0.8008
No log 6.0 330 1.4395 0.0264 0.2516 0.0478 0.8601
No log 7.0 385 1.5667 0.0253 0.2278 0.0456 0.8614
No log 8.0 440 1.6080 0.0286 0.2468 0.0512 0.8756
No log 9.0 495 1.7798 0.0289 0.2358 0.0515 0.8849
0.6462 10.0 550 1.9265 0.0364 0.2579 0.0638 0.8933
0.6462 11.0 605 2.0633 0.0347 0.2468 0.0608 0.8911
0.6462 12.0 660 2.2610 0.0458 0.2690 0.0783 0.9138
0.6462 13.0 715 2.1700 0.0435 0.2595 0.0745 0.9044
0.6462 14.0 770 2.3153 0.0480 0.2690 0.0814 0.9127
0.6462 15.0 825 2.3167 0.0476 0.2642 0.0807 0.9145

Framework versions

  • Transformers 4.30.0
  • Pytorch 2.2.2+cu121
  • Datasets 2.19.0
  • Tokenizers 0.13.3
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