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group2_non_all_zero

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: 2.3325
  • Precision: 0.0395
  • Recall: 0.182
  • F1: 0.0649
  • Accuracy: 0.8597

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 43 1.5592 0.0020 0.124 0.0040 0.3311
No log 2.0 86 1.2689 0.0104 0.14 0.0193 0.6247
No log 3.0 129 1.1742 0.0110 0.172 0.0206 0.6614
No log 4.0 172 1.3716 0.0147 0.178 0.0271 0.6468
No log 5.0 215 1.3265 0.0177 0.178 0.0323 0.7203
No log 6.0 258 1.5835 0.0217 0.176 0.0386 0.7574
No log 7.0 301 1.6678 0.0249 0.174 0.0435 0.7952
No log 8.0 344 1.9432 0.0387 0.18 0.0636 0.8551
No log 9.0 387 1.9371 0.0306 0.188 0.0526 0.7962
No log 10.0 430 2.0129 0.0305 0.182 0.0523 0.8187
No log 11.0 473 2.1952 0.0402 0.192 0.0664 0.8595
0.5993 12.0 516 2.1873 0.0369 0.182 0.0614 0.8512
0.5993 13.0 559 2.2653 0.0394 0.18 0.0646 0.8583
0.5993 14.0 602 2.3001 0.0397 0.184 0.0653 0.8553
0.5993 15.0 645 2.3325 0.0395 0.182 0.0649 0.8597

Framework versions

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