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aift-model

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 3.1789
  • Accuracy Thresh: 0.9161

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: 25

Training results

Training Loss Epoch Step Validation Loss Accuracy Thresh
No log 1.0 129 2.3568 0.3224
No log 2.0 258 1.6533 0.5518
No log 3.0 387 1.3939 0.7174
2.7357 4.0 516 1.3937 0.7875
2.7357 5.0 645 1.3416 0.8023
2.7357 6.0 774 1.3744 0.8696
2.7357 7.0 903 1.4840 0.8753
0.727 8.0 1032 1.6799 0.8858
0.727 9.0 1161 1.6802 0.8830
0.727 10.0 1290 1.8544 0.8968
0.727 11.0 1419 1.8931 0.8971
0.4047 12.0 1548 2.1624 0.8978
0.4047 13.0 1677 2.3448 0.9042
0.4047 14.0 1806 2.4661 0.9087
0.4047 15.0 1935 2.5098 0.9087
0.2396 16.0 2064 2.7352 0.9130
0.2396 17.0 2193 2.7756 0.9123
0.2396 18.0 2322 2.9367 0.9151
0.2396 19.0 2451 2.9757 0.9140
0.1658 20.0 2580 2.9550 0.9140
0.1658 21.0 2709 3.0914 0.9161
0.1658 22.0 2838 3.1505 0.9158
0.1658 23.0 2967 3.0856 0.9147
0.1233 24.0 3096 3.1968 0.9175
0.1233 25.0 3225 3.1789 0.9161

Framework versions

  • Transformers 4.38.0.dev0
  • Pytorch 2.2.0+cu121
  • Datasets 2.17.1
  • Tokenizers 0.15.2
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