slac-taste

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

  • Loss: 1.1289
  • Accuracy: 0.9076
  • F1 Macro: 0.8815
  • Precision Macro: 0.8776
  • Recall Macro: 0.8857
  • Total Tf: [1454, 148, 1454, 148]

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: 64
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 212
  • num_epochs: 15

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Macro Precision Macro Recall Macro Total Tf
0.4647 1.0 213 0.3426 0.9082 0.8894 0.8698 0.9217 [1455, 147, 1455, 147]
0.4011 2.0 426 0.3131 0.9238 0.9051 0.8917 0.9222 [1480, 122, 1480, 122]
0.2826 3.0 639 0.3667 0.9101 0.8899 0.8731 0.9144 [1458, 144, 1458, 144]
0.2055 4.0 852 0.4153 0.9164 0.8953 0.8834 0.9101 [1468, 134, 1468, 134]
0.1422 5.0 1065 0.4790 0.9151 0.8922 0.8847 0.9008 [1466, 136, 1466, 136]
0.1222 6.0 1278 0.6584 0.9164 0.8932 0.8876 0.8993 [1468, 134, 1468, 134]
0.0732 7.0 1491 0.7369 0.9020 0.8769 0.8666 0.8896 [1445, 157, 1445, 157]
0.0567 8.0 1704 0.8350 0.9107 0.8859 0.8807 0.8916 [1459, 143, 1459, 143]
0.0377 9.0 1917 0.8175 0.9120 0.8885 0.8804 0.8979 [1461, 141, 1461, 141]
0.0358 10.0 2130 0.9032 0.9107 0.8866 0.8794 0.8947 [1459, 143, 1459, 143]
0.0326 11.0 2343 0.9523 0.9039 0.8794 0.8687 0.8924 [1448, 154, 1448, 154]
0.011 12.0 2556 1.1113 0.9082 0.8817 0.8797 0.8838 [1455, 147, 1455, 147]
0.0159 13.0 2769 1.1049 0.9064 0.8810 0.8741 0.8887 [1452, 150, 1452, 150]
0.0191 14.0 2982 1.1846 0.9101 0.8831 0.8843 0.8819 [1458, 144, 1458, 144]
0.0095 15.0 3195 1.1289 0.9076 0.8815 0.8776 0.8857 [1454, 148, 1454, 148]

Framework versions

  • Transformers 4.44.0
  • Pytorch 2.4.0
  • Datasets 2.21.0
  • Tokenizers 0.19.1
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