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spellcorrector_2610_v16_canine-s

This model is a fine-tuned version of google/canine-s on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0974
  • Precision: 0.9789
  • Recall: 0.9829
  • F1: 0.9809
  • Accuracy: 0.9838

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: 30

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.252 1.0 976 0.1462 0.9386 0.9800 0.9589 0.9702
0.1463 2.0 1952 0.1256 0.9479 0.9794 0.9634 0.9721
0.1266 3.0 2928 0.1049 0.9578 0.9769 0.9673 0.9745
0.1081 4.0 3904 0.0938 0.9634 0.9787 0.9710 0.9772
0.0963 5.0 4880 0.0856 0.9663 0.9793 0.9727 0.9788
0.0863 6.0 5856 0.0838 0.9705 0.9759 0.9732 0.9786
0.077 7.0 6832 0.0804 0.9734 0.9757 0.9745 0.9797
0.0713 8.0 7808 0.0779 0.9726 0.9804 0.9765 0.9809
0.066 9.0 8784 0.0794 0.9749 0.9767 0.9758 0.9801
0.0602 10.0 9760 0.0748 0.9741 0.9823 0.9782 0.9821
0.0555 11.0 10736 0.0763 0.9750 0.9815 0.9782 0.9822
0.0512 12.0 11712 0.0764 0.9769 0.9800 0.9784 0.9823
0.048 13.0 12688 0.0767 0.9769 0.9822 0.9796 0.9832
0.0453 14.0 13664 0.0793 0.9767 0.9819 0.9793 0.9829
0.0412 15.0 14640 0.0809 0.9774 0.9822 0.9798 0.9832
0.0384 16.0 15616 0.0796 0.9765 0.9830 0.9798 0.9831
0.0364 17.0 16592 0.0830 0.9779 0.9825 0.9802 0.9833
0.0344 18.0 17568 0.0834 0.9779 0.9819 0.9799 0.9831
0.0307 19.0 18544 0.0857 0.9777 0.9823 0.9800 0.9832
0.0283 20.0 19520 0.0869 0.9776 0.9819 0.9797 0.9832
0.0269 21.0 20496 0.0885 0.9781 0.9822 0.9802 0.9833
0.0252 22.0 21472 0.0906 0.9784 0.9814 0.9799 0.9833
0.0229 23.0 22448 0.0932 0.9785 0.9820 0.9802 0.9833
0.0223 24.0 23424 0.0910 0.9785 0.9832 0.9809 0.9835
0.0209 25.0 24400 0.0936 0.9787 0.9824 0.9805 0.9836
0.0199 26.0 25376 0.0948 0.9791 0.9823 0.9807 0.9838
0.0189 27.0 26352 0.0961 0.9792 0.9828 0.9810 0.9838
0.0184 28.0 27328 0.0965 0.9786 0.9834 0.9810 0.9840
0.0178 29.0 28304 0.0970 0.9789 0.9829 0.9809 0.9838
0.0174 30.0 29280 0.0974 0.9789 0.9829 0.9809 0.9838

Framework versions

  • Transformers 4.28.0
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.13.3
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