spellcorrector_0411 / README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: spellcorrector_0411
    results: []

spellcorrector_0411

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: 0.0830
  • Precision: 0.9784
  • Recall: 0.9815
  • F1: 0.9799
  • Accuracy: 0.9828

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: 20

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.2319 1.0 975 0.1268 0.9458 0.9834 0.9642 0.9741
0.1296 2.0 1950 0.1063 0.9530 0.9812 0.9669 0.9754
0.1095 3.0 2925 0.0883 0.9653 0.9788 0.9720 0.9786
0.0934 4.0 3900 0.0842 0.9692 0.9776 0.9734 0.9790
0.0829 5.0 4875 0.0794 0.9716 0.9797 0.9756 0.9809
0.0753 6.0 5850 0.0755 0.9729 0.9816 0.9773 0.9817
0.0695 7.0 6825 0.0739 0.9751 0.9789 0.9770 0.9815
0.0641 8.0 7800 0.0736 0.9767 0.9798 0.9782 0.9821
0.0591 9.0 8775 0.0744 0.9767 0.9805 0.9786 0.9822
0.0537 10.0 9750 0.0742 0.9777 0.9798 0.9787 0.9822
0.0502 11.0 10725 0.0753 0.9773 0.9806 0.9790 0.9825
0.0472 12.0 11700 0.0757 0.9780 0.9808 0.9794 0.9827
0.044 13.0 12675 0.0768 0.9772 0.9816 0.9794 0.9827
0.0407 14.0 13650 0.0784 0.9775 0.9815 0.9795 0.9827
0.039 15.0 14625 0.0790 0.9779 0.9816 0.9798 0.9828
0.0364 16.0 15600 0.0804 0.9778 0.9813 0.9795 0.9825
0.0343 17.0 16575 0.0811 0.9783 0.9811 0.9797 0.9828
0.0329 18.0 17550 0.0819 0.9785 0.9820 0.9803 0.9829
0.0314 19.0 18525 0.0822 0.9785 0.9808 0.9797 0.9826
0.0308 20.0 19500 0.0830 0.9784 0.9815 0.9799 0.9828

Framework versions

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