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metadata
license: apache-2.0
base_model: google/canine-s
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: spellcorrector_17_02_050_qwerty
    results: []

spellcorrector_17_02_050_qwerty

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: 0.0163
  • Precision: 0.9930
  • Recall: 0.9887
  • F1: 0.9909
  • Accuracy: 0.9952

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
0.4639 1.0 967 0.1649 0.9619 0.9624 0.9622 0.9608
0.1737 2.0 1934 0.1300 0.9620 0.9656 0.9638 0.9664
0.145 3.0 2901 0.1099 0.9678 0.9694 0.9686 0.9708
0.1222 4.0 3868 0.0906 0.9699 0.9699 0.9699 0.9752
0.105 5.0 4835 0.0736 0.9726 0.9699 0.9712 0.9792
0.0933 6.0 5802 0.0633 0.9758 0.9732 0.9745 0.9817
0.0807 7.0 6769 0.0531 0.9822 0.9780 0.9801 0.9844
0.0715 8.0 7736 0.0468 0.9839 0.9828 0.9834 0.9864
0.0643 9.0 8703 0.0404 0.9833 0.9823 0.9828 0.9880
0.0575 10.0 9670 0.0356 0.9903 0.9866 0.9884 0.9894
0.0525 11.0 10637 0.0317 0.9887 0.9866 0.9876 0.9905
0.0481 12.0 11604 0.0281 0.9908 0.9871 0.9890 0.9915
0.0444 13.0 12571 0.0255 0.9919 0.9871 0.9895 0.9923
0.0417 14.0 13538 0.0235 0.9924 0.9877 0.9900 0.9930
0.0382 15.0 14505 0.0211 0.9925 0.9882 0.9903 0.9937
0.0358 16.0 15472 0.0198 0.9930 0.9887 0.9909 0.9941
0.034 17.0 16439 0.0184 0.9930 0.9882 0.9906 0.9946
0.0323 18.0 17406 0.0173 0.9930 0.9882 0.9906 0.9949
0.0306 19.0 18373 0.0167 0.9930 0.9887 0.9909 0.9951
0.0304 20.0 19340 0.0163 0.9930 0.9887 0.9909 0.9952

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.17.0
  • Tokenizers 0.15.2