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metadata
license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: spellcorrector_1709_v6
    results: []

spellcorrector_1709_v6

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: 0.0158
  • Precision: 0.9871
  • Recall: 0.9831
  • F1: 0.9851
  • Accuracy: 0.9951

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: 4
  • eval_batch_size: 4
  • 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
0.2794 1.0 1951 0.2144 0.7935 0.7534 0.7729 0.9414
0.2179 2.0 3902 0.1587 0.8333 0.7994 0.8160 0.9555
0.1752 3.0 5853 0.1272 0.8639 0.8239 0.8434 0.9642
0.1507 4.0 7804 0.1063 0.8844 0.8510 0.8674 0.9699
0.1273 5.0 9755 0.0859 0.9044 0.8753 0.8896 0.9755
0.1106 6.0 11706 0.0762 0.9197 0.8838 0.9014 0.9779
0.0984 7.0 13657 0.0670 0.9255 0.9035 0.9144 0.9805
0.0861 8.0 15608 0.0571 0.9402 0.9150 0.9274 0.9832
0.0754 9.0 17559 0.0517 0.9469 0.9269 0.9368 0.9844
0.07 10.0 19510 0.0442 0.9559 0.9361 0.9459 0.9866
0.0636 11.0 21461 0.0387 0.9604 0.9477 0.9540 0.9882
0.0597 12.0 23412 0.0315 0.9673 0.9559 0.9616 0.9903
0.0523 13.0 25363 0.0296 0.9723 0.9612 0.9667 0.9909
0.0449 14.0 27314 0.0258 0.9744 0.9683 0.9713 0.9920
0.0407 15.0 29265 0.0226 0.9797 0.9715 0.9756 0.9930
0.0395 16.0 31216 0.0212 0.9817 0.9758 0.9788 0.9934
0.0357 17.0 33167 0.0181 0.9843 0.9783 0.9813 0.9943
0.0356 18.0 35118 0.0172 0.9859 0.9812 0.9836 0.9947
0.0328 19.0 37069 0.0162 0.9865 0.9829 0.9847 0.9950
0.0316 20.0 39020 0.0158 0.9871 0.9831 0.9851 0.9951

Framework versions

  • Transformers 4.28.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.13.3