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metadata
license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: spellcorrector_1709_v7
    results: []

spellcorrector_1709_v7

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: 0.0885
  • Precision: 0.9610
  • Recall: 0.9739
  • F1: 0.9674
  • Accuracy: 0.9742

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
0.2583 1.0 1951 0.2210 0.8849 0.9710 0.9260 0.9443
0.2244 2.0 3902 0.1992 0.8954 0.9685 0.9305 0.9466
0.2099 3.0 5853 0.1868 0.9021 0.9684 0.9341 0.9482
0.1989 4.0 7804 0.1789 0.9109 0.9634 0.9364 0.9500
0.1926 5.0 9755 0.1694 0.9141 0.9652 0.9389 0.9518
0.1837 6.0 11706 0.1605 0.9160 0.9697 0.9421 0.9540
0.1817 7.0 13657 0.1547 0.9209 0.9656 0.9427 0.9549
0.1723 8.0 15608 0.1484 0.9226 0.9687 0.9451 0.9564
0.1692 9.0 17559 0.1442 0.9269 0.9649 0.9455 0.9571
0.1642 10.0 19510 0.1386 0.9269 0.9697 0.9478 0.9587
0.1578 11.0 21461 0.1327 0.9310 0.9693 0.9497 0.9600
0.148 12.0 23412 0.1231 0.9393 0.9674 0.9532 0.9630
0.1455 13.0 25363 0.1172 0.9413 0.9711 0.9560 0.9653
0.1402 14.0 27314 0.1123 0.9476 0.9673 0.9573 0.9662
0.1323 15.0 29265 0.1056 0.9511 0.9694 0.9602 0.9685
0.1269 16.0 31216 0.0989 0.9521 0.9740 0.9629 0.9709
0.1225 17.0 33167 0.0953 0.9575 0.9716 0.9645 0.9720
0.1186 18.0 35118 0.0907 0.9582 0.9744 0.9662 0.9735
0.1169 19.0 37069 0.0897 0.9606 0.9734 0.9670 0.9738
0.113 20.0 39020 0.0885 0.9610 0.9739 0.9674 0.9742

Framework versions

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