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spellcorrector_910_v13_autoregressive
This model is a fine-tuned version of google/canine-c on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0170
- Precision: 0.9963
- Recall: 0.9957
- F1: 0.9960
- Accuracy: 0.9953
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: 25
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.2531 | 1.0 | 1951 | 0.2224 | 0.9056 | 0.9748 | 0.9389 | 0.9590 |
| 0.208 | 2.0 | 3902 | 0.1857 | 0.9143 | 0.9734 | 0.9429 | 0.9613 |
| 0.1784 | 3.0 | 5853 | 0.1583 | 0.9213 | 0.9743 | 0.9470 | 0.9637 |
| 0.1604 | 4.0 | 7804 | 0.1380 | 0.9310 | 0.9735 | 0.9517 | 0.9662 |
| 0.1468 | 5.0 | 9755 | 0.1190 | 0.9399 | 0.9765 | 0.9579 | 0.9698 |
| 0.1301 | 6.0 | 11706 | 0.1028 | 0.9540 | 0.9744 | 0.9641 | 0.9737 |
| 0.1157 | 7.0 | 13657 | 0.0841 | 0.9610 | 0.9788 | 0.9698 | 0.9782 |
| 0.101 | 8.0 | 15608 | 0.0762 | 0.9702 | 0.9764 | 0.9733 | 0.9800 |
| 0.0892 | 9.0 | 17559 | 0.0621 | 0.9738 | 0.9823 | 0.9780 | 0.9835 |
| 0.0797 | 10.0 | 19510 | 0.0539 | 0.9756 | 0.9855 | 0.9805 | 0.9855 |
| 0.0734 | 11.0 | 21461 | 0.0490 | 0.9802 | 0.9848 | 0.9825 | 0.9868 |
| 0.0647 | 12.0 | 23412 | 0.0428 | 0.9826 | 0.9871 | 0.9849 | 0.9882 |
| 0.0606 | 13.0 | 25363 | 0.0400 | 0.9856 | 0.9871 | 0.9863 | 0.9890 |
| 0.0556 | 14.0 | 27314 | 0.0356 | 0.9874 | 0.9881 | 0.9877 | 0.9900 |
| 0.0506 | 15.0 | 29265 | 0.0334 | 0.9891 | 0.9896 | 0.9893 | 0.9907 |
| 0.0471 | 16.0 | 31216 | 0.0292 | 0.9902 | 0.9910 | 0.9906 | 0.9917 |
| 0.0442 | 17.0 | 33167 | 0.0270 | 0.9913 | 0.9915 | 0.9914 | 0.9923 |
| 0.0406 | 18.0 | 35118 | 0.0246 | 0.9924 | 0.9931 | 0.9928 | 0.9931 |
| 0.0383 | 19.0 | 37069 | 0.0230 | 0.9935 | 0.9935 | 0.9935 | 0.9936 |
| 0.0354 | 20.0 | 39020 | 0.0209 | 0.9944 | 0.9942 | 0.9943 | 0.9942 |
| 0.0337 | 21.0 | 40971 | 0.0204 | 0.9950 | 0.9942 | 0.9946 | 0.9943 |
| 0.0316 | 22.0 | 42922 | 0.0183 | 0.9956 | 0.9952 | 0.9954 | 0.9949 |
| 0.0302 | 23.0 | 44873 | 0.0177 | 0.9960 | 0.9954 | 0.9957 | 0.9951 |
| 0.0297 | 24.0 | 46824 | 0.0171 | 0.9961 | 0.9957 | 0.9959 | 0.9953 |
| 0.0284 | 25.0 | 48775 | 0.0170 | 0.9963 | 0.9957 | 0.9960 | 0.9953 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
