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canine_deasciifier_0305
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: 0.0007
- Precision: 0.9978
- Recall: 0.9983
- F1: 0.9981
- Accuracy: 0.9998
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: 32
- eval_batch_size: 32
- 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 |
---|---|---|---|---|---|---|---|
No log | 1.0 | 244 | 0.0672 | 0.7433 | 0.8204 | 0.7800 | 0.9735 |
No log | 2.0 | 488 | 0.0445 | 0.8205 | 0.8889 | 0.8533 | 0.9832 |
0.1401 | 3.0 | 732 | 0.0256 | 0.8950 | 0.9259 | 0.9102 | 0.9906 |
0.1401 | 4.0 | 976 | 0.0165 | 0.9384 | 0.9483 | 0.9433 | 0.9943 |
0.0364 | 5.0 | 1220 | 0.0112 | 0.9597 | 0.9629 | 0.9613 | 0.9962 |
0.0364 | 6.0 | 1464 | 0.0089 | 0.9656 | 0.9736 | 0.9696 | 0.9970 |
0.021 | 7.0 | 1708 | 0.0073 | 0.9710 | 0.9797 | 0.9753 | 0.9976 |
0.021 | 8.0 | 1952 | 0.0060 | 0.9740 | 0.9838 | 0.9789 | 0.9980 |
0.0139 | 9.0 | 2196 | 0.0038 | 0.9856 | 0.9890 | 0.9873 | 0.9988 |
0.0139 | 10.0 | 2440 | 0.0030 | 0.9893 | 0.9912 | 0.9903 | 0.9991 |
0.01 | 11.0 | 2684 | 0.0024 | 0.9916 | 0.9932 | 0.9924 | 0.9993 |
0.01 | 12.0 | 2928 | 0.0021 | 0.9919 | 0.9941 | 0.9930 | 0.9993 |
0.0072 | 13.0 | 3172 | 0.0018 | 0.9938 | 0.9957 | 0.9947 | 0.9995 |
0.0072 | 14.0 | 3416 | 0.0016 | 0.9940 | 0.9958 | 0.9949 | 0.9995 |
0.0056 | 15.0 | 3660 | 0.0012 | 0.9955 | 0.9968 | 0.9962 | 0.9996 |
0.0056 | 16.0 | 3904 | 0.0012 | 0.9954 | 0.9969 | 0.9962 | 0.9996 |
0.0045 | 17.0 | 4148 | 0.0008 | 0.9975 | 0.9979 | 0.9977 | 0.9998 |
0.0045 | 18.0 | 4392 | 0.0008 | 0.9975 | 0.9981 | 0.9978 | 0.9998 |
0.0039 | 19.0 | 4636 | 0.0008 | 0.9974 | 0.9981 | 0.9977 | 0.9998 |
0.0039 | 20.0 | 4880 | 0.0007 | 0.9978 | 0.9983 | 0.9981 | 0.9998 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3