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canine_2303
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: 0.0001
- Precision: 0.9987
- Recall: 0.9982
- F1: 0.9985
- Accuracy: 0.9999
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 | 0.0025 | 0.9819 | 0.9924 | 0.9871 | 0.9993 |
| No log | 2.0 | 488 | 0.0018 | 0.9855 | 0.9925 | 0.9890 | 0.9995 |
| 0.0382 | 3.0 | 732 | 0.0014 | 0.9923 | 0.9891 | 0.9907 | 0.9996 |
| 0.0382 | 4.0 | 976 | 0.0009 | 0.9930 | 0.9931 | 0.9931 | 0.9997 |
| 0.0017 | 5.0 | 1220 | 0.0009 | 0.9922 | 0.9949 | 0.9936 | 0.9997 |
| 0.0017 | 6.0 | 1464 | 0.0007 | 0.9940 | 0.9952 | 0.9946 | 0.9998 |
| 0.0012 | 7.0 | 1708 | 0.0005 | 0.9947 | 0.9952 | 0.9949 | 0.9998 |
| 0.0012 | 8.0 | 1952 | 0.0005 | 0.9947 | 0.9955 | 0.9951 | 0.9998 |
| 0.0009 | 9.0 | 2196 | 0.0003 | 0.9959 | 0.9960 | 0.9959 | 0.9998 |
| 0.0009 | 10.0 | 2440 | 0.0003 | 0.9958 | 0.9963 | 0.9961 | 0.9998 |
| 0.0007 | 11.0 | 2684 | 0.0003 | 0.9971 | 0.9958 | 0.9965 | 0.9999 |
| 0.0007 | 12.0 | 2928 | 0.0003 | 0.9971 | 0.9962 | 0.9967 | 0.9999 |
| 0.0005 | 13.0 | 3172 | 0.0002 | 0.9974 | 0.9967 | 0.9971 | 0.9999 |
| 0.0005 | 14.0 | 3416 | 0.0002 | 0.9980 | 0.9972 | 0.9976 | 0.9999 |
| 0.0004 | 15.0 | 3660 | 0.0002 | 0.9982 | 0.9980 | 0.9981 | 0.9999 |
| 0.0004 | 16.0 | 3904 | 0.0002 | 0.9984 | 0.9974 | 0.9979 | 0.9999 |
| 0.0004 | 17.0 | 4148 | 0.0001 | 0.9984 | 0.9975 | 0.9979 | 0.9999 |
| 0.0004 | 18.0 | 4392 | 0.0001 | 0.9988 | 0.9982 | 0.9985 | 0.9999 |
| 0.0003 | 19.0 | 4636 | 0.0001 | 0.9987 | 0.9982 | 0.9985 | 0.9999 |
| 0.0003 | 20.0 | 4880 | 0.0001 | 0.9987 | 0.9982 | 0.9985 | 0.9999 |
Framework versions
- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
