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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: vowelizer_1203_v2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# vowelizer_1203_v2 |
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This model is a fine-tuned version of [google/canine-s](https://huggingface.co/google/canine-s) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0018 |
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- Precision: 0.9989 |
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- Recall: 0.9988 |
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- F1: 0.9989 |
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- Accuracy: 0.9995 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.1589 | 1.0 | 1933 | 0.1247 | 0.9263 | 0.8773 | 0.9011 | 0.9577 | |
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| 0.1227 | 2.0 | 3866 | 0.0937 | 0.9453 | 0.9110 | 0.9278 | 0.9676 | |
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| 0.1019 | 3.0 | 5799 | 0.0738 | 0.9589 | 0.9261 | 0.9422 | 0.9743 | |
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| 0.0868 | 4.0 | 7732 | 0.0595 | 0.9654 | 0.9530 | 0.9592 | 0.9792 | |
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| 0.0745 | 5.0 | 9665 | 0.0470 | 0.9741 | 0.9609 | 0.9675 | 0.9833 | |
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| 0.0638 | 6.0 | 11598 | 0.0364 | 0.9799 | 0.9728 | 0.9764 | 0.9873 | |
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| 0.0529 | 7.0 | 13531 | 0.0282 | 0.9853 | 0.9748 | 0.9800 | 0.9899 | |
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| 0.0473 | 8.0 | 15464 | 0.0218 | 0.9894 | 0.9838 | 0.9866 | 0.9923 | |
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| 0.0381 | 9.0 | 17397 | 0.0170 | 0.9909 | 0.9895 | 0.9902 | 0.9940 | |
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| 0.0325 | 10.0 | 19330 | 0.0128 | 0.9936 | 0.9921 | 0.9928 | 0.9956 | |
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| 0.0284 | 11.0 | 21263 | 0.0100 | 0.9950 | 0.9938 | 0.9944 | 0.9965 | |
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| 0.0256 | 12.0 | 23196 | 0.0079 | 0.9959 | 0.9949 | 0.9954 | 0.9972 | |
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| 0.0222 | 13.0 | 25129 | 0.0058 | 0.9969 | 0.9965 | 0.9967 | 0.9980 | |
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| 0.0196 | 14.0 | 27062 | 0.0048 | 0.9974 | 0.9973 | 0.9974 | 0.9984 | |
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| 0.016 | 15.0 | 28995 | 0.0036 | 0.9979 | 0.9974 | 0.9977 | 0.9988 | |
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| 0.0143 | 16.0 | 30928 | 0.0030 | 0.9983 | 0.9981 | 0.9982 | 0.9990 | |
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| 0.0134 | 17.0 | 32861 | 0.0025 | 0.9986 | 0.9984 | 0.9985 | 0.9992 | |
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| 0.0117 | 18.0 | 34794 | 0.0021 | 0.9987 | 0.9986 | 0.9987 | 0.9993 | |
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| 0.0102 | 19.0 | 36727 | 0.0019 | 0.9987 | 0.9987 | 0.9987 | 0.9994 | |
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| 0.0098 | 20.0 | 38660 | 0.0018 | 0.9989 | 0.9988 | 0.9989 | 0.9995 | |
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### Framework versions |
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- Transformers 4.28.0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.13.3 |
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