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vowelizer_1203_v2

Token Classification
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canine
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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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+
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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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+
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+ # vowelizer_1203_v2
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+
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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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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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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+
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+
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+ ### Framework versions
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+
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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