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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_v9 |
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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_v9 |
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This model is a fine-tuned version of [Buseak/vowelizer_1203_v6](https://huggingface.co/Buseak/vowelizer_1203_v6) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0000 |
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- Precision: 1.0000 |
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- Recall: 1.0000 |
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- F1: 1.0000 |
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- Accuracy: 1.0000 |
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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: 8 |
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- eval_batch_size: 8 |
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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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| 0.0516 | 1.0 | 967 | 0.0195 | 0.9907 | 0.9827 | 0.9867 | 0.9941 | |
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| 0.0318 | 2.0 | 1934 | 0.0109 | 0.9950 | 0.9901 | 0.9925 | 0.9967 | |
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| 0.0225 | 3.0 | 2901 | 0.0065 | 0.9960 | 0.9950 | 0.9955 | 0.9980 | |
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| 0.017 | 4.0 | 3868 | 0.0037 | 0.9981 | 0.9968 | 0.9975 | 0.9988 | |
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| 0.013 | 5.0 | 4835 | 0.0026 | 0.9986 | 0.9980 | 0.9983 | 0.9992 | |
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| 0.0103 | 6.0 | 5802 | 0.0018 | 0.9989 | 0.9988 | 0.9989 | 0.9995 | |
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| 0.0091 | 7.0 | 6769 | 0.0012 | 0.9992 | 0.9990 | 0.9991 | 0.9996 | |
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| 0.0073 | 8.0 | 7736 | 0.0009 | 0.9994 | 0.9992 | 0.9993 | 0.9997 | |
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| 0.0065 | 9.0 | 8703 | 0.0006 | 0.9996 | 0.9996 | 0.9996 | 0.9998 | |
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| 0.0057 | 10.0 | 9670 | 0.0004 | 0.9997 | 0.9997 | 0.9997 | 0.9999 | |
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| 0.0045 | 11.0 | 10637 | 0.0003 | 0.9997 | 0.9997 | 0.9997 | 0.9999 | |
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| 0.004 | 12.0 | 11604 | 0.0003 | 0.9999 | 0.9998 | 0.9998 | 0.9999 | |
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| 0.0035 | 13.0 | 12571 | 0.0002 | 0.9998 | 0.9998 | 0.9998 | 0.9999 | |
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| 0.003 | 14.0 | 13538 | 0.0002 | 0.9999 | 0.9999 | 0.9999 | 1.0000 | |
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| 0.0029 | 15.0 | 14505 | 0.0001 | 0.9999 | 0.9999 | 0.9999 | 1.0000 | |
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| 0.0024 | 16.0 | 15472 | 0.0001 | 1.0000 | 0.9999 | 0.9999 | 1.0000 | |
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| 0.0021 | 17.0 | 16439 | 0.0001 | 0.9999 | 0.9999 | 0.9999 | 1.0000 | |
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| 0.0019 | 18.0 | 17406 | 0.0001 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | |
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| 0.0018 | 19.0 | 18373 | 0.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | |
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| 0.0015 | 20.0 | 19340 | 0.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | |
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### Framework versions |
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- Transformers 4.28.0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.13.3 |
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