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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: vowelizer_1203_v11
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vowelizer_1203_v11
This model is a fine-tuned version of [Buseak/vowelizer_1203_v9](https://huggingface.co/Buseak/vowelizer_1203_v9) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0001
- Precision: 1.0000
- Recall: 1.0000
- F1: 1.0000
- Accuracy: 1.0000
## 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: 8
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0659 | 1.0 | 967 | 0.0290 | 0.9908 | 0.9845 | 0.9877 | 0.9920 |
| 0.0394 | 2.0 | 1934 | 0.0166 | 0.9950 | 0.9921 | 0.9936 | 0.9955 |
| 0.0271 | 3.0 | 2901 | 0.0098 | 0.9967 | 0.9958 | 0.9963 | 0.9974 |
| 0.0202 | 4.0 | 3868 | 0.0059 | 0.9981 | 0.9978 | 0.9979 | 0.9984 |
| 0.0152 | 5.0 | 4835 | 0.0037 | 0.9989 | 0.9982 | 0.9985 | 0.9991 |
| 0.0119 | 6.0 | 5802 | 0.0026 | 0.9992 | 0.9989 | 0.9990 | 0.9993 |
| 0.01 | 7.0 | 6769 | 0.0017 | 0.9995 | 0.9992 | 0.9994 | 0.9996 |
| 0.0077 | 8.0 | 7736 | 0.0013 | 0.9995 | 0.9995 | 0.9995 | 0.9997 |
| 0.0062 | 9.0 | 8703 | 0.0009 | 0.9996 | 0.9997 | 0.9997 | 0.9998 |
| 0.0062 | 10.0 | 9670 | 0.0006 | 0.9998 | 0.9998 | 0.9998 | 0.9999 |
| 0.0051 | 11.0 | 10637 | 0.0006 | 0.9998 | 0.9997 | 0.9998 | 0.9999 |
| 0.0043 | 12.0 | 11604 | 0.0004 | 0.9999 | 0.9999 | 0.9999 | 0.9999 |
| 0.0036 | 13.0 | 12571 | 0.0003 | 0.9999 | 0.9999 | 0.9999 | 0.9999 |
| 0.0031 | 14.0 | 13538 | 0.0002 | 0.9999 | 0.9999 | 0.9999 | 1.0000 |
| 0.0027 | 15.0 | 14505 | 0.0002 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
| 0.0025 | 16.0 | 15472 | 0.0001 | 1.0000 | 0.9999 | 0.9999 | 1.0000 |
| 0.0021 | 17.0 | 16439 | 0.0001 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
| 0.0019 | 18.0 | 17406 | 0.0001 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
| 0.0017 | 19.0 | 18373 | 0.0001 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
| 0.0016 | 20.0 | 19340 | 0.0001 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.13.3
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