---
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