---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: vowelizer_1203_v2
  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_v2

This model is a fine-tuned version of [google/canine-s](https://huggingface.co/google/canine-s) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0018
- Precision: 0.9989
- Recall: 0.9988
- F1: 0.9989
- Accuracy: 0.9995

## 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: 4
- eval_batch_size: 4
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1589        | 1.0   | 1933  | 0.1247          | 0.9263    | 0.8773 | 0.9011 | 0.9577   |
| 0.1227        | 2.0   | 3866  | 0.0937          | 0.9453    | 0.9110 | 0.9278 | 0.9676   |
| 0.1019        | 3.0   | 5799  | 0.0738          | 0.9589    | 0.9261 | 0.9422 | 0.9743   |
| 0.0868        | 4.0   | 7732  | 0.0595          | 0.9654    | 0.9530 | 0.9592 | 0.9792   |
| 0.0745        | 5.0   | 9665  | 0.0470          | 0.9741    | 0.9609 | 0.9675 | 0.9833   |
| 0.0638        | 6.0   | 11598 | 0.0364          | 0.9799    | 0.9728 | 0.9764 | 0.9873   |
| 0.0529        | 7.0   | 13531 | 0.0282          | 0.9853    | 0.9748 | 0.9800 | 0.9899   |
| 0.0473        | 8.0   | 15464 | 0.0218          | 0.9894    | 0.9838 | 0.9866 | 0.9923   |
| 0.0381        | 9.0   | 17397 | 0.0170          | 0.9909    | 0.9895 | 0.9902 | 0.9940   |
| 0.0325        | 10.0  | 19330 | 0.0128          | 0.9936    | 0.9921 | 0.9928 | 0.9956   |
| 0.0284        | 11.0  | 21263 | 0.0100          | 0.9950    | 0.9938 | 0.9944 | 0.9965   |
| 0.0256        | 12.0  | 23196 | 0.0079          | 0.9959    | 0.9949 | 0.9954 | 0.9972   |
| 0.0222        | 13.0  | 25129 | 0.0058          | 0.9969    | 0.9965 | 0.9967 | 0.9980   |
| 0.0196        | 14.0  | 27062 | 0.0048          | 0.9974    | 0.9973 | 0.9974 | 0.9984   |
| 0.016         | 15.0  | 28995 | 0.0036          | 0.9979    | 0.9974 | 0.9977 | 0.9988   |
| 0.0143        | 16.0  | 30928 | 0.0030          | 0.9983    | 0.9981 | 0.9982 | 0.9990   |
| 0.0134        | 17.0  | 32861 | 0.0025          | 0.9986    | 0.9984 | 0.9985 | 0.9992   |
| 0.0117        | 18.0  | 34794 | 0.0021          | 0.9987    | 0.9986 | 0.9987 | 0.9993   |
| 0.0102        | 19.0  | 36727 | 0.0019          | 0.9987    | 0.9987 | 0.9987 | 0.9994   |
| 0.0098        | 20.0  | 38660 | 0.0018          | 0.9989    | 0.9988 | 0.9989 | 0.9995   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.13.3