---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: canine_vowelizer_0706_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. -->

# canine_vowelizer_0706_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: 0.0003
- 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: 2
- eval_batch_size: 2
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.161         | 1.0   | 3902  | 0.1236          | 0.9999    | 1.0000 | 0.9999 | 0.9578   |
| 0.1197        | 2.0   | 7804  | 0.0883          | 1.0000    | 1.0000 | 1.0000 | 0.9689   |
| 0.0978        | 3.0   | 11706 | 0.0626          | 1.0000    | 1.0000 | 1.0000 | 0.9779   |
| 0.0808        | 4.0   | 15608 | 0.0454          | 1.0000    | 1.0000 | 1.0000 | 0.9838   |
| 0.0668        | 5.0   | 19510 | 0.0320          | 1.0000    | 1.0000 | 1.0000 | 0.9885   |
| 0.0524        | 6.0   | 23412 | 0.0219          | 1.0000    | 1.0000 | 1.0000 | 0.9921   |
| 0.042         | 7.0   | 27314 | 0.0150          | 1.0000    | 1.0000 | 1.0000 | 0.9946   |
| 0.0348        | 8.0   | 31216 | 0.0109          | 1.0000    | 1.0000 | 1.0000 | 0.9961   |
| 0.0286        | 9.0   | 35118 | 0.0072          | 1.0000    | 1.0000 | 1.0000 | 0.9974   |
| 0.025         | 10.0  | 39020 | 0.0049          | 1.0000    | 1.0000 | 1.0000 | 0.9983   |
| 0.0183        | 11.0  | 42922 | 0.0035          | 1.0000    | 1.0000 | 1.0000 | 0.9988   |
| 0.0157        | 12.0  | 46824 | 0.0025          | 1.0000    | 1.0000 | 1.0000 | 0.9992   |
| 0.0113        | 13.0  | 50726 | 0.0016          | 1.0000    | 1.0000 | 1.0000 | 0.9995   |
| 0.0097        | 14.0  | 54628 | 0.0012          | 1.0000    | 1.0000 | 1.0000 | 0.9996   |
| 0.0081        | 15.0  | 58530 | 0.0008          | 1.0000    | 1.0000 | 1.0000 | 0.9998   |
| 0.0071        | 16.0  | 62432 | 0.0007          | 1.0000    | 1.0000 | 1.0000 | 0.9998   |
| 0.0054        | 17.0  | 66334 | 0.0005          | 1.0000    | 1.0000 | 1.0000 | 0.9999   |
| 0.0044        | 18.0  | 70236 | 0.0004          | 1.0000    | 1.0000 | 1.0000 | 0.9999   |
| 0.0053        | 19.0  | 74138 | 0.0003          | 1.0000    | 1.0000 | 1.0000 | 1.0000   |
| 0.0039        | 20.0  | 78040 | 0.0003          | 1.0000    | 1.0000 | 1.0000 | 1.0000   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3