---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: canine_vowelizer_0706_v4
  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_v4

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: 0.1450
- Precision: 1.0000
- Recall: 1.0
- F1: 1.0000
- Accuracy: 0.9775

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1088        | 1.0   | 1951  | 0.1144          | 0.9999    | 1.0    | 1.0000 | 0.9628   |
| 0.1009        | 2.0   | 3902  | 0.1023          | 0.9999    | 1.0    | 1.0000 | 0.9657   |
| 0.0917        | 3.0   | 5853  | 0.0985          | 1.0000    | 1.0    | 1.0000 | 0.9690   |
| 0.0757        | 4.0   | 7804  | 0.0928          | 1.0000    | 1.0000 | 1.0000 | 0.9712   |
| 0.0635        | 5.0   | 9755  | 0.0932          | 0.9999    | 1.0    | 1.0000 | 0.9725   |
| 0.0542        | 6.0   | 11706 | 0.0943          | 0.9999    | 1.0000 | 1.0000 | 0.9735   |
| 0.0453        | 7.0   | 13657 | 0.0980          | 1.0000    | 1.0000 | 1.0000 | 0.9738   |
| 0.0369        | 8.0   | 15608 | 0.1037          | 1.0000    | 1.0    | 1.0000 | 0.9750   |
| 0.0308        | 9.0   | 17559 | 0.1056          | 1.0000    | 1.0000 | 1.0000 | 0.9747   |
| 0.0275        | 10.0  | 19510 | 0.1138          | 1.0000    | 1.0    | 1.0000 | 0.9757   |
| 0.0222        | 11.0  | 21461 | 0.1187          | 1.0000    | 1.0    | 1.0000 | 0.9757   |
| 0.0185        | 12.0  | 23412 | 0.1201          | 1.0000    | 1.0000 | 1.0000 | 0.9761   |
| 0.0166        | 13.0  | 25363 | 0.1239          | 1.0000    | 1.0000 | 1.0000 | 0.9764   |
| 0.0146        | 14.0  | 27314 | 0.1302          | 1.0000    | 1.0    | 1.0000 | 0.9768   |
| 0.0112        | 15.0  | 29265 | 0.1351          | 1.0000    | 1.0000 | 1.0000 | 0.9768   |
| 0.0104        | 16.0  | 31216 | 0.1386          | 1.0000    | 1.0    | 1.0000 | 0.9769   |
| 0.0092        | 17.0  | 33167 | 0.1379          | 1.0000    | 1.0    | 1.0000 | 0.9771   |
| 0.0079        | 18.0  | 35118 | 0.1453          | 1.0000    | 1.0    | 1.0000 | 0.9771   |
| 0.0071        | 19.0  | 37069 | 0.1444          | 1.0000    | 1.0    | 1.0000 | 0.9775   |
| 0.0067        | 20.0  | 39020 | 0.1450          | 1.0000    | 1.0    | 1.0000 | 0.9775   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3