---
license: apache-2.0
base_model: google/canine-c
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: canine_deasciifier_final_0701_v8
  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_deasciifier_final_0701_v8

This model is a fine-tuned version of [google/canine-c](https://huggingface.co/google/canine-c) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0340
- Precision: 0.9153
- Recall: 0.9338
- F1: 0.9244
- Accuracy: 0.9918

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 488  | 0.0903          | 0.6868    | 0.7578 | 0.7205 | 0.9660   |
| 0.2413        | 2.0   | 976  | 0.0562          | 0.8068    | 0.8466 | 0.8262 | 0.9800   |
| 0.0812        | 3.0   | 1464 | 0.0409          | 0.8669    | 0.8674 | 0.8672 | 0.9854   |
| 0.052         | 4.0   | 1952 | 0.0381          | 0.8664    | 0.8971 | 0.8815 | 0.9870   |
| 0.0377        | 5.0   | 2440 | 0.0370          | 0.8738    | 0.9038 | 0.8886 | 0.9877   |
| 0.0291        | 6.0   | 2928 | 0.0314          | 0.9071    | 0.9088 | 0.9079 | 0.9900   |
| 0.0234        | 7.0   | 3416 | 0.0340          | 0.8867    | 0.9191 | 0.9026 | 0.9892   |
| 0.0196        | 8.0   | 3904 | 0.0325          | 0.9030    | 0.9242 | 0.9135 | 0.9905   |
| 0.0158        | 9.0   | 4392 | 0.0319          | 0.9109    | 0.9266 | 0.9187 | 0.9911   |
| 0.0132        | 10.0  | 4880 | 0.0329          | 0.9087    | 0.9318 | 0.9201 | 0.9913   |
| 0.0113        | 11.0  | 5368 | 0.0331          | 0.9045    | 0.9315 | 0.9178 | 0.9910   |
| 0.0099        | 12.0  | 5856 | 0.0337          | 0.9088    | 0.9314 | 0.9200 | 0.9913   |
| 0.0083        | 13.0  | 6344 | 0.0346          | 0.9133    | 0.9357 | 0.9244 | 0.9917   |
| 0.0081        | 14.0  | 6832 | 0.0332          | 0.9167    | 0.9324 | 0.9245 | 0.9918   |
| 0.0071        | 15.0  | 7320 | 0.0340          | 0.9153    | 0.9338 | 0.9244 | 0.9918   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0