|
--- |
|
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 |
|
|
