---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: trim-lesson7-classification
  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. -->

# trim-lesson7-classification

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1884
- Accuracy: 0.9670
- F1-score: 0.9671
- Recall-score: 0.9670
- Precision-score: 0.9679

## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Recall-score | Precision-score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------------:|:---------------:|
| 3.8363        | 1.0   | 223  | 3.7557          | 0.1494   | 0.0778   | 0.1494       | 0.0756          |
| 2.7979        | 2.0   | 446  | 2.5827          | 0.6355   | 0.5759   | 0.6355       | 0.6166          |
| 1.6429        | 3.0   | 669  | 1.5768          | 0.9235   | 0.9229   | 0.9235       | 0.9280          |
| 1.659         | 4.0   | 892  | 0.8894          | 0.9440   | 0.9439   | 0.9440       | 0.9461          |
| 0.5305        | 5.0   | 1115 | 0.5156          | 0.9557   | 0.9557   | 0.9557       | 0.9573          |
| 0.3831        | 6.0   | 1338 | 0.3869          | 0.9537   | 0.9539   | 0.9537       | 0.9560          |
| 0.1822        | 7.0   | 1561 | 0.3246          | 0.9576   | 0.9576   | 0.9576       | 0.9589          |
| 0.1308        | 8.0   | 1784 | 0.2840          | 0.9523   | 0.9524   | 0.9523       | 0.9540          |
| 0.1087        | 9.0   | 2007 | 0.3299          | 0.9401   | 0.9393   | 0.9401       | 0.9460          |
| 0.0968        | 10.0  | 2230 | 0.2539          | 0.9548   | 0.9549   | 0.9548       | 0.9571          |
| 0.1088        | 11.0  | 2453 | 0.2290          | 0.9606   | 0.9606   | 0.9606       | 0.9617          |
| 0.7219        | 12.0  | 2676 | 0.2346          | 0.9606   | 0.9607   | 0.9606       | 0.9616          |
| 0.2103        | 13.0  | 2899 | 0.2119          | 0.9629   | 0.9629   | 0.9629       | 0.9640          |
| 0.0414        | 14.0  | 3122 | 0.2431          | 0.9590   | 0.9590   | 0.9590       | 0.9603          |
| 0.9212        | 15.0  | 3345 | 0.2141          | 0.9651   | 0.9651   | 0.9651       | 0.9664          |
| 0.0244        | 16.0  | 3568 | 0.2185          | 0.9620   | 0.9620   | 0.9620       | 0.9633          |
| 0.0468        | 17.0  | 3791 | 0.1949          | 0.9645   | 0.9645   | 0.9645       | 0.9655          |
| 1.2045        | 18.0  | 4014 | 0.1985          | 0.9637   | 0.9637   | 0.9637       | 0.9648          |
| 0.1907        | 19.0  | 4237 | 0.1894          | 0.9634   | 0.9635   | 0.9634       | 0.9646          |
| 0.0185        | 20.0  | 4460 | 0.1956          | 0.9640   | 0.9640   | 0.9640       | 0.9648          |
| 0.0159        | 21.0  | 4683 | 0.2118          | 0.9601   | 0.9601   | 0.9601       | 0.9610          |
| 0.0633        | 22.0  | 4906 | 0.1953          | 0.9634   | 0.9635   | 0.9634       | 0.9646          |
| 0.0244        | 23.0  | 5129 | 0.1915          | 0.9665   | 0.9665   | 0.9665       | 0.9673          |
| 0.008         | 24.0  | 5352 | 0.1842          | 0.9690   | 0.9690   | 0.9690       | 0.9698          |
| 0.2523        | 25.0  | 5575 | 0.1884          | 0.9670   | 0.9671   | 0.9670       | 0.9679          |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.3.0+cu118
- Datasets 3.0.1
- Tokenizers 0.20.0