---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: wav2vec2-classifier
  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. -->

# wav2vec2-classifier

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.4346
- Accuracy: 0.0121
- Precision: 0.0001
- Recall: 0.0121
- F1: 0.0003
- Binary: 0.1515

## 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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Binary |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| No log        | 0.08  | 50   | 4.4494          | 0.0146   | 0.0002    | 0.0146 | 0.0004 | 0.1633 |
| No log        | 0.16  | 100  | 4.4464          | 0.0097   | 0.0001    | 0.0097 | 0.0002 | 0.1112 |
| No log        | 0.24  | 150  | 4.4330          | 0.0121   | 0.0001    | 0.0121 | 0.0003 | 0.1498 |
| No log        | 0.31  | 200  | 4.4432          | 0.0097   | 0.0001    | 0.0097 | 0.0002 | 0.1112 |
| No log        | 0.39  | 250  | 4.4338          | 0.0121   | 0.0001    | 0.0121 | 0.0003 | 0.1515 |
| No log        | 0.47  | 300  | 4.4480          | 0.0194   | 0.0004    | 0.0194 | 0.0007 | 0.1549 |
| No log        | 0.55  | 350  | 4.4439          | 0.0146   | 0.0002    | 0.0146 | 0.0004 | 0.1430 |
| No log        | 0.63  | 400  | 4.4467          | 0.0146   | 0.0002    | 0.0146 | 0.0004 | 0.1172 |
| No log        | 0.71  | 450  | 4.4418          | 0.0121   | 0.0001    | 0.0121 | 0.0003 | 0.1515 |
| No log        | 0.78  | 500  | 4.4394          | 0.0194   | 0.0004    | 0.0194 | 0.0007 | 0.1549 |
| No log        | 0.86  | 550  | 4.4450          | 0.0121   | 0.0001    | 0.0121 | 0.0003 | 0.1498 |
| No log        | 0.94  | 600  | 4.4342          | 0.0049   | 0.0000    | 0.0049 | 0.0000 | 0.1532 |
| 4.4341        | 1.02  | 650  | 4.4374          | 0.0121   | 0.0001    | 0.0121 | 0.0003 | 0.1061 |
| 4.4341        | 1.1   | 700  | 4.4479          | 0.0121   | 0.0001    | 0.0121 | 0.0003 | 0.1515 |
| 4.4341        | 1.18  | 750  | 4.4399          | 0.0121   | 0.0001    | 0.0121 | 0.0003 | 0.1515 |
| 4.4341        | 1.25  | 800  | 4.4421          | 0.0121   | 0.0001    | 0.0121 | 0.0003 | 0.1515 |
| 4.4341        | 1.33  | 850  | 4.4346          | 0.0121   | 0.0001    | 0.0121 | 0.0003 | 0.1515 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.15.1