---
license: apache-2.0
base_model: facebook/hubert-base-ls960
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: hubert-classifier-aug
  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. -->

# hubert-classifier-aug

This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.4213
- Accuracy: 0.0148
- Precision: 0.0002
- Recall: 0.0148
- F1: 0.0004
- Binary: 0.1283

## 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: 0.0001
- 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: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Binary |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| No log        | 0.15  | 50   | 4.4256          | 0.0121   | 0.0001    | 0.0121 | 0.0003 | 0.1243 |
| No log        | 0.31  | 100  | 4.4222          | 0.0135   | 0.0002    | 0.0135 | 0.0004 | 0.1333 |
| No log        | 0.46  | 150  | 4.4227          | 0.0148   | 0.0002    | 0.0148 | 0.0004 | 0.1352 |
| No log        | 0.62  | 200  | 4.4228          | 0.0148   | 0.0002    | 0.0148 | 0.0004 | 0.1371 |
| No log        | 0.77  | 250  | 4.4222          | 0.0135   | 0.0002    | 0.0135 | 0.0004 | 0.1264 |
| No log        | 0.92  | 300  | 4.4226          | 0.0148   | 0.0002    | 0.0148 | 0.0004 | 0.1371 |
| No log        | 1.08  | 350  | 4.4219          | 0.0148   | 0.0002    | 0.0148 | 0.0004 | 0.1283 |
| No log        | 1.23  | 400  | 4.4221          | 0.0148   | 0.0002    | 0.0148 | 0.0004 | 0.1261 |
| No log        | 1.39  | 450  | 4.4221          | 0.0148   | 0.0002    | 0.0148 | 0.0004 | 0.1283 |
| 4.4251        | 1.54  | 500  | 4.4213          | 0.0148   | 0.0002    | 0.0148 | 0.0004 | 0.1283 |
| 4.4251        | 1.69  | 550  | 4.4215          | 0.0148   | 0.0002    | 0.0148 | 0.0004 | 0.1352 |
| 4.4251        | 1.85  | 600  | 4.4210          | 0.0135   | 0.0002    | 0.0135 | 0.0004 | 0.1333 |
| 4.4251        | 2.0   | 650  | 4.4214          | 0.0148   | 0.0002    | 0.0148 | 0.0004 | 0.1371 |
| 4.4251        | 2.16  | 700  | 4.4229          | 0.0148   | 0.0002    | 0.0148 | 0.0004 | 0.1371 |
| 4.4251        | 2.31  | 750  | 4.4208          | 0.0148   | 0.0002    | 0.0148 | 0.0004 | 0.1283 |
| 4.4251        | 2.47  | 800  | 4.4214          | 0.0148   | 0.0002    | 0.0148 | 0.0004 | 0.1283 |
| 4.4251        | 2.62  | 850  | 4.4207          | 0.0148   | 0.0002    | 0.0148 | 0.0004 | 0.1371 |


### Framework versions

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