---
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-fold-0
  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-fold-0

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: 0.5649
- Accuracy: 0.8679
- Precision: 0.8791
- Recall: 0.8679
- F1: 0.8667
- Binary: 0.9082

## 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: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Binary |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| No log        | 0.13  | 50   | 3.8775          | 0.0512   | 0.0179    | 0.0512 | 0.0103 | 0.3205 |
| No log        | 0.27  | 100  | 3.4967          | 0.0526   | 0.0224    | 0.0526 | 0.0156 | 0.3290 |
| No log        | 0.4   | 150  | 3.2301          | 0.0970   | 0.0222    | 0.0970 | 0.0291 | 0.3659 |
| No log        | 0.54  | 200  | 3.0363          | 0.1361   | 0.0437    | 0.1361 | 0.0582 | 0.3920 |
| No log        | 0.67  | 250  | 2.7181          | 0.2385   | 0.1581    | 0.2385 | 0.1495 | 0.4625 |
| No log        | 0.81  | 300  | 2.3875          | 0.3019   | 0.1996    | 0.3019 | 0.2063 | 0.5101 |
| No log        | 0.94  | 350  | 2.1936          | 0.3895   | 0.3562    | 0.3895 | 0.3154 | 0.5706 |
| 3.2299        | 1.08  | 400  | 1.9583          | 0.4555   | 0.3983    | 0.4555 | 0.3887 | 0.6168 |
| 3.2299        | 1.21  | 450  | 1.7394          | 0.5      | 0.4895    | 0.5    | 0.4446 | 0.6497 |
| 3.2299        | 1.35  | 500  | 1.5140          | 0.6065   | 0.6096    | 0.6065 | 0.5686 | 0.7244 |
| 3.2299        | 1.48  | 550  | 1.3457          | 0.6361   | 0.6197    | 0.6361 | 0.5975 | 0.7460 |
| 3.2299        | 1.62  | 600  | 1.2439          | 0.6644   | 0.6718    | 0.6644 | 0.6320 | 0.7644 |
| 3.2299        | 1.75  | 650  | 1.0984          | 0.7035   | 0.6871    | 0.7035 | 0.6718 | 0.7927 |
| 3.2299        | 1.89  | 700  | 1.0212          | 0.7480   | 0.7557    | 0.7480 | 0.7313 | 0.8225 |
| 1.7016        | 2.02  | 750  | 0.9402          | 0.7412   | 0.7504    | 0.7412 | 0.7217 | 0.8187 |
| 1.7016        | 2.16  | 800  | 0.9117          | 0.7412   | 0.7498    | 0.7412 | 0.7285 | 0.8178 |
| 1.7016        | 2.29  | 850  | 0.8195          | 0.7763   | 0.8018    | 0.7763 | 0.7704 | 0.8439 |
| 1.7016        | 2.43  | 900  | 0.7736          | 0.7898   | 0.8195    | 0.7898 | 0.7817 | 0.8531 |
| 1.7016        | 2.56  | 950  | 0.7723          | 0.7965   | 0.8227    | 0.7965 | 0.7855 | 0.8597 |
| 1.7016        | 2.7   | 1000 | 0.7737          | 0.7857   | 0.8131    | 0.7857 | 0.7780 | 0.8512 |
| 1.7016        | 2.83  | 1050 | 0.6884          | 0.8113   | 0.8281    | 0.8113 | 0.8057 | 0.8679 |
| 1.7016        | 2.97  | 1100 | 0.7205          | 0.8032   | 0.8297    | 0.8032 | 0.7987 | 0.8646 |
| 1.0665        | 3.1   | 1150 | 0.6086          | 0.8342   | 0.8483    | 0.8342 | 0.8297 | 0.8864 |
| 1.0665        | 3.24  | 1200 | 0.5848          | 0.8531   | 0.8690    | 0.8531 | 0.8503 | 0.8991 |
| 1.0665        | 3.37  | 1250 | 0.6821          | 0.8369   | 0.8551    | 0.8369 | 0.8315 | 0.8861 |
| 1.0665        | 3.51  | 1300 | 0.6050          | 0.8315   | 0.8491    | 0.8315 | 0.8287 | 0.8833 |
| 1.0665        | 3.64  | 1350 | 0.5871          | 0.8504   | 0.8708    | 0.8504 | 0.8477 | 0.8960 |
| 1.0665        | 3.78  | 1400 | 0.6485          | 0.8329   | 0.8560    | 0.8329 | 0.8298 | 0.8837 |
| 1.0665        | 3.91  | 1450 | 0.5727          | 0.8518   | 0.8673    | 0.8518 | 0.8484 | 0.8970 |
| 0.7598        | 4.05  | 1500 | 0.5555          | 0.8652   | 0.8752    | 0.8652 | 0.8623 | 0.9069 |
| 0.7598        | 4.18  | 1550 | 0.5407          | 0.8585   | 0.8702    | 0.8585 | 0.8558 | 0.9031 |
| 0.7598        | 4.32  | 1600 | 0.5282          | 0.8679   | 0.8813    | 0.8679 | 0.8658 | 0.9082 |
| 0.7598        | 4.45  | 1650 | 0.5557          | 0.8558   | 0.8693    | 0.8558 | 0.8540 | 0.9008 |
| 0.7598        | 4.59  | 1700 | 0.5832          | 0.8693   | 0.8866    | 0.8693 | 0.8686 | 0.9097 |
| 0.7598        | 4.72  | 1750 | 0.5623          | 0.8720   | 0.8818    | 0.8720 | 0.8707 | 0.9097 |
| 0.7598        | 4.86  | 1800 | 0.5598          | 0.8612   | 0.8811    | 0.8612 | 0.8614 | 0.9040 |
| 0.7598        | 4.99  | 1850 | 0.6415          | 0.8504   | 0.8627    | 0.8504 | 0.8479 | 0.8970 |
| 0.5874        | 5.12  | 1900 | 0.5866          | 0.8652   | 0.8788    | 0.8652 | 0.8620 | 0.9074 |
| 0.5874        | 5.26  | 1950 | 0.6092          | 0.8612   | 0.8725    | 0.8612 | 0.8603 | 0.9036 |
| 0.5874        | 5.39  | 2000 | 0.5544          | 0.8733   | 0.8870    | 0.8733 | 0.8730 | 0.9125 |
| 0.5874        | 5.53  | 2050 | 0.5478          | 0.8598   | 0.8726    | 0.8598 | 0.8591 | 0.9026 |
| 0.5874        | 5.66  | 2100 | 0.5683          | 0.8733   | 0.8908    | 0.8733 | 0.8713 | 0.9116 |
| 0.5874        | 5.8   | 2150 | 0.5644          | 0.8679   | 0.8782    | 0.8679 | 0.8643 | 0.9102 |
| 0.5874        | 5.93  | 2200 | 0.5471          | 0.8666   | 0.8759    | 0.8666 | 0.8639 | 0.9093 |
| 0.4963        | 6.07  | 2250 | 0.6050          | 0.8612   | 0.8757    | 0.8612 | 0.8603 | 0.9044 |
| 0.4963        | 6.2   | 2300 | 0.5908          | 0.8625   | 0.8770    | 0.8625 | 0.8613 | 0.9054 |
| 0.4963        | 6.34  | 2350 | 0.5994          | 0.8598   | 0.8773    | 0.8598 | 0.8589 | 0.9036 |


### Framework versions

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