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---

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-ref
  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-ref

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: 3.1461
- Accuracy: 0.1671
- Precision: 0.0661
- Recall: 0.1671
- F1: 0.0830
- Binary: 0.4137

## 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: 1e-05

- 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        | 0.19  | 50   | 4.4123          | 0.0377   | 0.0239    | 0.0377 | 0.0213 | 0.2075 |

| No log        | 0.38  | 100  | 4.3574          | 0.0674   | 0.0177    | 0.0674 | 0.0253 | 0.2741 |

| No log        | 0.58  | 150  | 4.2332          | 0.0323   | 0.0017    | 0.0323 | 0.0032 | 0.2884 |

| No log        | 0.77  | 200  | 4.1388          | 0.0647   | 0.0160    | 0.0647 | 0.0182 | 0.3380 |

| No log        | 0.96  | 250  | 4.0567          | 0.0674   | 0.0350    | 0.0674 | 0.0222 | 0.3407 |

| No log        | 1.15  | 300  | 4.0043          | 0.0566   | 0.0114    | 0.0566 | 0.0143 | 0.3221 |

| No log        | 1.34  | 350  | 3.9470          | 0.0485   | 0.0049    | 0.0485 | 0.0080 | 0.3221 |

| No log        | 1.53  | 400  | 3.8803          | 0.0593   | 0.0124    | 0.0593 | 0.0135 | 0.3353 |

| No log        | 1.73  | 450  | 3.8326          | 0.0566   | 0.0057    | 0.0566 | 0.0097 | 0.3323 |

| 4.1711        | 1.92  | 500  | 3.7760          | 0.0566   | 0.0061    | 0.0566 | 0.0103 | 0.3356 |

| 4.1711        | 2.11  | 550  | 3.7454          | 0.0647   | 0.0066    | 0.0647 | 0.0118 | 0.3372 |

| 4.1711        | 2.3   | 600  | 3.7036          | 0.0701   | 0.0075    | 0.0701 | 0.0132 | 0.3429 |

| 4.1711        | 2.49  | 650  | 3.6729          | 0.0728   | 0.0094    | 0.0728 | 0.0161 | 0.3431 |

| 4.1711        | 2.68  | 700  | 3.6306          | 0.0728   | 0.0117    | 0.0728 | 0.0177 | 0.3461 |

| 4.1711        | 2.88  | 750  | 3.6075          | 0.0836   | 0.0155    | 0.0836 | 0.0237 | 0.3536 |

| 4.1711        | 3.07  | 800  | 3.5817          | 0.0943   | 0.0284    | 0.0943 | 0.0285 | 0.3604 |

| 4.1711        | 3.26  | 850  | 3.5607          | 0.0916   | 0.0179    | 0.0916 | 0.0272 | 0.3577 |

| 4.1711        | 3.45  | 900  | 3.5373          | 0.0943   | 0.0214    | 0.0943 | 0.0304 | 0.3588 |

| 4.1711        | 3.64  | 950  | 3.5083          | 0.1078   | 0.0357    | 0.1078 | 0.0464 | 0.3714 |

| 3.7424        | 3.84  | 1000 | 3.4717          | 0.1105   | 0.0512    | 0.1105 | 0.0520 | 0.3765 |

| 3.7424        | 4.03  | 1050 | 3.4619          | 0.1213   | 0.0361    | 0.1213 | 0.0489 | 0.3825 |

| 3.7424        | 4.22  | 1100 | 3.4375          | 0.1240   | 0.0453    | 0.1240 | 0.0554 | 0.3844 |

| 3.7424        | 4.41  | 1150 | 3.4282          | 0.1267   | 0.0390    | 0.1267 | 0.0547 | 0.3849 |

| 3.7424        | 4.6   | 1200 | 3.4076          | 0.1267   | 0.0334    | 0.1267 | 0.0493 | 0.3838 |

| 3.7424        | 4.79  | 1250 | 3.3875          | 0.1078   | 0.0263    | 0.1078 | 0.0388 | 0.3730 |

| 3.7424        | 4.99  | 1300 | 3.3746          | 0.1240   | 0.0547    | 0.1240 | 0.0496 | 0.3822 |

| 3.7424        | 5.18  | 1350 | 3.3459          | 0.1375   | 0.0621    | 0.1375 | 0.0618 | 0.3946 |

| 3.7424        | 5.37  | 1400 | 3.3313          | 0.1375   | 0.0598    | 0.1375 | 0.0650 | 0.3946 |

| 3.7424        | 5.56  | 1450 | 3.3263          | 0.1429   | 0.0556    | 0.1429 | 0.0623 | 0.3951 |

| 3.5358        | 5.75  | 1500 | 3.3100          | 0.1348   | 0.0629    | 0.1348 | 0.0640 | 0.3895 |

| 3.5358        | 5.94  | 1550 | 3.2880          | 0.1402   | 0.0637    | 0.1402 | 0.0641 | 0.3957 |

| 3.5358        | 6.14  | 1600 | 3.2742          | 0.1402   | 0.0628    | 0.1402 | 0.0640 | 0.3965 |

| 3.5358        | 6.33  | 1650 | 3.2605          | 0.1509   | 0.0861    | 0.1509 | 0.0786 | 0.4049 |

| 3.5358        | 6.52  | 1700 | 3.2480          | 0.1429   | 0.0626    | 0.1429 | 0.0663 | 0.3976 |

| 3.5358        | 6.71  | 1750 | 3.2435          | 0.1482   | 0.0575    | 0.1482 | 0.0665 | 0.4030 |

| 3.5358        | 6.9   | 1800 | 3.2324          | 0.1482   | 0.0619    | 0.1482 | 0.0670 | 0.4022 |

| 3.5358        | 7.09  | 1850 | 3.2193          | 0.1563   | 0.0806    | 0.1563 | 0.0799 | 0.4070 |

| 3.5358        | 7.29  | 1900 | 3.2122          | 0.1644   | 0.0825    | 0.1644 | 0.0865 | 0.4119 |

| 3.5358        | 7.48  | 1950 | 3.1995          | 0.1617   | 0.0776    | 0.1617 | 0.0836 | 0.4108 |

| 3.4065        | 7.67  | 2000 | 3.1945          | 0.1617   | 0.0771    | 0.1617 | 0.0837 | 0.4116 |

| 3.4065        | 7.86  | 2050 | 3.1851          | 0.1725   | 0.0832    | 0.1725 | 0.0919 | 0.4191 |

| 3.4065        | 8.05  | 2100 | 3.1805          | 0.1617   | 0.0592    | 0.1617 | 0.0776 | 0.4100 |

| 3.4065        | 8.25  | 2150 | 3.1729          | 0.1617   | 0.0573    | 0.1617 | 0.0762 | 0.4100 |

| 3.4065        | 8.44  | 2200 | 3.1696          | 0.1617   | 0.0571    | 0.1617 | 0.0750 | 0.4100 |

| 3.4065        | 8.63  | 2250 | 3.1638          | 0.1644   | 0.0651    | 0.1644 | 0.0781 | 0.4119 |

| 3.4065        | 8.82  | 2300 | 3.1597          | 0.1590   | 0.0540    | 0.1590 | 0.0735 | 0.4089 |

| 3.4065        | 9.01  | 2350 | 3.1548          | 0.1671   | 0.0688    | 0.1671 | 0.0860 | 0.4137 |

| 3.4065        | 9.2   | 2400 | 3.1540          | 0.1617   | 0.0623    | 0.1617 | 0.0798 | 0.4100 |

| 3.4065        | 9.4   | 2450 | 3.1489          | 0.1644   | 0.0661    | 0.1644 | 0.0820 | 0.4119 |

| 3.3382        | 9.59  | 2500 | 3.1493          | 0.1644   | 0.0706    | 0.1644 | 0.0820 | 0.4119 |

| 3.3382        | 9.78  | 2550 | 3.1464          | 0.1671   | 0.0661    | 0.1671 | 0.0831 | 0.4137 |

| 3.3382        | 9.97  | 2600 | 3.1461          | 0.1671   | 0.0661    | 0.1671 | 0.0830 | 0.4137 |





### Framework versions



- Transformers 4.38.2

- Pytorch 2.3.0

- Datasets 2.19.1

- Tokenizers 0.15.1