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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-fold-9
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-9
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: 4.4196
- Accuracy: 0.0162
- Precision: 0.0003
- Recall: 0.0162
- F1: 0.0005
- Binary: 0.1388
## 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 | 0.19 | 50 | 4.4284 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
| No log | 0.38 | 100 | 4.4249 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1296 |
| No log | 0.58 | 150 | 4.4280 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1353 |
| No log | 0.77 | 200 | 4.4229 | 0.0108 | 0.0001 | 0.0108 | 0.0002 | 0.1315 |
| No log | 0.96 | 250 | 4.4254 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
| 4.4398 | 1.15 | 300 | 4.4262 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
| 4.4398 | 1.34 | 350 | 4.4237 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
| 4.4398 | 1.53 | 400 | 4.4237 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1315 |
| 4.4398 | 1.73 | 450 | 4.4233 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1315 |
| 4.4398 | 1.92 | 500 | 4.4241 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
| 4.4196 | 2.11 | 550 | 4.4240 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
| 4.4196 | 2.3 | 600 | 4.4236 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1213 |
| 4.4196 | 2.49 | 650 | 4.4227 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
| 4.4196 | 2.68 | 700 | 4.4235 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1353 |
| 4.4196 | 2.88 | 750 | 4.4234 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
| 4.4189 | 3.07 | 800 | 4.4234 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
| 4.4189 | 3.26 | 850 | 4.4240 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
| 4.4189 | 3.45 | 900 | 4.4230 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
| 4.4189 | 3.64 | 950 | 4.4230 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
| 4.4189 | 3.84 | 1000 | 4.4225 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1315 |
| 4.4185 | 4.03 | 1050 | 4.4233 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
| 4.4185 | 4.22 | 1100 | 4.4229 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
| 4.4185 | 4.41 | 1150 | 4.4226 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
| 4.4185 | 4.6 | 1200 | 4.4223 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
| 4.4185 | 4.79 | 1250 | 4.4234 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
| 4.4185 | 4.99 | 1300 | 4.4225 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1302 |
| 4.4351 | 5.18 | 1350 | 4.4225 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
| 4.4351 | 5.37 | 1400 | 4.4231 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
| 4.4351 | 5.56 | 1450 | 4.4233 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
| 4.4351 | 5.75 | 1500 | 4.4220 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
| 4.4351 | 5.94 | 1550 | 4.4217 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
| 4.4178 | 6.14 | 1600 | 4.4225 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
| 4.4178 | 6.33 | 1650 | 4.4217 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
| 4.4178 | 6.52 | 1700 | 4.4222 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
| 4.4178 | 6.71 | 1750 | 4.4226 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
| 4.4178 | 6.9 | 1800 | 4.4222 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
| 4.4175 | 7.09 | 1850 | 4.4232 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
| 4.4175 | 7.29 | 1900 | 4.4235 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
| 4.4175 | 7.48 | 1950 | 4.4231 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
| 4.4175 | 7.67 | 2000 | 4.4235 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.15.1
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