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---
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license: apache-2.0
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base_model: facebook/hubert-base-ls960
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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- precision
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- recall
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- f1
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model-index:
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- name: hubert-classifier-aug-fold-9
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# hubert-classifier-aug-fold-9
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This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 4.4196
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- Accuracy: 0.0162
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- Precision: 0.0003
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- Recall: 0.0162
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- F1: 0.0005
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- Binary: 0.1388
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0001
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 128
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 30
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Binary |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
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| No log | 0.19 | 50 | 4.4284 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
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| No log | 0.38 | 100 | 4.4249 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1296 |
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| No log | 0.58 | 150 | 4.4280 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1353 |
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| No log | 0.77 | 200 | 4.4229 | 0.0108 | 0.0001 | 0.0108 | 0.0002 | 0.1315 |
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| No log | 0.96 | 250 | 4.4254 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
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| 4.4398 | 1.15 | 300 | 4.4262 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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| 4.4398 | 1.34 | 350 | 4.4237 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
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| 4.4398 | 1.53 | 400 | 4.4237 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1315 |
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| 4.4398 | 1.73 | 450 | 4.4233 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1315 |
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| 4.4398 | 1.92 | 500 | 4.4241 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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| 4.4196 | 2.11 | 550 | 4.4240 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
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| 4.4196 | 2.3 | 600 | 4.4236 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1213 |
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| 4.4196 | 2.49 | 650 | 4.4227 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
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| 4.4196 | 2.68 | 700 | 4.4235 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1353 |
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| 4.4196 | 2.88 | 750 | 4.4234 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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| 4.4189 | 3.07 | 800 | 4.4234 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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| 4.4189 | 3.26 | 850 | 4.4240 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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| 4.4189 | 3.45 | 900 | 4.4230 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
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| 4.4189 | 3.64 | 950 | 4.4230 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
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| 4.4189 | 3.84 | 1000 | 4.4225 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1315 |
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| 4.4185 | 4.03 | 1050 | 4.4233 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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| 4.4185 | 4.22 | 1100 | 4.4229 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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| 4.4185 | 4.41 | 1150 | 4.4226 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
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| 4.4185 | 4.6 | 1200 | 4.4223 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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| 4.4185 | 4.79 | 1250 | 4.4234 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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| 4.4185 | 4.99 | 1300 | 4.4225 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1302 |
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| 4.4351 | 5.18 | 1350 | 4.4225 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
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| 4.4351 | 5.37 | 1400 | 4.4231 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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| 4.4351 | 5.56 | 1450 | 4.4233 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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| 4.4351 | 5.75 | 1500 | 4.4220 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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| 4.4351 | 5.94 | 1550 | 4.4217 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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| 4.4178 | 6.14 | 1600 | 4.4225 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
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| 4.4178 | 6.33 | 1650 | 4.4217 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
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| 4.4178 | 6.52 | 1700 | 4.4222 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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| 4.4178 | 6.71 | 1750 | 4.4226 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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| 4.4178 | 6.9 | 1800 | 4.4222 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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| 4.4175 | 7.09 | 1850 | 4.4232 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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| 4.4175 | 7.29 | 1900 | 4.4235 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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| 4.4175 | 7.48 | 1950 | 4.4231 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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| 4.4175 | 7.67 | 2000 | 4.4235 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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### Framework versions
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- Transformers 4.38.2
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- Pytorch 2.3.0
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- Datasets 2.19.1
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- Tokenizers 0.15.1
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