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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-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
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