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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
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
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: 1.1058
- Accuracy: 0.7748
- Precision: 0.8018
- Recall: 0.7748
- F1: 0.7651
- Binary: 0.8455
## 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: 3e-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 | 0.17 | 50 | 4.2665 | 0.0412 | 0.0107 | 0.0412 | 0.0127 | 0.2923 |
| No log | 0.35 | 100 | 3.9427 | 0.0339 | 0.0016 | 0.0339 | 0.0030 | 0.3172 |
| No log | 0.52 | 150 | 3.7412 | 0.0363 | 0.0025 | 0.0363 | 0.0041 | 0.3206 |
| No log | 0.69 | 200 | 3.6193 | 0.0654 | 0.0238 | 0.0654 | 0.0259 | 0.3373 |
| No log | 0.86 | 250 | 3.4784 | 0.1041 | 0.0460 | 0.1041 | 0.0459 | 0.3663 |
| No log | 1.04 | 300 | 3.3705 | 0.1211 | 0.0602 | 0.1211 | 0.0466 | 0.3789 |
| No log | 1.21 | 350 | 3.2597 | 0.1768 | 0.0811 | 0.1768 | 0.0894 | 0.4218 |
| No log | 1.38 | 400 | 3.1606 | 0.2082 | 0.1867 | 0.2082 | 0.1416 | 0.4424 |
| No log | 1.55 | 450 | 3.0720 | 0.1913 | 0.1490 | 0.1913 | 0.1296 | 0.4312 |
| 3.6525 | 1.73 | 500 | 2.9557 | 0.2446 | 0.1432 | 0.2446 | 0.1609 | 0.4671 |
| 3.6525 | 1.9 | 550 | 2.8287 | 0.2857 | 0.2265 | 0.2857 | 0.2059 | 0.4973 |
| 3.6525 | 2.07 | 600 | 2.7005 | 0.3075 | 0.2103 | 0.3075 | 0.2154 | 0.5136 |
| 3.6525 | 2.24 | 650 | 2.6183 | 0.3414 | 0.2398 | 0.3414 | 0.2486 | 0.5341 |
| 3.6525 | 2.42 | 700 | 2.5133 | 0.3632 | 0.2942 | 0.3632 | 0.2732 | 0.5516 |
| 3.6525 | 2.59 | 750 | 2.4277 | 0.3753 | 0.3322 | 0.3753 | 0.2948 | 0.5615 |
| 3.6525 | 2.76 | 800 | 2.3329 | 0.4092 | 0.3538 | 0.4092 | 0.3338 | 0.5845 |
| 3.6525 | 2.93 | 850 | 2.2465 | 0.4407 | 0.4125 | 0.4407 | 0.3745 | 0.6073 |
| 3.6525 | 3.11 | 900 | 2.1792 | 0.4600 | 0.4329 | 0.4600 | 0.3995 | 0.6203 |
| 3.6525 | 3.28 | 950 | 2.1004 | 0.5109 | 0.4995 | 0.5109 | 0.4540 | 0.6550 |
| 2.6844 | 3.45 | 1000 | 2.0314 | 0.5109 | 0.4799 | 0.5109 | 0.4520 | 0.6557 |
| 2.6844 | 3.62 | 1050 | 1.9561 | 0.5400 | 0.5309 | 0.5400 | 0.4859 | 0.6743 |
| 2.6844 | 3.8 | 1100 | 1.9362 | 0.5472 | 0.5441 | 0.5472 | 0.5066 | 0.6804 |
| 2.6844 | 3.97 | 1150 | 1.8666 | 0.5642 | 0.5647 | 0.5642 | 0.5232 | 0.6930 |
| 2.6844 | 4.14 | 1200 | 1.8204 | 0.5811 | 0.5716 | 0.5811 | 0.5416 | 0.7048 |
| 2.6844 | 4.31 | 1250 | 1.7494 | 0.5908 | 0.6153 | 0.5908 | 0.5618 | 0.7109 |
| 2.6844 | 4.49 | 1300 | 1.6973 | 0.6126 | 0.6062 | 0.6126 | 0.5804 | 0.7291 |
| 2.6844 | 4.66 | 1350 | 1.6615 | 0.6053 | 0.5864 | 0.6053 | 0.5707 | 0.7211 |
| 2.6844 | 4.83 | 1400 | 1.6120 | 0.6295 | 0.6304 | 0.6295 | 0.6000 | 0.7385 |
| 2.6844 | 5.0 | 1450 | 1.5620 | 0.6610 | 0.6605 | 0.6610 | 0.6333 | 0.7615 |
| 2.1096 | 5.18 | 1500 | 1.5330 | 0.6538 | 0.6424 | 0.6538 | 0.6223 | 0.7581 |
| 2.1096 | 5.35 | 1550 | 1.5112 | 0.6707 | 0.6830 | 0.6707 | 0.6484 | 0.7707 |
| 2.1096 | 5.52 | 1600 | 1.4732 | 0.6659 | 0.6793 | 0.6659 | 0.6430 | 0.7685 |
| 2.1096 | 5.69 | 1650 | 1.4420 | 0.6755 | 0.6969 | 0.6755 | 0.6538 | 0.7734 |
| 2.1096 | 5.87 | 1700 | 1.4011 | 0.7094 | 0.7461 | 0.7094 | 0.6929 | 0.7988 |
| 2.1096 | 6.04 | 1750 | 1.3924 | 0.6780 | 0.6835 | 0.6780 | 0.6557 | 0.7760 |
| 2.1096 | 6.21 | 1800 | 1.3604 | 0.7022 | 0.7116 | 0.7022 | 0.6838 | 0.7937 |
| 2.1096 | 6.38 | 1850 | 1.3271 | 0.7070 | 0.7079 | 0.7070 | 0.6882 | 0.7954 |
| 2.1096 | 6.56 | 1900 | 1.3104 | 0.7264 | 0.7338 | 0.7264 | 0.7110 | 0.8099 |
| 2.1096 | 6.73 | 1950 | 1.2804 | 0.7312 | 0.7591 | 0.7312 | 0.7159 | 0.8131 |
| 1.7648 | 6.9 | 2000 | 1.2722 | 0.7312 | 0.7739 | 0.7312 | 0.7185 | 0.8131 |
| 1.7648 | 7.08 | 2050 | 1.2777 | 0.7240 | 0.7581 | 0.7240 | 0.7109 | 0.8099 |
| 1.7648 | 7.25 | 2100 | 1.2319 | 0.7288 | 0.7373 | 0.7288 | 0.7114 | 0.8123 |
| 1.7648 | 7.42 | 2150 | 1.2074 | 0.7433 | 0.7717 | 0.7433 | 0.7317 | 0.8215 |
| 1.7648 | 7.59 | 2200 | 1.2150 | 0.7433 | 0.7850 | 0.7433 | 0.7348 | 0.8235 |
| 1.7648 | 7.77 | 2250 | 1.1787 | 0.7603 | 0.7930 | 0.7603 | 0.7462 | 0.8344 |
| 1.7648 | 7.94 | 2300 | 1.1815 | 0.7676 | 0.7932 | 0.7676 | 0.7576 | 0.8404 |
| 1.7648 | 8.11 | 2350 | 1.1578 | 0.7676 | 0.7972 | 0.7676 | 0.7601 | 0.8404 |
| 1.7648 | 8.28 | 2400 | 1.1605 | 0.7651 | 0.7982 | 0.7651 | 0.7560 | 0.8387 |
| 1.7648 | 8.46 | 2450 | 1.1563 | 0.7627 | 0.7937 | 0.7627 | 0.7548 | 0.8370 |
| 1.5781 | 8.63 | 2500 | 1.1303 | 0.7579 | 0.7847 | 0.7579 | 0.7476 | 0.8337 |
| 1.5781 | 8.8 | 2550 | 1.1217 | 0.7797 | 0.8117 | 0.7797 | 0.7702 | 0.8489 |
| 1.5781 | 8.97 | 2600 | 1.1278 | 0.7724 | 0.8025 | 0.7724 | 0.7640 | 0.8438 |
| 1.5781 | 9.15 | 2650 | 1.1188 | 0.7748 | 0.8022 | 0.7748 | 0.7653 | 0.8455 |
| 1.5781 | 9.32 | 2700 | 1.1161 | 0.7676 | 0.7979 | 0.7676 | 0.7588 | 0.8404 |
| 1.5781 | 9.49 | 2750 | 1.1078 | 0.7748 | 0.8012 | 0.7748 | 0.7650 | 0.8446 |
| 1.5781 | 9.66 | 2800 | 1.1104 | 0.7724 | 0.7973 | 0.7724 | 0.7632 | 0.8429 |
| 1.5781 | 9.84 | 2850 | 1.1058 | 0.7748 | 0.8018 | 0.7748 | 0.7651 | 0.8455 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.15.1
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