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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
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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
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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: 0.5817
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- Accuracy: 0.8356
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- Precision: 0.8647
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- Recall: 0.8356
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- F1: 0.8286
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- Binary: 0.8852
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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: 100
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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 | 4.0265 | 0.0404 | 0.0071 | 0.0404 | 0.0079 | 0.3011 |
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| No log | 0.38 | 100 | 3.5428 | 0.0485 | 0.0048 | 0.0485 | 0.0080 | 0.3286 |
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| No log | 0.58 | 150 | 3.3405 | 0.0836 | 0.0175 | 0.0836 | 0.0251 | 0.3555 |
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| No log | 0.77 | 200 | 3.2238 | 0.0809 | 0.0143 | 0.0809 | 0.0216 | 0.3512 |
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| No log | 0.96 | 250 | 3.1041 | 0.0728 | 0.0135 | 0.0728 | 0.0202 | 0.3493 |
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| No log | 1.15 | 300 | 2.9851 | 0.1078 | 0.0517 | 0.1078 | 0.0480 | 0.3730 |
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| No log | 1.34 | 350 | 2.8525 | 0.1779 | 0.0780 | 0.1779 | 0.0876 | 0.4197 |
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| No log | 1.53 | 400 | 2.7647 | 0.1752 | 0.1108 | 0.1752 | 0.1064 | 0.4221 |
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| No log | 1.73 | 450 | 2.5521 | 0.2291 | 0.1539 | 0.2291 | 0.1450 | 0.4547 |
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| 3.3693 | 1.92 | 500 | 2.4121 | 0.2372 | 0.1655 | 0.2372 | 0.1618 | 0.4668 |
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| 3.3693 | 2.11 | 550 | 2.2312 | 0.2992 | 0.2286 | 0.2992 | 0.2185 | 0.5081 |
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| 3.3693 | 2.3 | 600 | 2.0065 | 0.4124 | 0.2985 | 0.4124 | 0.3133 | 0.5865 |
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| 3.3693 | 2.49 | 650 | 1.8816 | 0.4313 | 0.3359 | 0.4313 | 0.3461 | 0.6013 |
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| 3.3693 | 2.68 | 700 | 1.8069 | 0.4906 | 0.4702 | 0.4906 | 0.4308 | 0.6426 |
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| 3.3693 | 2.88 | 750 | 1.6310 | 0.5418 | 0.4981 | 0.5418 | 0.4728 | 0.6803 |
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| 3.3693 | 3.07 | 800 | 1.5274 | 0.5580 | 0.5219 | 0.5580 | 0.5002 | 0.6908 |
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| 3.3693 | 3.26 | 850 | 1.3417 | 0.6415 | 0.6343 | 0.6415 | 0.5980 | 0.7544 |
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| 3.3693 | 3.45 | 900 | 1.3121 | 0.6173 | 0.6059 | 0.6173 | 0.5690 | 0.7334 |
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| 3.3693 | 3.64 | 950 | 1.2298 | 0.6523 | 0.6501 | 0.6523 | 0.6183 | 0.7577 |
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| 2.2303 | 3.84 | 1000 | 1.1427 | 0.7197 | 0.7323 | 0.7197 | 0.6897 | 0.8040 |
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| 2.2303 | 4.03 | 1050 | 1.0947 | 0.6765 | 0.6891 | 0.6765 | 0.6387 | 0.7741 |
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| 2.2303 | 4.22 | 1100 | 1.1233 | 0.6361 | 0.6473 | 0.6361 | 0.6054 | 0.7447 |
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| 2.2303 | 4.41 | 1150 | 0.9765 | 0.7547 | 0.7606 | 0.7547 | 0.7331 | 0.8296 |
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| 2.2303 | 4.6 | 1200 | 0.9206 | 0.7547 | 0.7546 | 0.7547 | 0.7270 | 0.8305 |
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| 2.2303 | 4.79 | 1250 | 0.8658 | 0.7790 | 0.7868 | 0.7790 | 0.7625 | 0.8456 |
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| 2.2303 | 4.99 | 1300 | 0.8961 | 0.7385 | 0.7576 | 0.7385 | 0.7254 | 0.8186 |
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| 2.2303 | 5.18 | 1350 | 0.7709 | 0.8005 | 0.8185 | 0.8005 | 0.7912 | 0.8596 |
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| 2.2303 | 5.37 | 1400 | 0.7638 | 0.7925 | 0.8118 | 0.7925 | 0.7760 | 0.8547 |
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| 2.2303 | 5.56 | 1450 | 0.7085 | 0.8194 | 0.8415 | 0.8194 | 0.8081 | 0.8741 |
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| 1.6078 | 5.75 | 1500 | 0.7230 | 0.7790 | 0.8195 | 0.7790 | 0.7739 | 0.8456 |
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| 1.6078 | 5.94 | 1550 | 0.6475 | 0.7951 | 0.8174 | 0.7951 | 0.7813 | 0.8558 |
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| 1.6078 | 6.14 | 1600 | 0.6910 | 0.7844 | 0.8082 | 0.7844 | 0.7686 | 0.8504 |
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| 1.6078 | 6.33 | 1650 | 0.6233 | 0.8194 | 0.8462 | 0.8194 | 0.8111 | 0.8730 |
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| 1.6078 | 6.52 | 1700 | 0.6599 | 0.8059 | 0.8429 | 0.8059 | 0.8031 | 0.8633 |
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| 1.6078 | 6.71 | 1750 | 0.6999 | 0.7925 | 0.8119 | 0.7925 | 0.7751 | 0.8550 |
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| 1.6078 | 6.9 | 1800 | 0.6271 | 0.8140 | 0.8266 | 0.8140 | 0.8018 | 0.8701 |
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| 1.6078 | 7.09 | 1850 | 0.5545 | 0.8329 | 0.8557 | 0.8329 | 0.8288 | 0.8822 |
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| 1.6078 | 7.29 | 1900 | 0.6343 | 0.8032 | 0.8179 | 0.8032 | 0.7930 | 0.8625 |
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| 1.6078 | 7.48 | 1950 | 0.6007 | 0.8194 | 0.8447 | 0.8194 | 0.8136 | 0.8728 |
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| 1.2974 | 7.67 | 2000 | 0.5878 | 0.8356 | 0.8674 | 0.8356 | 0.8333 | 0.8841 |
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| 1.2974 | 7.86 | 2050 | 0.6410 | 0.8086 | 0.8344 | 0.8086 | 0.8011 | 0.8652 |
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| 1.2974 | 8.05 | 2100 | 0.6430 | 0.8005 | 0.8201 | 0.8005 | 0.7894 | 0.8598 |
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| 1.2974 | 8.25 | 2150 | 0.5540 | 0.8221 | 0.8414 | 0.8221 | 0.8177 | 0.8747 |
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| 1.2974 | 8.44 | 2200 | 0.5511 | 0.8356 | 0.8635 | 0.8356 | 0.8317 | 0.8833 |
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| 1.2974 | 8.63 | 2250 | 0.5817 | 0.8356 | 0.8647 | 0.8356 | 0.8286 | 0.8852 |
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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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