hubert-classifier-aug-ref / README.md

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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-ref
	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-ref

	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: 3.1461
	- Accuracy: 0.1671
	- Precision: 0.0661
	- Recall: 0.1671
	- F1: 0.0830
	- Binary: 0.4137

	## 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: 1e-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 \| 0.19 \| 50 \| 4.4123 \| 0.0377 \| 0.0239 \| 0.0377 \| 0.0213 \| 0.2075 \|
	\| No log \| 0.38 \| 100 \| 4.3574 \| 0.0674 \| 0.0177 \| 0.0674 \| 0.0253 \| 0.2741 \|
	\| No log \| 0.58 \| 150 \| 4.2332 \| 0.0323 \| 0.0017 \| 0.0323 \| 0.0032 \| 0.2884 \|
	\| No log \| 0.77 \| 200 \| 4.1388 \| 0.0647 \| 0.0160 \| 0.0647 \| 0.0182 \| 0.3380 \|
	\| No log \| 0.96 \| 250 \| 4.0567 \| 0.0674 \| 0.0350 \| 0.0674 \| 0.0222 \| 0.3407 \|
	\| No log \| 1.15 \| 300 \| 4.0043 \| 0.0566 \| 0.0114 \| 0.0566 \| 0.0143 \| 0.3221 \|
	\| No log \| 1.34 \| 350 \| 3.9470 \| 0.0485 \| 0.0049 \| 0.0485 \| 0.0080 \| 0.3221 \|
	\| No log \| 1.53 \| 400 \| 3.8803 \| 0.0593 \| 0.0124 \| 0.0593 \| 0.0135 \| 0.3353 \|
	\| No log \| 1.73 \| 450 \| 3.8326 \| 0.0566 \| 0.0057 \| 0.0566 \| 0.0097 \| 0.3323 \|
	\| 4.1711 \| 1.92 \| 500 \| 3.7760 \| 0.0566 \| 0.0061 \| 0.0566 \| 0.0103 \| 0.3356 \|
	\| 4.1711 \| 2.11 \| 550 \| 3.7454 \| 0.0647 \| 0.0066 \| 0.0647 \| 0.0118 \| 0.3372 \|
	\| 4.1711 \| 2.3 \| 600 \| 3.7036 \| 0.0701 \| 0.0075 \| 0.0701 \| 0.0132 \| 0.3429 \|
	\| 4.1711 \| 2.49 \| 650 \| 3.6729 \| 0.0728 \| 0.0094 \| 0.0728 \| 0.0161 \| 0.3431 \|
	\| 4.1711 \| 2.68 \| 700 \| 3.6306 \| 0.0728 \| 0.0117 \| 0.0728 \| 0.0177 \| 0.3461 \|
	\| 4.1711 \| 2.88 \| 750 \| 3.6075 \| 0.0836 \| 0.0155 \| 0.0836 \| 0.0237 \| 0.3536 \|
	\| 4.1711 \| 3.07 \| 800 \| 3.5817 \| 0.0943 \| 0.0284 \| 0.0943 \| 0.0285 \| 0.3604 \|
	\| 4.1711 \| 3.26 \| 850 \| 3.5607 \| 0.0916 \| 0.0179 \| 0.0916 \| 0.0272 \| 0.3577 \|
	\| 4.1711 \| 3.45 \| 900 \| 3.5373 \| 0.0943 \| 0.0214 \| 0.0943 \| 0.0304 \| 0.3588 \|
	\| 4.1711 \| 3.64 \| 950 \| 3.5083 \| 0.1078 \| 0.0357 \| 0.1078 \| 0.0464 \| 0.3714 \|
	\| 3.7424 \| 3.84 \| 1000 \| 3.4717 \| 0.1105 \| 0.0512 \| 0.1105 \| 0.0520 \| 0.3765 \|
	\| 3.7424 \| 4.03 \| 1050 \| 3.4619 \| 0.1213 \| 0.0361 \| 0.1213 \| 0.0489 \| 0.3825 \|
	\| 3.7424 \| 4.22 \| 1100 \| 3.4375 \| 0.1240 \| 0.0453 \| 0.1240 \| 0.0554 \| 0.3844 \|
	\| 3.7424 \| 4.41 \| 1150 \| 3.4282 \| 0.1267 \| 0.0390 \| 0.1267 \| 0.0547 \| 0.3849 \|
	\| 3.7424 \| 4.6 \| 1200 \| 3.4076 \| 0.1267 \| 0.0334 \| 0.1267 \| 0.0493 \| 0.3838 \|
	\| 3.7424 \| 4.79 \| 1250 \| 3.3875 \| 0.1078 \| 0.0263 \| 0.1078 \| 0.0388 \| 0.3730 \|
	\| 3.7424 \| 4.99 \| 1300 \| 3.3746 \| 0.1240 \| 0.0547 \| 0.1240 \| 0.0496 \| 0.3822 \|
	\| 3.7424 \| 5.18 \| 1350 \| 3.3459 \| 0.1375 \| 0.0621 \| 0.1375 \| 0.0618 \| 0.3946 \|
	\| 3.7424 \| 5.37 \| 1400 \| 3.3313 \| 0.1375 \| 0.0598 \| 0.1375 \| 0.0650 \| 0.3946 \|
	\| 3.7424 \| 5.56 \| 1450 \| 3.3263 \| 0.1429 \| 0.0556 \| 0.1429 \| 0.0623 \| 0.3951 \|
	\| 3.5358 \| 5.75 \| 1500 \| 3.3100 \| 0.1348 \| 0.0629 \| 0.1348 \| 0.0640 \| 0.3895 \|
	\| 3.5358 \| 5.94 \| 1550 \| 3.2880 \| 0.1402 \| 0.0637 \| 0.1402 \| 0.0641 \| 0.3957 \|
	\| 3.5358 \| 6.14 \| 1600 \| 3.2742 \| 0.1402 \| 0.0628 \| 0.1402 \| 0.0640 \| 0.3965 \|
	\| 3.5358 \| 6.33 \| 1650 \| 3.2605 \| 0.1509 \| 0.0861 \| 0.1509 \| 0.0786 \| 0.4049 \|
	\| 3.5358 \| 6.52 \| 1700 \| 3.2480 \| 0.1429 \| 0.0626 \| 0.1429 \| 0.0663 \| 0.3976 \|
	\| 3.5358 \| 6.71 \| 1750 \| 3.2435 \| 0.1482 \| 0.0575 \| 0.1482 \| 0.0665 \| 0.4030 \|
	\| 3.5358 \| 6.9 \| 1800 \| 3.2324 \| 0.1482 \| 0.0619 \| 0.1482 \| 0.0670 \| 0.4022 \|
	\| 3.5358 \| 7.09 \| 1850 \| 3.2193 \| 0.1563 \| 0.0806 \| 0.1563 \| 0.0799 \| 0.4070 \|
	\| 3.5358 \| 7.29 \| 1900 \| 3.2122 \| 0.1644 \| 0.0825 \| 0.1644 \| 0.0865 \| 0.4119 \|
	\| 3.5358 \| 7.48 \| 1950 \| 3.1995 \| 0.1617 \| 0.0776 \| 0.1617 \| 0.0836 \| 0.4108 \|
	\| 3.4065 \| 7.67 \| 2000 \| 3.1945 \| 0.1617 \| 0.0771 \| 0.1617 \| 0.0837 \| 0.4116 \|
	\| 3.4065 \| 7.86 \| 2050 \| 3.1851 \| 0.1725 \| 0.0832 \| 0.1725 \| 0.0919 \| 0.4191 \|
	\| 3.4065 \| 8.05 \| 2100 \| 3.1805 \| 0.1617 \| 0.0592 \| 0.1617 \| 0.0776 \| 0.4100 \|
	\| 3.4065 \| 8.25 \| 2150 \| 3.1729 \| 0.1617 \| 0.0573 \| 0.1617 \| 0.0762 \| 0.4100 \|
	\| 3.4065 \| 8.44 \| 2200 \| 3.1696 \| 0.1617 \| 0.0571 \| 0.1617 \| 0.0750 \| 0.4100 \|
	\| 3.4065 \| 8.63 \| 2250 \| 3.1638 \| 0.1644 \| 0.0651 \| 0.1644 \| 0.0781 \| 0.4119 \|
	\| 3.4065 \| 8.82 \| 2300 \| 3.1597 \| 0.1590 \| 0.0540 \| 0.1590 \| 0.0735 \| 0.4089 \|
	\| 3.4065 \| 9.01 \| 2350 \| 3.1548 \| 0.1671 \| 0.0688 \| 0.1671 \| 0.0860 \| 0.4137 \|
	\| 3.4065 \| 9.2 \| 2400 \| 3.1540 \| 0.1617 \| 0.0623 \| 0.1617 \| 0.0798 \| 0.4100 \|
	\| 3.4065 \| 9.4 \| 2450 \| 3.1489 \| 0.1644 \| 0.0661 \| 0.1644 \| 0.0820 \| 0.4119 \|
	\| 3.3382 \| 9.59 \| 2500 \| 3.1493 \| 0.1644 \| 0.0706 \| 0.1644 \| 0.0820 \| 0.4119 \|
	\| 3.3382 \| 9.78 \| 2550 \| 3.1464 \| 0.1671 \| 0.0661 \| 0.1671 \| 0.0831 \| 0.4137 \|
	\| 3.3382 \| 9.97 \| 2600 \| 3.1461 \| 0.1671 \| 0.0661 \| 0.1671 \| 0.0830 \| 0.4137 \|


	### Framework versions

	- Transformers 4.38.2
	- Pytorch 2.3.0
	- Datasets 2.19.1
	- Tokenizers 0.15.1

	---
	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-ref
	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-ref

	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: 3.1461
	- Accuracy: 0.1671
	- Precision: 0.0661
	- Recall: 0.1671
	- F1: 0.0830
	- Binary: 0.4137

	## 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: 1e-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 \| 0.19 \| 50 \| 4.4123 \| 0.0377 \| 0.0239 \| 0.0377 \| 0.0213 \| 0.2075 \|
	\| No log \| 0.38 \| 100 \| 4.3574 \| 0.0674 \| 0.0177 \| 0.0674 \| 0.0253 \| 0.2741 \|
	\| No log \| 0.58 \| 150 \| 4.2332 \| 0.0323 \| 0.0017 \| 0.0323 \| 0.0032 \| 0.2884 \|
	\| No log \| 0.77 \| 200 \| 4.1388 \| 0.0647 \| 0.0160 \| 0.0647 \| 0.0182 \| 0.3380 \|
	\| No log \| 0.96 \| 250 \| 4.0567 \| 0.0674 \| 0.0350 \| 0.0674 \| 0.0222 \| 0.3407 \|
	\| No log \| 1.15 \| 300 \| 4.0043 \| 0.0566 \| 0.0114 \| 0.0566 \| 0.0143 \| 0.3221 \|
	\| No log \| 1.34 \| 350 \| 3.9470 \| 0.0485 \| 0.0049 \| 0.0485 \| 0.0080 \| 0.3221 \|
	\| No log \| 1.53 \| 400 \| 3.8803 \| 0.0593 \| 0.0124 \| 0.0593 \| 0.0135 \| 0.3353 \|
	\| No log \| 1.73 \| 450 \| 3.8326 \| 0.0566 \| 0.0057 \| 0.0566 \| 0.0097 \| 0.3323 \|
	\| 4.1711 \| 1.92 \| 500 \| 3.7760 \| 0.0566 \| 0.0061 \| 0.0566 \| 0.0103 \| 0.3356 \|
	\| 4.1711 \| 2.11 \| 550 \| 3.7454 \| 0.0647 \| 0.0066 \| 0.0647 \| 0.0118 \| 0.3372 \|
	\| 4.1711 \| 2.3 \| 600 \| 3.7036 \| 0.0701 \| 0.0075 \| 0.0701 \| 0.0132 \| 0.3429 \|
	\| 4.1711 \| 2.49 \| 650 \| 3.6729 \| 0.0728 \| 0.0094 \| 0.0728 \| 0.0161 \| 0.3431 \|
	\| 4.1711 \| 2.68 \| 700 \| 3.6306 \| 0.0728 \| 0.0117 \| 0.0728 \| 0.0177 \| 0.3461 \|
	\| 4.1711 \| 2.88 \| 750 \| 3.6075 \| 0.0836 \| 0.0155 \| 0.0836 \| 0.0237 \| 0.3536 \|
	\| 4.1711 \| 3.07 \| 800 \| 3.5817 \| 0.0943 \| 0.0284 \| 0.0943 \| 0.0285 \| 0.3604 \|
	\| 4.1711 \| 3.26 \| 850 \| 3.5607 \| 0.0916 \| 0.0179 \| 0.0916 \| 0.0272 \| 0.3577 \|
	\| 4.1711 \| 3.45 \| 900 \| 3.5373 \| 0.0943 \| 0.0214 \| 0.0943 \| 0.0304 \| 0.3588 \|
	\| 4.1711 \| 3.64 \| 950 \| 3.5083 \| 0.1078 \| 0.0357 \| 0.1078 \| 0.0464 \| 0.3714 \|
	\| 3.7424 \| 3.84 \| 1000 \| 3.4717 \| 0.1105 \| 0.0512 \| 0.1105 \| 0.0520 \| 0.3765 \|
	\| 3.7424 \| 4.03 \| 1050 \| 3.4619 \| 0.1213 \| 0.0361 \| 0.1213 \| 0.0489 \| 0.3825 \|
	\| 3.7424 \| 4.22 \| 1100 \| 3.4375 \| 0.1240 \| 0.0453 \| 0.1240 \| 0.0554 \| 0.3844 \|
	\| 3.7424 \| 4.41 \| 1150 \| 3.4282 \| 0.1267 \| 0.0390 \| 0.1267 \| 0.0547 \| 0.3849 \|
	\| 3.7424 \| 4.6 \| 1200 \| 3.4076 \| 0.1267 \| 0.0334 \| 0.1267 \| 0.0493 \| 0.3838 \|
	\| 3.7424 \| 4.79 \| 1250 \| 3.3875 \| 0.1078 \| 0.0263 \| 0.1078 \| 0.0388 \| 0.3730 \|
	\| 3.7424 \| 4.99 \| 1300 \| 3.3746 \| 0.1240 \| 0.0547 \| 0.1240 \| 0.0496 \| 0.3822 \|
	\| 3.7424 \| 5.18 \| 1350 \| 3.3459 \| 0.1375 \| 0.0621 \| 0.1375 \| 0.0618 \| 0.3946 \|
	\| 3.7424 \| 5.37 \| 1400 \| 3.3313 \| 0.1375 \| 0.0598 \| 0.1375 \| 0.0650 \| 0.3946 \|
	\| 3.7424 \| 5.56 \| 1450 \| 3.3263 \| 0.1429 \| 0.0556 \| 0.1429 \| 0.0623 \| 0.3951 \|
	\| 3.5358 \| 5.75 \| 1500 \| 3.3100 \| 0.1348 \| 0.0629 \| 0.1348 \| 0.0640 \| 0.3895 \|
	\| 3.5358 \| 5.94 \| 1550 \| 3.2880 \| 0.1402 \| 0.0637 \| 0.1402 \| 0.0641 \| 0.3957 \|
	\| 3.5358 \| 6.14 \| 1600 \| 3.2742 \| 0.1402 \| 0.0628 \| 0.1402 \| 0.0640 \| 0.3965 \|
	\| 3.5358 \| 6.33 \| 1650 \| 3.2605 \| 0.1509 \| 0.0861 \| 0.1509 \| 0.0786 \| 0.4049 \|
	\| 3.5358 \| 6.52 \| 1700 \| 3.2480 \| 0.1429 \| 0.0626 \| 0.1429 \| 0.0663 \| 0.3976 \|
	\| 3.5358 \| 6.71 \| 1750 \| 3.2435 \| 0.1482 \| 0.0575 \| 0.1482 \| 0.0665 \| 0.4030 \|
	\| 3.5358 \| 6.9 \| 1800 \| 3.2324 \| 0.1482 \| 0.0619 \| 0.1482 \| 0.0670 \| 0.4022 \|
	\| 3.5358 \| 7.09 \| 1850 \| 3.2193 \| 0.1563 \| 0.0806 \| 0.1563 \| 0.0799 \| 0.4070 \|
	\| 3.5358 \| 7.29 \| 1900 \| 3.2122 \| 0.1644 \| 0.0825 \| 0.1644 \| 0.0865 \| 0.4119 \|
	\| 3.5358 \| 7.48 \| 1950 \| 3.1995 \| 0.1617 \| 0.0776 \| 0.1617 \| 0.0836 \| 0.4108 \|
	\| 3.4065 \| 7.67 \| 2000 \| 3.1945 \| 0.1617 \| 0.0771 \| 0.1617 \| 0.0837 \| 0.4116 \|
	\| 3.4065 \| 7.86 \| 2050 \| 3.1851 \| 0.1725 \| 0.0832 \| 0.1725 \| 0.0919 \| 0.4191 \|
	\| 3.4065 \| 8.05 \| 2100 \| 3.1805 \| 0.1617 \| 0.0592 \| 0.1617 \| 0.0776 \| 0.4100 \|
	\| 3.4065 \| 8.25 \| 2150 \| 3.1729 \| 0.1617 \| 0.0573 \| 0.1617 \| 0.0762 \| 0.4100 \|
	\| 3.4065 \| 8.44 \| 2200 \| 3.1696 \| 0.1617 \| 0.0571 \| 0.1617 \| 0.0750 \| 0.4100 \|
	\| 3.4065 \| 8.63 \| 2250 \| 3.1638 \| 0.1644 \| 0.0651 \| 0.1644 \| 0.0781 \| 0.4119 \|
	\| 3.4065 \| 8.82 \| 2300 \| 3.1597 \| 0.1590 \| 0.0540 \| 0.1590 \| 0.0735 \| 0.4089 \|
	\| 3.4065 \| 9.01 \| 2350 \| 3.1548 \| 0.1671 \| 0.0688 \| 0.1671 \| 0.0860 \| 0.4137 \|
	\| 3.4065 \| 9.2 \| 2400 \| 3.1540 \| 0.1617 \| 0.0623 \| 0.1617 \| 0.0798 \| 0.4100 \|
	\| 3.4065 \| 9.4 \| 2450 \| 3.1489 \| 0.1644 \| 0.0661 \| 0.1644 \| 0.0820 \| 0.4119 \|
	\| 3.3382 \| 9.59 \| 2500 \| 3.1493 \| 0.1644 \| 0.0706 \| 0.1644 \| 0.0820 \| 0.4119 \|
	\| 3.3382 \| 9.78 \| 2550 \| 3.1464 \| 0.1671 \| 0.0661 \| 0.1671 \| 0.0831 \| 0.4137 \|
	\| 3.3382 \| 9.97 \| 2600 \| 3.1461 \| 0.1671 \| 0.0661 \| 0.1671 \| 0.0830 \| 0.4137 \|


	### Framework versions

	- Transformers 4.38.2
	- Pytorch 2.3.0
	- Datasets 2.19.1
	- Tokenizers 0.15.1