End of training

878d563 verified 5 months ago

7.47 kB

	---
	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

	---
	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