End of training

ba51d4c verified 5 months ago

7.5 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-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: 0.5592
	- Accuracy: 0.8464
	- Precision: 0.8588
	- Recall: 0.8464
	- F1: 0.8431
	- Binary: 0.8926

	## 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 \| 0.22 \| 50 \| 4.4295 \| 0.0135 \| 0.0002 \| 0.0135 \| 0.0004 \| 0.1332 \|
	\| No log \| 0.43 \| 100 \| 4.4254 \| 0.0148 \| 0.0002 \| 0.0148 \| 0.0004 \| 0.1274 \|
	\| No log \| 0.65 \| 150 \| 3.8186 \| 0.0364 \| 0.0121 \| 0.0364 \| 0.0050 \| 0.3090 \|
	\| No log \| 0.86 \| 200 \| 3.5321 \| 0.0391 \| 0.0090 \| 0.0391 \| 0.0062 \| 0.3193 \|
	\| 4.1413 \| 1.08 \| 250 \| 3.3337 \| 0.0728 \| 0.0256 \| 0.0728 \| 0.0286 \| 0.3453 \|
	\| 4.1413 \| 1.29 \| 300 \| 3.1664 \| 0.0970 \| 0.0489 \| 0.0970 \| 0.0400 \| 0.3590 \|
	\| 4.1413 \| 1.51 \| 350 \| 2.9961 \| 0.1253 \| 0.0613 \| 0.1253 \| 0.0631 \| 0.3821 \|
	\| 4.1413 \| 1.73 \| 400 \| 2.8225 \| 0.1739 \| 0.0798 \| 0.1739 \| 0.0904 \| 0.4181 \|
	\| 4.1413 \| 1.94 \| 450 \| 2.6439 \| 0.2116 \| 0.1109 \| 0.2116 \| 0.1236 \| 0.4457 \|
	\| 3.2276 \| 2.16 \| 500 \| 2.4578 \| 0.2385 \| 0.1802 \| 0.2385 \| 0.1570 \| 0.4670 \|
	\| 3.2276 \| 2.37 \| 550 \| 2.2801 \| 0.3396 \| 0.2831 \| 0.3396 \| 0.2516 \| 0.5358 \|
	\| 3.2276 \| 2.59 \| 600 \| 2.0684 \| 0.4003 \| 0.3030 \| 0.4003 \| 0.3068 \| 0.5796 \|
	\| 3.2276 \| 2.8 \| 650 \| 1.9308 \| 0.4299 \| 0.3493 \| 0.4299 \| 0.3516 \| 0.6005 \|
	\| 2.5852 \| 3.02 \| 700 \| 1.8448 \| 0.4501 \| 0.4000 \| 0.4501 \| 0.3811 \| 0.6146 \|
	\| 2.5852 \| 3.24 \| 750 \| 1.6568 \| 0.5283 \| 0.4743 \| 0.5283 \| 0.4552 \| 0.6689 \|
	\| 2.5852 \| 3.45 \| 800 \| 1.6974 \| 0.4690 \| 0.4551 \| 0.4690 \| 0.4169 \| 0.6264 \|
	\| 2.5852 \| 3.67 \| 850 \| 1.4828 \| 0.5687 \| 0.5769 \| 0.5687 \| 0.5231 \| 0.6978 \|
	\| 2.5852 \| 3.88 \| 900 \| 1.4420 \| 0.5580 \| 0.5477 \| 0.5580 \| 0.5126 \| 0.6896 \|
	\| 2.1226 \| 4.1 \| 950 \| 1.3306 \| 0.6186 \| 0.6133 \| 0.6186 \| 0.5784 \| 0.7315 \|
	\| 2.1226 \| 4.31 \| 1000 \| 1.2209 \| 0.6456 \| 0.6561 \| 0.6456 \| 0.6076 \| 0.7500 \|
	\| 2.1226 \| 4.53 \| 1050 \| 1.1256 \| 0.6698 \| 0.6865 \| 0.6698 \| 0.6404 \| 0.7664 \|
	\| 2.1226 \| 4.75 \| 1100 \| 1.0700 \| 0.6846 \| 0.7003 \| 0.6846 \| 0.6586 \| 0.7770 \|
	\| 2.1226 \| 4.96 \| 1150 \| 1.0085 \| 0.7156 \| 0.7415 \| 0.7156 \| 0.6942 \| 0.7993 \|
	\| 1.8257 \| 5.18 \| 1200 \| 1.0190 \| 0.7224 \| 0.7397 \| 0.7224 \| 0.7028 \| 0.8046 \|
	\| 1.8257 \| 5.39 \| 1250 \| 0.9742 \| 0.7102 \| 0.7244 \| 0.7102 \| 0.6886 \| 0.7961 \|
	\| 1.8257 \| 5.61 \| 1300 \| 0.8793 \| 0.7561 \| 0.7680 \| 0.7561 \| 0.7384 \| 0.8284 \|
	\| 1.8257 \| 5.83 \| 1350 \| 0.8472 \| 0.7547 \| 0.7763 \| 0.7547 \| 0.7426 \| 0.8280 \|
	\| 1.5842 \| 6.04 \| 1400 \| 0.8424 \| 0.7601 \| 0.7956 \| 0.7601 \| 0.7487 \| 0.8327 \|
	\| 1.5842 \| 6.26 \| 1450 \| 0.7802 \| 0.7642 \| 0.7846 \| 0.7642 \| 0.7513 \| 0.8348 \|
	\| 1.5842 \| 6.47 \| 1500 \| 0.7447 \| 0.7965 \| 0.8096 \| 0.7965 \| 0.7914 \| 0.8574 \|
	\| 1.5842 \| 6.69 \| 1550 \| 0.7081 \| 0.7844 \| 0.8035 \| 0.7844 \| 0.7772 \| 0.8499 \|
	\| 1.5842 \| 6.9 \| 1600 \| 0.7616 \| 0.7722 \| 0.7995 \| 0.7722 \| 0.7681 \| 0.8399 \|
	\| 1.4387 \| 7.12 \| 1650 \| 0.7133 \| 0.7709 \| 0.7904 \| 0.7709 \| 0.7607 \| 0.8403 \|
	\| 1.4387 \| 7.34 \| 1700 \| 0.6570 \| 0.8127 \| 0.8301 \| 0.8127 \| 0.8094 \| 0.8695 \|
	\| 1.4387 \| 7.55 \| 1750 \| 0.6325 \| 0.8221 \| 0.8461 \| 0.8221 \| 0.8212 \| 0.8761 \|
	\| 1.4387 \| 7.77 \| 1800 \| 0.6352 \| 0.8032 \| 0.8251 \| 0.8032 \| 0.8004 \| 0.8625 \|
	\| 1.4387 \| 7.98 \| 1850 \| 0.6313 \| 0.8086 \| 0.8270 \| 0.8086 \| 0.8040 \| 0.8678 \|
	\| 1.3174 \| 8.2 \| 1900 \| 0.6843 \| 0.8154 \| 0.8372 \| 0.8154 \| 0.8100 \| 0.8710 \|
	\| 1.3174 \| 8.41 \| 1950 \| 0.6142 \| 0.8194 \| 0.8360 \| 0.8194 \| 0.8153 \| 0.8739 \|
	\| 1.3174 \| 8.63 \| 2000 \| 0.6324 \| 0.8154 \| 0.8229 \| 0.8154 \| 0.8102 \| 0.8710 \|
	\| 1.3174 \| 8.85 \| 2050 \| 0.5751 \| 0.8383 \| 0.8566 \| 0.8383 \| 0.8351 \| 0.8852 \|
	\| 1.2131 \| 9.06 \| 2100 \| 0.5873 \| 0.8275 \| 0.8439 \| 0.8275 \| 0.8250 \| 0.8805 \|
	\| 1.2131 \| 9.28 \| 2150 \| 0.6016 \| 0.8167 \| 0.8346 \| 0.8167 \| 0.8131 \| 0.8729 \|
	\| 1.2131 \| 9.49 \| 2200 \| 0.5982 \| 0.8410 \| 0.8617 \| 0.8410 \| 0.8387 \| 0.8879 \|
	\| 1.2131 \| 9.71 \| 2250 \| 0.5490 \| 0.8437 \| 0.8564 \| 0.8437 \| 0.8410 \| 0.8912 \|
	\| 1.2131 \| 9.92 \| 2300 \| 0.5587 \| 0.8342 \| 0.8537 \| 0.8342 \| 0.8309 \| 0.8837 \|
	\| 1.1426 \| 10.14 \| 2350 \| 0.5969 \| 0.8261 \| 0.8446 \| 0.8261 \| 0.8214 \| 0.8790 \|
	\| 1.1426 \| 10.36 \| 2400 \| 0.5936 \| 0.8410 \| 0.8575 \| 0.8410 \| 0.8382 \| 0.8889 \|
	\| 1.1426 \| 10.57 \| 2450 \| 0.5656 \| 0.8383 \| 0.8579 \| 0.8383 \| 0.8364 \| 0.8865 \|
	\| 1.1426 \| 10.79 \| 2500 \| 0.5130 \| 0.8625 \| 0.8756 \| 0.8625 \| 0.8593 \| 0.9054 \|
	\| 1.0738 \| 11.0 \| 2550 \| 0.5832 \| 0.8396 \| 0.8618 \| 0.8396 \| 0.8389 \| 0.8880 \|
	\| 1.0738 \| 11.22 \| 2600 \| 0.5554 \| 0.8423 \| 0.8634 \| 0.8423 \| 0.8417 \| 0.8908 \|
	\| 1.0738 \| 11.43 \| 2650 \| 0.5763 \| 0.8275 \| 0.8490 \| 0.8275 \| 0.8238 \| 0.8801 \|
	\| 1.0738 \| 11.65 \| 2700 \| 0.5697 \| 0.8329 \| 0.8452 \| 0.8329 \| 0.8281 \| 0.8857 \|
	\| 1.0738 \| 11.87 \| 2750 \| 0.5413 \| 0.8464 \| 0.8655 \| 0.8464 \| 0.8432 \| 0.8922 \|
	\| 1.0326 \| 12.08 \| 2800 \| 0.5954 \| 0.8235 \| 0.8443 \| 0.8235 \| 0.8176 \| 0.8761 \|
	\| 1.0326 \| 12.3 \| 2850 \| 0.5665 \| 0.8410 \| 0.8611 \| 0.8410 \| 0.8354 \| 0.8908 \|


	### 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-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: 0.5592
	- Accuracy: 0.8464
	- Precision: 0.8588
	- Recall: 0.8464
	- F1: 0.8431
	- Binary: 0.8926

	## 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 \| 0.22 \| 50 \| 4.4295 \| 0.0135 \| 0.0002 \| 0.0135 \| 0.0004 \| 0.1332 \|
	\| No log \| 0.43 \| 100 \| 4.4254 \| 0.0148 \| 0.0002 \| 0.0148 \| 0.0004 \| 0.1274 \|
	\| No log \| 0.65 \| 150 \| 3.8186 \| 0.0364 \| 0.0121 \| 0.0364 \| 0.0050 \| 0.3090 \|
	\| No log \| 0.86 \| 200 \| 3.5321 \| 0.0391 \| 0.0090 \| 0.0391 \| 0.0062 \| 0.3193 \|
	\| 4.1413 \| 1.08 \| 250 \| 3.3337 \| 0.0728 \| 0.0256 \| 0.0728 \| 0.0286 \| 0.3453 \|
	\| 4.1413 \| 1.29 \| 300 \| 3.1664 \| 0.0970 \| 0.0489 \| 0.0970 \| 0.0400 \| 0.3590 \|
	\| 4.1413 \| 1.51 \| 350 \| 2.9961 \| 0.1253 \| 0.0613 \| 0.1253 \| 0.0631 \| 0.3821 \|
	\| 4.1413 \| 1.73 \| 400 \| 2.8225 \| 0.1739 \| 0.0798 \| 0.1739 \| 0.0904 \| 0.4181 \|
	\| 4.1413 \| 1.94 \| 450 \| 2.6439 \| 0.2116 \| 0.1109 \| 0.2116 \| 0.1236 \| 0.4457 \|
	\| 3.2276 \| 2.16 \| 500 \| 2.4578 \| 0.2385 \| 0.1802 \| 0.2385 \| 0.1570 \| 0.4670 \|
	\| 3.2276 \| 2.37 \| 550 \| 2.2801 \| 0.3396 \| 0.2831 \| 0.3396 \| 0.2516 \| 0.5358 \|
	\| 3.2276 \| 2.59 \| 600 \| 2.0684 \| 0.4003 \| 0.3030 \| 0.4003 \| 0.3068 \| 0.5796 \|
	\| 3.2276 \| 2.8 \| 650 \| 1.9308 \| 0.4299 \| 0.3493 \| 0.4299 \| 0.3516 \| 0.6005 \|
	\| 2.5852 \| 3.02 \| 700 \| 1.8448 \| 0.4501 \| 0.4000 \| 0.4501 \| 0.3811 \| 0.6146 \|
	\| 2.5852 \| 3.24 \| 750 \| 1.6568 \| 0.5283 \| 0.4743 \| 0.5283 \| 0.4552 \| 0.6689 \|
	\| 2.5852 \| 3.45 \| 800 \| 1.6974 \| 0.4690 \| 0.4551 \| 0.4690 \| 0.4169 \| 0.6264 \|
	\| 2.5852 \| 3.67 \| 850 \| 1.4828 \| 0.5687 \| 0.5769 \| 0.5687 \| 0.5231 \| 0.6978 \|
	\| 2.5852 \| 3.88 \| 900 \| 1.4420 \| 0.5580 \| 0.5477 \| 0.5580 \| 0.5126 \| 0.6896 \|
	\| 2.1226 \| 4.1 \| 950 \| 1.3306 \| 0.6186 \| 0.6133 \| 0.6186 \| 0.5784 \| 0.7315 \|
	\| 2.1226 \| 4.31 \| 1000 \| 1.2209 \| 0.6456 \| 0.6561 \| 0.6456 \| 0.6076 \| 0.7500 \|
	\| 2.1226 \| 4.53 \| 1050 \| 1.1256 \| 0.6698 \| 0.6865 \| 0.6698 \| 0.6404 \| 0.7664 \|
	\| 2.1226 \| 4.75 \| 1100 \| 1.0700 \| 0.6846 \| 0.7003 \| 0.6846 \| 0.6586 \| 0.7770 \|
	\| 2.1226 \| 4.96 \| 1150 \| 1.0085 \| 0.7156 \| 0.7415 \| 0.7156 \| 0.6942 \| 0.7993 \|
	\| 1.8257 \| 5.18 \| 1200 \| 1.0190 \| 0.7224 \| 0.7397 \| 0.7224 \| 0.7028 \| 0.8046 \|
	\| 1.8257 \| 5.39 \| 1250 \| 0.9742 \| 0.7102 \| 0.7244 \| 0.7102 \| 0.6886 \| 0.7961 \|
	\| 1.8257 \| 5.61 \| 1300 \| 0.8793 \| 0.7561 \| 0.7680 \| 0.7561 \| 0.7384 \| 0.8284 \|
	\| 1.8257 \| 5.83 \| 1350 \| 0.8472 \| 0.7547 \| 0.7763 \| 0.7547 \| 0.7426 \| 0.8280 \|
	\| 1.5842 \| 6.04 \| 1400 \| 0.8424 \| 0.7601 \| 0.7956 \| 0.7601 \| 0.7487 \| 0.8327 \|
	\| 1.5842 \| 6.26 \| 1450 \| 0.7802 \| 0.7642 \| 0.7846 \| 0.7642 \| 0.7513 \| 0.8348 \|
	\| 1.5842 \| 6.47 \| 1500 \| 0.7447 \| 0.7965 \| 0.8096 \| 0.7965 \| 0.7914 \| 0.8574 \|
	\| 1.5842 \| 6.69 \| 1550 \| 0.7081 \| 0.7844 \| 0.8035 \| 0.7844 \| 0.7772 \| 0.8499 \|
	\| 1.5842 \| 6.9 \| 1600 \| 0.7616 \| 0.7722 \| 0.7995 \| 0.7722 \| 0.7681 \| 0.8399 \|
	\| 1.4387 \| 7.12 \| 1650 \| 0.7133 \| 0.7709 \| 0.7904 \| 0.7709 \| 0.7607 \| 0.8403 \|
	\| 1.4387 \| 7.34 \| 1700 \| 0.6570 \| 0.8127 \| 0.8301 \| 0.8127 \| 0.8094 \| 0.8695 \|
	\| 1.4387 \| 7.55 \| 1750 \| 0.6325 \| 0.8221 \| 0.8461 \| 0.8221 \| 0.8212 \| 0.8761 \|
	\| 1.4387 \| 7.77 \| 1800 \| 0.6352 \| 0.8032 \| 0.8251 \| 0.8032 \| 0.8004 \| 0.8625 \|
	\| 1.4387 \| 7.98 \| 1850 \| 0.6313 \| 0.8086 \| 0.8270 \| 0.8086 \| 0.8040 \| 0.8678 \|
	\| 1.3174 \| 8.2 \| 1900 \| 0.6843 \| 0.8154 \| 0.8372 \| 0.8154 \| 0.8100 \| 0.8710 \|
	\| 1.3174 \| 8.41 \| 1950 \| 0.6142 \| 0.8194 \| 0.8360 \| 0.8194 \| 0.8153 \| 0.8739 \|
	\| 1.3174 \| 8.63 \| 2000 \| 0.6324 \| 0.8154 \| 0.8229 \| 0.8154 \| 0.8102 \| 0.8710 \|
	\| 1.3174 \| 8.85 \| 2050 \| 0.5751 \| 0.8383 \| 0.8566 \| 0.8383 \| 0.8351 \| 0.8852 \|
	\| 1.2131 \| 9.06 \| 2100 \| 0.5873 \| 0.8275 \| 0.8439 \| 0.8275 \| 0.8250 \| 0.8805 \|
	\| 1.2131 \| 9.28 \| 2150 \| 0.6016 \| 0.8167 \| 0.8346 \| 0.8167 \| 0.8131 \| 0.8729 \|
	\| 1.2131 \| 9.49 \| 2200 \| 0.5982 \| 0.8410 \| 0.8617 \| 0.8410 \| 0.8387 \| 0.8879 \|
	\| 1.2131 \| 9.71 \| 2250 \| 0.5490 \| 0.8437 \| 0.8564 \| 0.8437 \| 0.8410 \| 0.8912 \|
	\| 1.2131 \| 9.92 \| 2300 \| 0.5587 \| 0.8342 \| 0.8537 \| 0.8342 \| 0.8309 \| 0.8837 \|
	\| 1.1426 \| 10.14 \| 2350 \| 0.5969 \| 0.8261 \| 0.8446 \| 0.8261 \| 0.8214 \| 0.8790 \|
	\| 1.1426 \| 10.36 \| 2400 \| 0.5936 \| 0.8410 \| 0.8575 \| 0.8410 \| 0.8382 \| 0.8889 \|
	\| 1.1426 \| 10.57 \| 2450 \| 0.5656 \| 0.8383 \| 0.8579 \| 0.8383 \| 0.8364 \| 0.8865 \|
	\| 1.1426 \| 10.79 \| 2500 \| 0.5130 \| 0.8625 \| 0.8756 \| 0.8625 \| 0.8593 \| 0.9054 \|
	\| 1.0738 \| 11.0 \| 2550 \| 0.5832 \| 0.8396 \| 0.8618 \| 0.8396 \| 0.8389 \| 0.8880 \|
	\| 1.0738 \| 11.22 \| 2600 \| 0.5554 \| 0.8423 \| 0.8634 \| 0.8423 \| 0.8417 \| 0.8908 \|
	\| 1.0738 \| 11.43 \| 2650 \| 0.5763 \| 0.8275 \| 0.8490 \| 0.8275 \| 0.8238 \| 0.8801 \|
	\| 1.0738 \| 11.65 \| 2700 \| 0.5697 \| 0.8329 \| 0.8452 \| 0.8329 \| 0.8281 \| 0.8857 \|
	\| 1.0738 \| 11.87 \| 2750 \| 0.5413 \| 0.8464 \| 0.8655 \| 0.8464 \| 0.8432 \| 0.8922 \|
	\| 1.0326 \| 12.08 \| 2800 \| 0.5954 \| 0.8235 \| 0.8443 \| 0.8235 \| 0.8176 \| 0.8761 \|
	\| 1.0326 \| 12.3 \| 2850 \| 0.5665 \| 0.8410 \| 0.8611 \| 0.8410 \| 0.8354 \| 0.8908 \|


	### Framework versions

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