End of training

42c5c7d verified 5 months ago

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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-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: 0.6445
	- Accuracy: 0.7978
	- Precision: 0.8206
	- Recall: 0.7978
	- F1: 0.7858
	- Binary: 0.8580

	## 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 \| 0.19 \| 50 \| 4.3930 \| 0.0216 \| 0.0009 \| 0.0216 \| 0.0017 \| 0.1358 \|
	\| No log \| 0.38 \| 100 \| 4.0474 \| 0.0270 \| 0.0008 \| 0.0270 \| 0.0016 \| 0.2286 \|
	\| No log \| 0.58 \| 150 \| 3.8906 \| 0.0296 \| 0.0013 \| 0.0296 \| 0.0025 \| 0.2442 \|
	\| No log \| 0.77 \| 200 \| 3.6015 \| 0.0485 \| 0.0029 \| 0.0485 \| 0.0053 \| 0.3294 \|
	\| No log \| 0.96 \| 250 \| 3.5656 \| 0.0539 \| 0.0138 \| 0.0539 \| 0.0140 \| 0.3226 \|
	\| 4.0634 \| 1.15 \| 300 \| 3.4188 \| 0.0485 \| 0.0066 \| 0.0485 \| 0.0105 \| 0.3310 \|
	\| 4.0634 \| 1.34 \| 350 \| 3.3200 \| 0.0728 \| 0.0113 \| 0.0728 \| 0.0190 \| 0.3461 \|
	\| 4.0634 \| 1.53 \| 400 \| 3.1522 \| 0.0809 \| 0.0132 \| 0.0809 \| 0.0210 \| 0.3534 \|
	\| 4.0634 \| 1.73 \| 450 \| 3.0267 \| 0.1078 \| 0.0260 \| 0.1078 \| 0.0363 \| 0.3747 \|
	\| 4.0634 \| 1.92 \| 500 \| 2.9975 \| 0.1024 \| 0.0334 \| 0.1024 \| 0.0409 \| 0.3677 \|
	\| 3.3451 \| 2.11 \| 550 \| 2.7835 \| 0.1617 \| 0.0980 \| 0.1617 \| 0.0936 \| 0.4108 \|
	\| 3.3451 \| 2.3 \| 600 \| 2.6777 \| 0.2237 \| 0.1048 \| 0.2237 \| 0.1246 \| 0.4561 \|
	\| 3.3451 \| 2.49 \| 650 \| 2.4946 \| 0.2642 \| 0.1847 \| 0.2642 \| 0.1754 \| 0.4833 \|
	\| 3.3451 \| 2.68 \| 700 \| 2.3266 \| 0.2776 \| 0.2121 \| 0.2776 \| 0.1948 \| 0.4919 \|
	\| 3.3451 \| 2.88 \| 750 \| 2.1236 \| 0.3558 \| 0.2876 \| 0.3558 \| 0.2704 \| 0.5461 \|
	\| 2.8034 \| 3.07 \| 800 \| 1.9687 \| 0.3908 \| 0.3357 \| 0.3908 \| 0.3092 \| 0.5712 \|
	\| 2.8034 \| 3.26 \| 850 \| 1.8541 \| 0.4151 \| 0.3439 \| 0.4151 \| 0.3410 \| 0.5873 \|
	\| 2.8034 \| 3.45 \| 900 \| 1.7298 \| 0.4798 \| 0.4299 \| 0.4798 \| 0.4120 \| 0.6342 \|
	\| 2.8034 \| 3.64 \| 950 \| 1.6094 \| 0.5148 \| 0.4804 \| 0.5148 \| 0.4505 \| 0.6590 \|
	\| 2.8034 \| 3.84 \| 1000 \| 1.5433 \| 0.5526 \| 0.5102 \| 0.5526 \| 0.4910 \| 0.6863 \|
	\| 2.2398 \| 4.03 \| 1050 \| 1.4371 \| 0.5687 \| 0.5274 \| 0.5687 \| 0.5078 \| 0.6960 \|
	\| 2.2398 \| 4.22 \| 1100 \| 1.2423 \| 0.6334 \| 0.6342 \| 0.6334 \| 0.6024 \| 0.7445 \|
	\| 2.2398 \| 4.41 \| 1150 \| 1.1731 \| 0.6631 \| 0.6428 \| 0.6631 \| 0.6210 \| 0.7625 \|
	\| 2.2398 \| 4.6 \| 1200 \| 1.1174 \| 0.7008 \| 0.7177 \| 0.7008 \| 0.6778 \| 0.7900 \|
	\| 2.2398 \| 4.79 \| 1250 \| 1.0677 \| 0.6954 \| 0.7081 \| 0.6954 \| 0.6674 \| 0.7854 \|
	\| 2.2398 \| 4.99 \| 1300 \| 1.0534 \| 0.6739 \| 0.6889 \| 0.6739 \| 0.6480 \| 0.7712 \|
	\| 1.8823 \| 5.18 \| 1350 \| 1.0200 \| 0.7035 \| 0.7035 \| 0.7035 \| 0.6711 \| 0.7930 \|
	\| 1.8823 \| 5.37 \| 1400 \| 0.9667 \| 0.7035 \| 0.7223 \| 0.7035 \| 0.6864 \| 0.7919 \|
	\| 1.8823 \| 5.56 \| 1450 \| 0.9057 \| 0.7197 \| 0.7433 \| 0.7197 \| 0.6972 \| 0.8043 \|
	\| 1.8823 \| 5.75 \| 1500 \| 0.8284 \| 0.7547 \| 0.7680 \| 0.7547 \| 0.7348 \| 0.8296 \|
	\| 1.8823 \| 5.94 \| 1550 \| 0.8156 \| 0.7439 \| 0.7708 \| 0.7439 \| 0.7310 \| 0.8205 \|
	\| 1.6355 \| 6.14 \| 1600 \| 0.8034 \| 0.7412 \| 0.7776 \| 0.7412 \| 0.7313 \| 0.8194 \|
	\| 1.6355 \| 6.33 \| 1650 \| 0.8032 \| 0.7547 \| 0.7768 \| 0.7547 \| 0.7430 \| 0.8307 \|
	\| 1.6355 \| 6.52 \| 1700 \| 0.8030 \| 0.7412 \| 0.7495 \| 0.7412 \| 0.7195 \| 0.8213 \|
	\| 1.6355 \| 6.71 \| 1750 \| 0.7365 \| 0.7898 \| 0.8217 \| 0.7898 \| 0.7786 \| 0.8542 \|
	\| 1.6355 \| 6.9 \| 1800 \| 0.7149 \| 0.7817 \| 0.8157 \| 0.7817 \| 0.7653 \| 0.8493 \|
	\| 1.4501 \| 7.09 \| 1850 \| 0.7493 \| 0.7790 \| 0.8200 \| 0.7790 \| 0.7698 \| 0.8453 \|
	\| 1.4501 \| 7.29 \| 1900 \| 0.7022 \| 0.8086 \| 0.8316 \| 0.8086 \| 0.8003 \| 0.8663 \|
	\| 1.4501 \| 7.48 \| 1950 \| 0.6849 \| 0.7978 \| 0.8226 \| 0.7978 \| 0.7874 \| 0.8596 \|
	\| 1.4501 \| 7.67 \| 2000 \| 0.6008 \| 0.8464 \| 0.8689 \| 0.8464 \| 0.8436 \| 0.8927 \|
	\| 1.4501 \| 7.86 \| 2050 \| 0.6288 \| 0.8194 \| 0.8449 \| 0.8194 \| 0.8112 \| 0.8757 \|
	\| 1.3207 \| 8.05 \| 2100 \| 0.6410 \| 0.8167 \| 0.8407 \| 0.8167 \| 0.8051 \| 0.8722 \|
	\| 1.3207 \| 8.25 \| 2150 \| 0.6128 \| 0.8086 \| 0.8318 \| 0.8086 \| 0.8031 \| 0.8663 \|
	\| 1.3207 \| 8.44 \| 2200 \| 0.6072 \| 0.8113 \| 0.8337 \| 0.8113 \| 0.8053 \| 0.8682 \|
	\| 1.3207 \| 8.63 \| 2250 \| 0.5702 \| 0.8275 \| 0.8519 \| 0.8275 \| 0.8210 \| 0.8795 \|
	\| 1.3207 \| 8.82 \| 2300 \| 0.6059 \| 0.8140 \| 0.8373 \| 0.8140 \| 0.8079 \| 0.8714 \|
	\| 1.2239 \| 9.01 \| 2350 \| 0.5053 \| 0.8491 \| 0.8685 \| 0.8491 \| 0.8453 \| 0.8954 \|
	\| 1.2239 \| 9.2 \| 2400 \| 0.5551 \| 0.8383 \| 0.8657 \| 0.8383 \| 0.8339 \| 0.8871 \|
	\| 1.2239 \| 9.4 \| 2450 \| 0.5767 \| 0.8248 \| 0.8571 \| 0.8248 \| 0.8201 \| 0.8776 \|
	\| 1.2239 \| 9.59 \| 2500 \| 0.5514 \| 0.8383 \| 0.8618 \| 0.8383 \| 0.8331 \| 0.8852 \|
	\| 1.2239 \| 9.78 \| 2550 \| 0.5911 \| 0.8248 \| 0.8542 \| 0.8248 \| 0.8195 \| 0.8765 \|
	\| 1.2239 \| 9.97 \| 2600 \| 0.5498 \| 0.8302 \| 0.8677 \| 0.8302 \| 0.8267 \| 0.8814 \|
	\| 1.1305 \| 10.16 \| 2650 \| 0.4722 \| 0.8706 \| 0.8928 \| 0.8706 \| 0.8672 \| 0.9086 \|
	\| 1.1305 \| 10.35 \| 2700 \| 0.5509 \| 0.8302 \| 0.8654 \| 0.8302 \| 0.8261 \| 0.8814 \|
	\| 1.1305 \| 10.55 \| 2750 \| 0.5590 \| 0.8383 \| 0.8713 \| 0.8383 \| 0.8336 \| 0.8863 \|
	\| 1.1305 \| 10.74 \| 2800 \| 0.5053 \| 0.8464 \| 0.8702 \| 0.8464 \| 0.8424 \| 0.8919 \|
	\| 1.1305 \| 10.93 \| 2850 \| 0.5135 \| 0.8329 \| 0.8548 \| 0.8329 \| 0.8272 \| 0.8822 \|
	\| 1.0868 \| 11.12 \| 2900 \| 0.5438 \| 0.8221 \| 0.8579 \| 0.8221 \| 0.8145 \| 0.8757 \|
	\| 1.0868 \| 11.31 \| 2950 \| 0.5633 \| 0.8410 \| 0.8720 \| 0.8410 \| 0.8348 \| 0.8881 \|
	\| 1.0868 \| 11.51 \| 3000 \| 0.5050 \| 0.8410 \| 0.8664 \| 0.8410 \| 0.8389 \| 0.8889 \|


	### 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: 0.6445
	- Accuracy: 0.7978
	- Precision: 0.8206
	- Recall: 0.7978
	- F1: 0.7858
	- Binary: 0.8580

	## 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 \| 0.19 \| 50 \| 4.3930 \| 0.0216 \| 0.0009 \| 0.0216 \| 0.0017 \| 0.1358 \|
	\| No log \| 0.38 \| 100 \| 4.0474 \| 0.0270 \| 0.0008 \| 0.0270 \| 0.0016 \| 0.2286 \|
	\| No log \| 0.58 \| 150 \| 3.8906 \| 0.0296 \| 0.0013 \| 0.0296 \| 0.0025 \| 0.2442 \|
	\| No log \| 0.77 \| 200 \| 3.6015 \| 0.0485 \| 0.0029 \| 0.0485 \| 0.0053 \| 0.3294 \|
	\| No log \| 0.96 \| 250 \| 3.5656 \| 0.0539 \| 0.0138 \| 0.0539 \| 0.0140 \| 0.3226 \|
	\| 4.0634 \| 1.15 \| 300 \| 3.4188 \| 0.0485 \| 0.0066 \| 0.0485 \| 0.0105 \| 0.3310 \|
	\| 4.0634 \| 1.34 \| 350 \| 3.3200 \| 0.0728 \| 0.0113 \| 0.0728 \| 0.0190 \| 0.3461 \|
	\| 4.0634 \| 1.53 \| 400 \| 3.1522 \| 0.0809 \| 0.0132 \| 0.0809 \| 0.0210 \| 0.3534 \|
	\| 4.0634 \| 1.73 \| 450 \| 3.0267 \| 0.1078 \| 0.0260 \| 0.1078 \| 0.0363 \| 0.3747 \|
	\| 4.0634 \| 1.92 \| 500 \| 2.9975 \| 0.1024 \| 0.0334 \| 0.1024 \| 0.0409 \| 0.3677 \|
	\| 3.3451 \| 2.11 \| 550 \| 2.7835 \| 0.1617 \| 0.0980 \| 0.1617 \| 0.0936 \| 0.4108 \|
	\| 3.3451 \| 2.3 \| 600 \| 2.6777 \| 0.2237 \| 0.1048 \| 0.2237 \| 0.1246 \| 0.4561 \|
	\| 3.3451 \| 2.49 \| 650 \| 2.4946 \| 0.2642 \| 0.1847 \| 0.2642 \| 0.1754 \| 0.4833 \|
	\| 3.3451 \| 2.68 \| 700 \| 2.3266 \| 0.2776 \| 0.2121 \| 0.2776 \| 0.1948 \| 0.4919 \|
	\| 3.3451 \| 2.88 \| 750 \| 2.1236 \| 0.3558 \| 0.2876 \| 0.3558 \| 0.2704 \| 0.5461 \|
	\| 2.8034 \| 3.07 \| 800 \| 1.9687 \| 0.3908 \| 0.3357 \| 0.3908 \| 0.3092 \| 0.5712 \|
	\| 2.8034 \| 3.26 \| 850 \| 1.8541 \| 0.4151 \| 0.3439 \| 0.4151 \| 0.3410 \| 0.5873 \|
	\| 2.8034 \| 3.45 \| 900 \| 1.7298 \| 0.4798 \| 0.4299 \| 0.4798 \| 0.4120 \| 0.6342 \|
	\| 2.8034 \| 3.64 \| 950 \| 1.6094 \| 0.5148 \| 0.4804 \| 0.5148 \| 0.4505 \| 0.6590 \|
	\| 2.8034 \| 3.84 \| 1000 \| 1.5433 \| 0.5526 \| 0.5102 \| 0.5526 \| 0.4910 \| 0.6863 \|
	\| 2.2398 \| 4.03 \| 1050 \| 1.4371 \| 0.5687 \| 0.5274 \| 0.5687 \| 0.5078 \| 0.6960 \|
	\| 2.2398 \| 4.22 \| 1100 \| 1.2423 \| 0.6334 \| 0.6342 \| 0.6334 \| 0.6024 \| 0.7445 \|
	\| 2.2398 \| 4.41 \| 1150 \| 1.1731 \| 0.6631 \| 0.6428 \| 0.6631 \| 0.6210 \| 0.7625 \|
	\| 2.2398 \| 4.6 \| 1200 \| 1.1174 \| 0.7008 \| 0.7177 \| 0.7008 \| 0.6778 \| 0.7900 \|
	\| 2.2398 \| 4.79 \| 1250 \| 1.0677 \| 0.6954 \| 0.7081 \| 0.6954 \| 0.6674 \| 0.7854 \|
	\| 2.2398 \| 4.99 \| 1300 \| 1.0534 \| 0.6739 \| 0.6889 \| 0.6739 \| 0.6480 \| 0.7712 \|
	\| 1.8823 \| 5.18 \| 1350 \| 1.0200 \| 0.7035 \| 0.7035 \| 0.7035 \| 0.6711 \| 0.7930 \|
	\| 1.8823 \| 5.37 \| 1400 \| 0.9667 \| 0.7035 \| 0.7223 \| 0.7035 \| 0.6864 \| 0.7919 \|
	\| 1.8823 \| 5.56 \| 1450 \| 0.9057 \| 0.7197 \| 0.7433 \| 0.7197 \| 0.6972 \| 0.8043 \|
	\| 1.8823 \| 5.75 \| 1500 \| 0.8284 \| 0.7547 \| 0.7680 \| 0.7547 \| 0.7348 \| 0.8296 \|
	\| 1.8823 \| 5.94 \| 1550 \| 0.8156 \| 0.7439 \| 0.7708 \| 0.7439 \| 0.7310 \| 0.8205 \|
	\| 1.6355 \| 6.14 \| 1600 \| 0.8034 \| 0.7412 \| 0.7776 \| 0.7412 \| 0.7313 \| 0.8194 \|
	\| 1.6355 \| 6.33 \| 1650 \| 0.8032 \| 0.7547 \| 0.7768 \| 0.7547 \| 0.7430 \| 0.8307 \|
	\| 1.6355 \| 6.52 \| 1700 \| 0.8030 \| 0.7412 \| 0.7495 \| 0.7412 \| 0.7195 \| 0.8213 \|
	\| 1.6355 \| 6.71 \| 1750 \| 0.7365 \| 0.7898 \| 0.8217 \| 0.7898 \| 0.7786 \| 0.8542 \|
	\| 1.6355 \| 6.9 \| 1800 \| 0.7149 \| 0.7817 \| 0.8157 \| 0.7817 \| 0.7653 \| 0.8493 \|
	\| 1.4501 \| 7.09 \| 1850 \| 0.7493 \| 0.7790 \| 0.8200 \| 0.7790 \| 0.7698 \| 0.8453 \|
	\| 1.4501 \| 7.29 \| 1900 \| 0.7022 \| 0.8086 \| 0.8316 \| 0.8086 \| 0.8003 \| 0.8663 \|
	\| 1.4501 \| 7.48 \| 1950 \| 0.6849 \| 0.7978 \| 0.8226 \| 0.7978 \| 0.7874 \| 0.8596 \|
	\| 1.4501 \| 7.67 \| 2000 \| 0.6008 \| 0.8464 \| 0.8689 \| 0.8464 \| 0.8436 \| 0.8927 \|
	\| 1.4501 \| 7.86 \| 2050 \| 0.6288 \| 0.8194 \| 0.8449 \| 0.8194 \| 0.8112 \| 0.8757 \|
	\| 1.3207 \| 8.05 \| 2100 \| 0.6410 \| 0.8167 \| 0.8407 \| 0.8167 \| 0.8051 \| 0.8722 \|
	\| 1.3207 \| 8.25 \| 2150 \| 0.6128 \| 0.8086 \| 0.8318 \| 0.8086 \| 0.8031 \| 0.8663 \|
	\| 1.3207 \| 8.44 \| 2200 \| 0.6072 \| 0.8113 \| 0.8337 \| 0.8113 \| 0.8053 \| 0.8682 \|
	\| 1.3207 \| 8.63 \| 2250 \| 0.5702 \| 0.8275 \| 0.8519 \| 0.8275 \| 0.8210 \| 0.8795 \|
	\| 1.3207 \| 8.82 \| 2300 \| 0.6059 \| 0.8140 \| 0.8373 \| 0.8140 \| 0.8079 \| 0.8714 \|
	\| 1.2239 \| 9.01 \| 2350 \| 0.5053 \| 0.8491 \| 0.8685 \| 0.8491 \| 0.8453 \| 0.8954 \|
	\| 1.2239 \| 9.2 \| 2400 \| 0.5551 \| 0.8383 \| 0.8657 \| 0.8383 \| 0.8339 \| 0.8871 \|
	\| 1.2239 \| 9.4 \| 2450 \| 0.5767 \| 0.8248 \| 0.8571 \| 0.8248 \| 0.8201 \| 0.8776 \|
	\| 1.2239 \| 9.59 \| 2500 \| 0.5514 \| 0.8383 \| 0.8618 \| 0.8383 \| 0.8331 \| 0.8852 \|
	\| 1.2239 \| 9.78 \| 2550 \| 0.5911 \| 0.8248 \| 0.8542 \| 0.8248 \| 0.8195 \| 0.8765 \|
	\| 1.2239 \| 9.97 \| 2600 \| 0.5498 \| 0.8302 \| 0.8677 \| 0.8302 \| 0.8267 \| 0.8814 \|
	\| 1.1305 \| 10.16 \| 2650 \| 0.4722 \| 0.8706 \| 0.8928 \| 0.8706 \| 0.8672 \| 0.9086 \|
	\| 1.1305 \| 10.35 \| 2700 \| 0.5509 \| 0.8302 \| 0.8654 \| 0.8302 \| 0.8261 \| 0.8814 \|
	\| 1.1305 \| 10.55 \| 2750 \| 0.5590 \| 0.8383 \| 0.8713 \| 0.8383 \| 0.8336 \| 0.8863 \|
	\| 1.1305 \| 10.74 \| 2800 \| 0.5053 \| 0.8464 \| 0.8702 \| 0.8464 \| 0.8424 \| 0.8919 \|
	\| 1.1305 \| 10.93 \| 2850 \| 0.5135 \| 0.8329 \| 0.8548 \| 0.8329 \| 0.8272 \| 0.8822 \|
	\| 1.0868 \| 11.12 \| 2900 \| 0.5438 \| 0.8221 \| 0.8579 \| 0.8221 \| 0.8145 \| 0.8757 \|
	\| 1.0868 \| 11.31 \| 2950 \| 0.5633 \| 0.8410 \| 0.8720 \| 0.8410 \| 0.8348 \| 0.8881 \|
	\| 1.0868 \| 11.51 \| 3000 \| 0.5050 \| 0.8410 \| 0.8664 \| 0.8410 \| 0.8389 \| 0.8889 \|


	### Framework versions

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