hubert-classifier-aug-fold-3 / README.md

End of training

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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-3
	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-3

	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: 0.6214
	- Accuracy: 0.8544
	- Precision: 0.8720
	- Recall: 0.8544
	- F1: 0.8540
	- Binary: 0.8989

	## 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
	- lr_scheduler_warmup_steps: 500
	- num_epochs: 100
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| Binary \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\|:------:\|
	\| No log \| 0.24 \| 50 \| 4.4176 \| 0.0172 \| 0.0140 \| 0.0172 \| 0.0083 \| 0.1549 \|
	\| No log \| 0.48 \| 100 \| 4.3072 \| 0.0434 \| 0.0497 \| 0.0434 \| 0.0197 \| 0.2778 \|
	\| No log \| 0.72 \| 150 \| 3.9604 \| 0.0906 \| 0.0606 \| 0.0906 \| 0.0446 \| 0.3581 \|
	\| No log \| 0.96 \| 200 \| 3.6355 \| 0.1191 \| 0.0507 \| 0.1191 \| 0.0558 \| 0.3783 \|
	\| 4.235 \| 1.2 \| 250 \| 3.3489 \| 0.1700 \| 0.0800 \| 0.1700 \| 0.0858 \| 0.4157 \|
	\| 4.235 \| 1.44 \| 300 \| 3.1008 \| 0.2015 \| 0.1270 \| 0.2015 \| 0.1158 \| 0.4382 \|
	\| 4.235 \| 1.68 \| 350 \| 2.8220 \| 0.2906 \| 0.2075 \| 0.2906 \| 0.2012 \| 0.5014 \|
	\| 4.235 \| 1.92 \| 400 \| 2.5557 \| 0.3843 \| 0.3160 \| 0.3843 \| 0.3099 \| 0.5671 \|
	\| 3.2055 \| 2.16 \| 450 \| 2.1790 \| 0.4801 \| 0.4047 \| 0.4801 \| 0.4036 \| 0.6344 \|
	\| 3.2055 \| 2.4 \| 500 \| 1.9034 \| 0.5790 \| 0.5557 \| 0.5790 \| 0.5261 \| 0.7028 \|
	\| 3.2055 \| 2.63 \| 550 \| 1.6707 \| 0.6135 \| 0.6116 \| 0.6135 \| 0.5701 \| 0.7273 \|
	\| 3.2055 \| 2.87 \| 600 \| 1.4658 \| 0.6285 \| 0.6047 \| 0.6285 \| 0.5817 \| 0.7381 \|
	\| 2.1878 \| 3.11 \| 650 \| 1.3665 \| 0.6457 \| 0.6522 \| 0.6457 \| 0.6153 \| 0.7534 \|
	\| 2.1878 \| 3.35 \| 700 \| 1.2309 \| 0.6757 \| 0.6806 \| 0.6757 \| 0.6446 \| 0.7730 \|
	\| 2.1878 \| 3.59 \| 750 \| 1.1077 \| 0.7169 \| 0.7307 \| 0.7169 \| 0.6966 \| 0.7999 \|
	\| 2.1878 \| 3.83 \| 800 \| 1.0393 \| 0.7341 \| 0.7548 \| 0.7341 \| 0.7226 \| 0.8130 \|
	\| 1.534 \| 4.07 \| 850 \| 0.9478 \| 0.7678 \| 0.7794 \| 0.7678 \| 0.7572 \| 0.8384 \|
	\| 1.534 \| 4.31 \| 900 \| 0.8755 \| 0.7715 \| 0.7789 \| 0.7715 \| 0.7627 \| 0.8395 \|
	\| 1.534 \| 4.55 \| 950 \| 0.8563 \| 0.7618 \| 0.7737 \| 0.7618 \| 0.7491 \| 0.8330 \|
	\| 1.534 \| 4.79 \| 1000 \| 0.7866 \| 0.8007 \| 0.8046 \| 0.8007 \| 0.7921 \| 0.8616 \|
	\| 1.2035 \| 5.03 \| 1050 \| 0.7462 \| 0.8007 \| 0.8212 \| 0.8007 \| 0.7945 \| 0.8591 \|
	\| 1.2035 \| 5.27 \| 1100 \| 0.7003 \| 0.8157 \| 0.8272 \| 0.8157 \| 0.8126 \| 0.8717 \|
	\| 1.2035 \| 5.51 \| 1150 \| 0.7421 \| 0.8105 \| 0.8262 \| 0.8105 \| 0.8074 \| 0.8672 \|
	\| 1.2035 \| 5.75 \| 1200 \| 0.7638 \| 0.7993 \| 0.8294 \| 0.7993 \| 0.7979 \| 0.8595 \|
	\| 1.2035 \| 5.99 \| 1250 \| 0.6872 \| 0.8187 \| 0.8330 \| 0.8187 \| 0.8171 \| 0.8742 \|
	\| 0.9909 \| 6.23 \| 1300 \| 0.6378 \| 0.8345 \| 0.8462 \| 0.8345 \| 0.8338 \| 0.8840 \|
	\| 0.9909 \| 6.47 \| 1350 \| 0.6835 \| 0.8075 \| 0.8266 \| 0.8075 \| 0.8063 \| 0.8669 \|
	\| 0.9909 \| 6.71 \| 1400 \| 0.6367 \| 0.8345 \| 0.8480 \| 0.8345 \| 0.8337 \| 0.8874 \|
	\| 0.9909 \| 6.95 \| 1450 \| 0.5793 \| 0.8434 \| 0.8521 \| 0.8434 \| 0.8425 \| 0.8931 \|
	\| 0.8826 \| 7.19 \| 1500 \| 0.6528 \| 0.8307 \| 0.8458 \| 0.8307 \| 0.8293 \| 0.8824 \|
	\| 0.8826 \| 7.43 \| 1550 \| 0.6361 \| 0.8225 \| 0.8382 \| 0.8225 \| 0.8218 \| 0.8761 \|
	\| 0.8826 \| 7.66 \| 1600 \| 0.6189 \| 0.8360 \| 0.8478 \| 0.8360 \| 0.8334 \| 0.8855 \|
	\| 0.8826 \| 7.9 \| 1650 \| 0.6078 \| 0.8337 \| 0.8433 \| 0.8337 \| 0.8321 \| 0.8831 \|
	\| 0.7752 \| 8.14 \| 1700 \| 0.6868 \| 0.8315 \| 0.8436 \| 0.8315 \| 0.8289 \| 0.8835 \|
	\| 0.7752 \| 8.38 \| 1750 \| 0.6118 \| 0.8419 \| 0.8549 \| 0.8419 \| 0.8411 \| 0.8897 \|
	\| 0.7752 \| 8.62 \| 1800 \| 0.5837 \| 0.8532 \| 0.8660 \| 0.8532 \| 0.8531 \| 0.8974 \|
	\| 0.7752 \| 8.86 \| 1850 \| 0.5758 \| 0.8487 \| 0.8613 \| 0.8487 \| 0.8494 \| 0.8956 \|
	\| 0.7067 \| 9.1 \| 1900 \| 0.6950 \| 0.8307 \| 0.8490 \| 0.8307 \| 0.8279 \| 0.8827 \|
	\| 0.7067 \| 9.34 \| 1950 \| 0.5968 \| 0.8479 \| 0.8595 \| 0.8479 \| 0.8470 \| 0.8942 \|
	\| 0.7067 \| 9.58 \| 2000 \| 0.5714 \| 0.8614 \| 0.8696 \| 0.8614 \| 0.8613 \| 0.9035 \|
	\| 0.7067 \| 9.82 \| 2050 \| 0.6389 \| 0.8427 \| 0.8538 \| 0.8427 \| 0.8415 \| 0.8903 \|
	\| 0.6457 \| 10.06 \| 2100 \| 0.6504 \| 0.8502 \| 0.8639 \| 0.8502 \| 0.8504 \| 0.8948 \|
	\| 0.6457 \| 10.3 \| 2150 \| 0.5776 \| 0.8547 \| 0.8659 \| 0.8547 \| 0.8534 \| 0.8988 \|
	\| 0.6457 \| 10.54 \| 2200 \| 0.6775 \| 0.8434 \| 0.8570 \| 0.8434 \| 0.8438 \| 0.8912 \|
	\| 0.6457 \| 10.78 \| 2250 \| 0.5849 \| 0.8569 \| 0.8686 \| 0.8569 \| 0.8579 \| 0.9013 \|
	\| 0.6098 \| 11.02 \| 2300 \| 0.5767 \| 0.8622 \| 0.8706 \| 0.8622 \| 0.8632 \| 0.9037 \|
	\| 0.6098 \| 11.26 \| 2350 \| 0.6875 \| 0.8404 \| 0.8588 \| 0.8404 \| 0.8404 \| 0.8898 \|
	\| 0.6098 \| 11.5 \| 2400 \| 0.7397 \| 0.8352 \| 0.8483 \| 0.8352 \| 0.8340 \| 0.8865 \|
	\| 0.6098 \| 11.74 \| 2450 \| 0.5998 \| 0.8629 \| 0.8716 \| 0.8629 \| 0.8618 \| 0.9053 \|
	\| 0.6098 \| 11.98 \| 2500 \| 0.6435 \| 0.8449 \| 0.8549 \| 0.8449 \| 0.8441 \| 0.8918 \|
	\| 0.5538 \| 12.22 \| 2550 \| 0.6969 \| 0.8502 \| 0.8640 \| 0.8502 \| 0.8508 \| 0.8965 \|
	\| 0.5538 \| 12.46 \| 2600 \| 0.6323 \| 0.8577 \| 0.8710 \| 0.8577 \| 0.8566 \| 0.9006 \|
	\| 0.5538 \| 12.69 \| 2650 \| 0.6989 \| 0.8532 \| 0.8660 \| 0.8532 \| 0.8525 \| 0.8981 \|
	\| 0.5538 \| 12.93 \| 2700 \| 0.6736 \| 0.8554 \| 0.8666 \| 0.8554 \| 0.8552 \| 0.8994 \|
	\| 0.5356 \| 13.17 \| 2750 \| 0.6737 \| 0.8487 \| 0.8584 \| 0.8487 \| 0.8469 \| 0.8960 \|
	\| 0.5356 \| 13.41 \| 2800 \| 0.6893 \| 0.8457 \| 0.8565 \| 0.8457 \| 0.8452 \| 0.8921 \|


	### 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-3
	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-3

	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: 0.6214
	- Accuracy: 0.8544
	- Precision: 0.8720
	- Recall: 0.8544
	- F1: 0.8540
	- Binary: 0.8989

	## 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
	- lr_scheduler_warmup_steps: 500
	- num_epochs: 100
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| Binary \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\|:------:\|
	\| No log \| 0.24 \| 50 \| 4.4176 \| 0.0172 \| 0.0140 \| 0.0172 \| 0.0083 \| 0.1549 \|
	\| No log \| 0.48 \| 100 \| 4.3072 \| 0.0434 \| 0.0497 \| 0.0434 \| 0.0197 \| 0.2778 \|
	\| No log \| 0.72 \| 150 \| 3.9604 \| 0.0906 \| 0.0606 \| 0.0906 \| 0.0446 \| 0.3581 \|
	\| No log \| 0.96 \| 200 \| 3.6355 \| 0.1191 \| 0.0507 \| 0.1191 \| 0.0558 \| 0.3783 \|
	\| 4.235 \| 1.2 \| 250 \| 3.3489 \| 0.1700 \| 0.0800 \| 0.1700 \| 0.0858 \| 0.4157 \|
	\| 4.235 \| 1.44 \| 300 \| 3.1008 \| 0.2015 \| 0.1270 \| 0.2015 \| 0.1158 \| 0.4382 \|
	\| 4.235 \| 1.68 \| 350 \| 2.8220 \| 0.2906 \| 0.2075 \| 0.2906 \| 0.2012 \| 0.5014 \|
	\| 4.235 \| 1.92 \| 400 \| 2.5557 \| 0.3843 \| 0.3160 \| 0.3843 \| 0.3099 \| 0.5671 \|
	\| 3.2055 \| 2.16 \| 450 \| 2.1790 \| 0.4801 \| 0.4047 \| 0.4801 \| 0.4036 \| 0.6344 \|
	\| 3.2055 \| 2.4 \| 500 \| 1.9034 \| 0.5790 \| 0.5557 \| 0.5790 \| 0.5261 \| 0.7028 \|
	\| 3.2055 \| 2.63 \| 550 \| 1.6707 \| 0.6135 \| 0.6116 \| 0.6135 \| 0.5701 \| 0.7273 \|
	\| 3.2055 \| 2.87 \| 600 \| 1.4658 \| 0.6285 \| 0.6047 \| 0.6285 \| 0.5817 \| 0.7381 \|
	\| 2.1878 \| 3.11 \| 650 \| 1.3665 \| 0.6457 \| 0.6522 \| 0.6457 \| 0.6153 \| 0.7534 \|
	\| 2.1878 \| 3.35 \| 700 \| 1.2309 \| 0.6757 \| 0.6806 \| 0.6757 \| 0.6446 \| 0.7730 \|
	\| 2.1878 \| 3.59 \| 750 \| 1.1077 \| 0.7169 \| 0.7307 \| 0.7169 \| 0.6966 \| 0.7999 \|
	\| 2.1878 \| 3.83 \| 800 \| 1.0393 \| 0.7341 \| 0.7548 \| 0.7341 \| 0.7226 \| 0.8130 \|
	\| 1.534 \| 4.07 \| 850 \| 0.9478 \| 0.7678 \| 0.7794 \| 0.7678 \| 0.7572 \| 0.8384 \|
	\| 1.534 \| 4.31 \| 900 \| 0.8755 \| 0.7715 \| 0.7789 \| 0.7715 \| 0.7627 \| 0.8395 \|
	\| 1.534 \| 4.55 \| 950 \| 0.8563 \| 0.7618 \| 0.7737 \| 0.7618 \| 0.7491 \| 0.8330 \|
	\| 1.534 \| 4.79 \| 1000 \| 0.7866 \| 0.8007 \| 0.8046 \| 0.8007 \| 0.7921 \| 0.8616 \|
	\| 1.2035 \| 5.03 \| 1050 \| 0.7462 \| 0.8007 \| 0.8212 \| 0.8007 \| 0.7945 \| 0.8591 \|
	\| 1.2035 \| 5.27 \| 1100 \| 0.7003 \| 0.8157 \| 0.8272 \| 0.8157 \| 0.8126 \| 0.8717 \|
	\| 1.2035 \| 5.51 \| 1150 \| 0.7421 \| 0.8105 \| 0.8262 \| 0.8105 \| 0.8074 \| 0.8672 \|
	\| 1.2035 \| 5.75 \| 1200 \| 0.7638 \| 0.7993 \| 0.8294 \| 0.7993 \| 0.7979 \| 0.8595 \|
	\| 1.2035 \| 5.99 \| 1250 \| 0.6872 \| 0.8187 \| 0.8330 \| 0.8187 \| 0.8171 \| 0.8742 \|
	\| 0.9909 \| 6.23 \| 1300 \| 0.6378 \| 0.8345 \| 0.8462 \| 0.8345 \| 0.8338 \| 0.8840 \|
	\| 0.9909 \| 6.47 \| 1350 \| 0.6835 \| 0.8075 \| 0.8266 \| 0.8075 \| 0.8063 \| 0.8669 \|
	\| 0.9909 \| 6.71 \| 1400 \| 0.6367 \| 0.8345 \| 0.8480 \| 0.8345 \| 0.8337 \| 0.8874 \|
	\| 0.9909 \| 6.95 \| 1450 \| 0.5793 \| 0.8434 \| 0.8521 \| 0.8434 \| 0.8425 \| 0.8931 \|
	\| 0.8826 \| 7.19 \| 1500 \| 0.6528 \| 0.8307 \| 0.8458 \| 0.8307 \| 0.8293 \| 0.8824 \|
	\| 0.8826 \| 7.43 \| 1550 \| 0.6361 \| 0.8225 \| 0.8382 \| 0.8225 \| 0.8218 \| 0.8761 \|
	\| 0.8826 \| 7.66 \| 1600 \| 0.6189 \| 0.8360 \| 0.8478 \| 0.8360 \| 0.8334 \| 0.8855 \|
	\| 0.8826 \| 7.9 \| 1650 \| 0.6078 \| 0.8337 \| 0.8433 \| 0.8337 \| 0.8321 \| 0.8831 \|
	\| 0.7752 \| 8.14 \| 1700 \| 0.6868 \| 0.8315 \| 0.8436 \| 0.8315 \| 0.8289 \| 0.8835 \|
	\| 0.7752 \| 8.38 \| 1750 \| 0.6118 \| 0.8419 \| 0.8549 \| 0.8419 \| 0.8411 \| 0.8897 \|
	\| 0.7752 \| 8.62 \| 1800 \| 0.5837 \| 0.8532 \| 0.8660 \| 0.8532 \| 0.8531 \| 0.8974 \|
	\| 0.7752 \| 8.86 \| 1850 \| 0.5758 \| 0.8487 \| 0.8613 \| 0.8487 \| 0.8494 \| 0.8956 \|
	\| 0.7067 \| 9.1 \| 1900 \| 0.6950 \| 0.8307 \| 0.8490 \| 0.8307 \| 0.8279 \| 0.8827 \|
	\| 0.7067 \| 9.34 \| 1950 \| 0.5968 \| 0.8479 \| 0.8595 \| 0.8479 \| 0.8470 \| 0.8942 \|
	\| 0.7067 \| 9.58 \| 2000 \| 0.5714 \| 0.8614 \| 0.8696 \| 0.8614 \| 0.8613 \| 0.9035 \|
	\| 0.7067 \| 9.82 \| 2050 \| 0.6389 \| 0.8427 \| 0.8538 \| 0.8427 \| 0.8415 \| 0.8903 \|
	\| 0.6457 \| 10.06 \| 2100 \| 0.6504 \| 0.8502 \| 0.8639 \| 0.8502 \| 0.8504 \| 0.8948 \|
	\| 0.6457 \| 10.3 \| 2150 \| 0.5776 \| 0.8547 \| 0.8659 \| 0.8547 \| 0.8534 \| 0.8988 \|
	\| 0.6457 \| 10.54 \| 2200 \| 0.6775 \| 0.8434 \| 0.8570 \| 0.8434 \| 0.8438 \| 0.8912 \|
	\| 0.6457 \| 10.78 \| 2250 \| 0.5849 \| 0.8569 \| 0.8686 \| 0.8569 \| 0.8579 \| 0.9013 \|
	\| 0.6098 \| 11.02 \| 2300 \| 0.5767 \| 0.8622 \| 0.8706 \| 0.8622 \| 0.8632 \| 0.9037 \|
	\| 0.6098 \| 11.26 \| 2350 \| 0.6875 \| 0.8404 \| 0.8588 \| 0.8404 \| 0.8404 \| 0.8898 \|
	\| 0.6098 \| 11.5 \| 2400 \| 0.7397 \| 0.8352 \| 0.8483 \| 0.8352 \| 0.8340 \| 0.8865 \|
	\| 0.6098 \| 11.74 \| 2450 \| 0.5998 \| 0.8629 \| 0.8716 \| 0.8629 \| 0.8618 \| 0.9053 \|
	\| 0.6098 \| 11.98 \| 2500 \| 0.6435 \| 0.8449 \| 0.8549 \| 0.8449 \| 0.8441 \| 0.8918 \|
	\| 0.5538 \| 12.22 \| 2550 \| 0.6969 \| 0.8502 \| 0.8640 \| 0.8502 \| 0.8508 \| 0.8965 \|
	\| 0.5538 \| 12.46 \| 2600 \| 0.6323 \| 0.8577 \| 0.8710 \| 0.8577 \| 0.8566 \| 0.9006 \|
	\| 0.5538 \| 12.69 \| 2650 \| 0.6989 \| 0.8532 \| 0.8660 \| 0.8532 \| 0.8525 \| 0.8981 \|
	\| 0.5538 \| 12.93 \| 2700 \| 0.6736 \| 0.8554 \| 0.8666 \| 0.8554 \| 0.8552 \| 0.8994 \|
	\| 0.5356 \| 13.17 \| 2750 \| 0.6737 \| 0.8487 \| 0.8584 \| 0.8487 \| 0.8469 \| 0.8960 \|
	\| 0.5356 \| 13.41 \| 2800 \| 0.6893 \| 0.8457 \| 0.8565 \| 0.8457 \| 0.8452 \| 0.8921 \|


	### Framework versions

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