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	---
	library_name: transformers
	license: apache-2.0
	base_model: facebook/wav2vec2-base
	tags:
	- generated_from_trainer
	datasets:
	- minds14
	metrics:
	- accuracy
	model-index:
	- name: test_model_dir
	results:
	- task:
	name: Audio Classification
	type: audio-classification
	dataset:
	name: minds14
	type: minds14
	config: en-US
	split: train
	args: en-US
	metrics:
	- name: Accuracy
	type: accuracy
	value: 0.08849557522123894
	---

	<!-- 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. -->

	# test_model_dir

	This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset.
	It achieves the following results on the evaluation set:
	- Loss: 2.7181
	- Accuracy: 0.0885

	## 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: 64
	- eval_batch_size: 64
	- seed: 42
	- gradient_accumulation_steps: 4
	- total_train_batch_size: 256
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_ratio: 0.1
	- num_epochs: 100

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|
	\| No log \| 1.0 \| 2 \| 2.6433 \| 0.0354 \|
	\| No log \| 2.0 \| 4 \| 2.6427 \| 0.0265 \|
	\| No log \| 3.0 \| 6 \| 2.6412 \| 0.0619 \|
	\| No log \| 4.0 \| 8 \| 2.6391 \| 0.0885 \|
	\| 2.6378 \| 5.0 \| 10 \| 2.6384 \| 0.1239 \|
	\| 2.6378 \| 6.0 \| 12 \| 2.6380 \| 0.0973 \|
	\| 2.6378 \| 7.0 \| 14 \| 2.6375 \| 0.0708 \|
	\| 2.6378 \| 8.0 \| 16 \| 2.6415 \| 0.0796 \|
	\| 2.6378 \| 9.0 \| 18 \| 2.6399 \| 0.0531 \|
	\| 2.6288 \| 10.0 \| 20 \| 2.6450 \| 0.0796 \|
	\| 2.6288 \| 11.0 \| 22 \| 2.6450 \| 0.0619 \|
	\| 2.6288 \| 12.0 \| 24 \| 2.6452 \| 0.0708 \|
	\| 2.6288 \| 13.0 \| 26 \| 2.6479 \| 0.0708 \|
	\| 2.6288 \| 14.0 \| 28 \| 2.6496 \| 0.0619 \|
	\| 2.6185 \| 15.0 \| 30 \| 2.6522 \| 0.0796 \|
	\| 2.6185 \| 16.0 \| 32 \| 2.6558 \| 0.0796 \|
	\| 2.6185 \| 17.0 \| 34 \| 2.6567 \| 0.0708 \|
	\| 2.6185 \| 18.0 \| 36 \| 2.6572 \| 0.0619 \|
	\| 2.6185 \| 19.0 \| 38 \| 2.6611 \| 0.0619 \|
	\| 2.6069 \| 20.0 \| 40 \| 2.6629 \| 0.0619 \|
	\| 2.6069 \| 21.0 \| 42 \| 2.6621 \| 0.0531 \|
	\| 2.6069 \| 22.0 \| 44 \| 2.6663 \| 0.0531 \|
	\| 2.6069 \| 23.0 \| 46 \| 2.6672 \| 0.0442 \|
	\| 2.6069 \| 24.0 \| 48 \| 2.6645 \| 0.0531 \|
	\| 2.599 \| 25.0 \| 50 \| 2.6670 \| 0.0708 \|
	\| 2.599 \| 26.0 \| 52 \| 2.6692 \| 0.0531 \|
	\| 2.599 \| 27.0 \| 54 \| 2.6653 \| 0.0708 \|
	\| 2.599 \| 28.0 \| 56 \| 2.6669 \| 0.0885 \|
	\| 2.599 \| 29.0 \| 58 \| 2.6797 \| 0.0619 \|
	\| 2.5767 \| 30.0 \| 60 \| 2.6781 \| 0.0354 \|
	\| 2.5767 \| 31.0 \| 62 \| 2.6861 \| 0.0265 \|
	\| 2.5767 \| 32.0 \| 64 \| 2.6852 \| 0.0442 \|
	\| 2.5767 \| 33.0 \| 66 \| 2.6733 \| 0.0442 \|
	\| 2.5767 \| 34.0 \| 68 \| 2.6881 \| 0.0708 \|
	\| 2.5771 \| 35.0 \| 70 \| 2.6800 \| 0.0708 \|
	\| 2.5771 \| 36.0 \| 72 \| 2.6777 \| 0.0619 \|
	\| 2.5771 \| 37.0 \| 74 \| 2.6761 \| 0.0708 \|
	\| 2.5771 \| 38.0 \| 76 \| 2.6657 \| 0.0619 \|
	\| 2.5771 \| 39.0 \| 78 \| 2.6667 \| 0.0708 \|
	\| 2.5636 \| 40.0 \| 80 \| 2.6681 \| 0.0708 \|
	\| 2.5636 \| 41.0 \| 82 \| 2.6649 \| 0.0796 \|
	\| 2.5636 \| 42.0 \| 84 \| 2.6598 \| 0.0796 \|
	\| 2.5636 \| 43.0 \| 86 \| 2.6627 \| 0.0619 \|
	\| 2.5636 \| 44.0 \| 88 \| 2.6596 \| 0.0796 \|
	\| 2.5608 \| 45.0 \| 90 \| 2.6511 \| 0.0796 \|
	\| 2.5608 \| 46.0 \| 92 \| 2.6522 \| 0.0708 \|
	\| 2.5608 \| 47.0 \| 94 \| 2.6610 \| 0.0708 \|
	\| 2.5608 \| 48.0 \| 96 \| 2.6638 \| 0.0531 \|
	\| 2.5608 \| 49.0 \| 98 \| 2.6642 \| 0.0619 \|
	\| 2.5432 \| 50.0 \| 100 \| 2.6596 \| 0.0796 \|
	\| 2.5432 \| 51.0 \| 102 \| 2.6675 \| 0.0885 \|
	\| 2.5432 \| 52.0 \| 104 \| 2.6964 \| 0.0885 \|
	\| 2.5432 \| 53.0 \| 106 \| 2.7030 \| 0.0531 \|
	\| 2.5432 \| 54.0 \| 108 \| 2.7016 \| 0.0531 \|
	\| 2.5295 \| 55.0 \| 110 \| 2.6918 \| 0.0619 \|
	\| 2.5295 \| 56.0 \| 112 \| 2.6893 \| 0.0619 \|
	\| 2.5295 \| 57.0 \| 114 \| 2.6936 \| 0.0708 \|
	\| 2.5295 \| 58.0 \| 116 \| 2.6905 \| 0.0885 \|
	\| 2.5295 \| 59.0 \| 118 \| 2.6838 \| 0.0796 \|
	\| 2.5207 \| 60.0 \| 120 \| 2.6845 \| 0.0708 \|
	\| 2.5207 \| 61.0 \| 122 \| 2.6896 \| 0.0708 \|
	\| 2.5207 \| 62.0 \| 124 \| 2.6965 \| 0.0796 \|
	\| 2.5207 \| 63.0 \| 126 \| 2.6971 \| 0.1062 \|
	\| 2.5207 \| 64.0 \| 128 \| 2.6982 \| 0.0973 \|
	\| 2.5015 \| 65.0 \| 130 \| 2.7037 \| 0.0885 \|
	\| 2.5015 \| 66.0 \| 132 \| 2.7065 \| 0.0973 \|
	\| 2.5015 \| 67.0 \| 134 \| 2.7078 \| 0.0973 \|
	\| 2.5015 \| 68.0 \| 136 \| 2.7055 \| 0.0973 \|
	\| 2.5015 \| 69.0 \| 138 \| 2.7023 \| 0.0973 \|
	\| 2.4869 \| 70.0 \| 140 \| 2.6923 \| 0.1062 \|
	\| 2.4869 \| 71.0 \| 142 \| 2.6906 \| 0.1062 \|
	\| 2.4869 \| 72.0 \| 144 \| 2.6989 \| 0.1062 \|
	\| 2.4869 \| 73.0 \| 146 \| 2.7078 \| 0.0885 \|
	\| 2.4869 \| 74.0 \| 148 \| 2.7106 \| 0.0973 \|
	\| 2.4638 \| 75.0 \| 150 \| 2.7117 \| 0.0796 \|
	\| 2.4638 \| 76.0 \| 152 \| 2.7119 \| 0.0796 \|
	\| 2.4638 \| 77.0 \| 154 \| 2.7153 \| 0.0708 \|
	\| 2.4638 \| 78.0 \| 156 \| 2.7111 \| 0.0708 \|
	\| 2.4638 \| 79.0 \| 158 \| 2.7086 \| 0.0885 \|
	\| 2.4408 \| 80.0 \| 160 \| 2.7000 \| 0.1150 \|
	\| 2.4408 \| 81.0 \| 162 \| 2.6915 \| 0.1062 \|
	\| 2.4408 \| 82.0 \| 164 \| 2.6907 \| 0.1062 \|
	\| 2.4408 \| 83.0 \| 166 \| 2.6908 \| 0.0973 \|
	\| 2.4408 \| 84.0 \| 168 \| 2.6926 \| 0.0796 \|
	\| 2.4688 \| 85.0 \| 170 \| 2.6984 \| 0.1062 \|
	\| 2.4688 \| 86.0 \| 172 \| 2.7039 \| 0.1062 \|
	\| 2.4688 \| 87.0 \| 174 \| 2.7053 \| 0.0973 \|
	\| 2.4688 \| 88.0 \| 176 \| 2.7098 \| 0.0796 \|
	\| 2.4688 \| 89.0 \| 178 \| 2.7100 \| 0.0885 \|
	\| 2.4379 \| 90.0 \| 180 \| 2.7113 \| 0.1062 \|
	\| 2.4379 \| 91.0 \| 182 \| 2.7121 \| 0.0973 \|
	\| 2.4379 \| 92.0 \| 184 \| 2.7127 \| 0.0973 \|
	\| 2.4379 \| 93.0 \| 186 \| 2.7162 \| 0.0973 \|
	\| 2.4379 \| 94.0 \| 188 \| 2.7189 \| 0.0973 \|
	\| 2.4385 \| 95.0 \| 190 \| 2.7199 \| 0.0885 \|
	\| 2.4385 \| 96.0 \| 192 \| 2.7186 \| 0.0796 \|
	\| 2.4385 \| 97.0 \| 194 \| 2.7182 \| 0.0885 \|
	\| 2.4385 \| 98.0 \| 196 \| 2.7183 \| 0.0885 \|
	\| 2.4385 \| 99.0 \| 198 \| 2.7182 \| 0.0885 \|
	\| 2.4402 \| 100.0 \| 200 \| 2.7181 \| 0.0885 \|


	### Framework versions

	- Transformers 4.44.2
	- Pytorch 2.4.0+cu121
	- Datasets 2.21.0
	- Tokenizers 0.19.1

	---
	library_name: transformers
	license: apache-2.0
	base_model: facebook/wav2vec2-base
	tags:
	- generated_from_trainer
	datasets:
	- minds14
	metrics:
	- accuracy
	model-index:
	- name: test_model_dir
	results:
	- task:
	name: Audio Classification
	type: audio-classification
	dataset:
	name: minds14
	type: minds14
	config: en-US
	split: train
	args: en-US
	metrics:
	- name: Accuracy
	type: accuracy
	value: 0.08849557522123894
	---

	<!-- 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. -->

	# test_model_dir

	This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset.
	It achieves the following results on the evaluation set:
	- Loss: 2.7181
	- Accuracy: 0.0885

	## 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: 64
	- eval_batch_size: 64
	- seed: 42
	- gradient_accumulation_steps: 4
	- total_train_batch_size: 256
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_ratio: 0.1
	- num_epochs: 100

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|
	\| No log \| 1.0 \| 2 \| 2.6433 \| 0.0354 \|
	\| No log \| 2.0 \| 4 \| 2.6427 \| 0.0265 \|
	\| No log \| 3.0 \| 6 \| 2.6412 \| 0.0619 \|
	\| No log \| 4.0 \| 8 \| 2.6391 \| 0.0885 \|
	\| 2.6378 \| 5.0 \| 10 \| 2.6384 \| 0.1239 \|
	\| 2.6378 \| 6.0 \| 12 \| 2.6380 \| 0.0973 \|
	\| 2.6378 \| 7.0 \| 14 \| 2.6375 \| 0.0708 \|
	\| 2.6378 \| 8.0 \| 16 \| 2.6415 \| 0.0796 \|
	\| 2.6378 \| 9.0 \| 18 \| 2.6399 \| 0.0531 \|
	\| 2.6288 \| 10.0 \| 20 \| 2.6450 \| 0.0796 \|
	\| 2.6288 \| 11.0 \| 22 \| 2.6450 \| 0.0619 \|
	\| 2.6288 \| 12.0 \| 24 \| 2.6452 \| 0.0708 \|
	\| 2.6288 \| 13.0 \| 26 \| 2.6479 \| 0.0708 \|
	\| 2.6288 \| 14.0 \| 28 \| 2.6496 \| 0.0619 \|
	\| 2.6185 \| 15.0 \| 30 \| 2.6522 \| 0.0796 \|
	\| 2.6185 \| 16.0 \| 32 \| 2.6558 \| 0.0796 \|
	\| 2.6185 \| 17.0 \| 34 \| 2.6567 \| 0.0708 \|
	\| 2.6185 \| 18.0 \| 36 \| 2.6572 \| 0.0619 \|
	\| 2.6185 \| 19.0 \| 38 \| 2.6611 \| 0.0619 \|
	\| 2.6069 \| 20.0 \| 40 \| 2.6629 \| 0.0619 \|
	\| 2.6069 \| 21.0 \| 42 \| 2.6621 \| 0.0531 \|
	\| 2.6069 \| 22.0 \| 44 \| 2.6663 \| 0.0531 \|
	\| 2.6069 \| 23.0 \| 46 \| 2.6672 \| 0.0442 \|
	\| 2.6069 \| 24.0 \| 48 \| 2.6645 \| 0.0531 \|
	\| 2.599 \| 25.0 \| 50 \| 2.6670 \| 0.0708 \|
	\| 2.599 \| 26.0 \| 52 \| 2.6692 \| 0.0531 \|
	\| 2.599 \| 27.0 \| 54 \| 2.6653 \| 0.0708 \|
	\| 2.599 \| 28.0 \| 56 \| 2.6669 \| 0.0885 \|
	\| 2.599 \| 29.0 \| 58 \| 2.6797 \| 0.0619 \|
	\| 2.5767 \| 30.0 \| 60 \| 2.6781 \| 0.0354 \|
	\| 2.5767 \| 31.0 \| 62 \| 2.6861 \| 0.0265 \|
	\| 2.5767 \| 32.0 \| 64 \| 2.6852 \| 0.0442 \|
	\| 2.5767 \| 33.0 \| 66 \| 2.6733 \| 0.0442 \|
	\| 2.5767 \| 34.0 \| 68 \| 2.6881 \| 0.0708 \|
	\| 2.5771 \| 35.0 \| 70 \| 2.6800 \| 0.0708 \|
	\| 2.5771 \| 36.0 \| 72 \| 2.6777 \| 0.0619 \|
	\| 2.5771 \| 37.0 \| 74 \| 2.6761 \| 0.0708 \|
	\| 2.5771 \| 38.0 \| 76 \| 2.6657 \| 0.0619 \|
	\| 2.5771 \| 39.0 \| 78 \| 2.6667 \| 0.0708 \|
	\| 2.5636 \| 40.0 \| 80 \| 2.6681 \| 0.0708 \|
	\| 2.5636 \| 41.0 \| 82 \| 2.6649 \| 0.0796 \|
	\| 2.5636 \| 42.0 \| 84 \| 2.6598 \| 0.0796 \|
	\| 2.5636 \| 43.0 \| 86 \| 2.6627 \| 0.0619 \|
	\| 2.5636 \| 44.0 \| 88 \| 2.6596 \| 0.0796 \|
	\| 2.5608 \| 45.0 \| 90 \| 2.6511 \| 0.0796 \|
	\| 2.5608 \| 46.0 \| 92 \| 2.6522 \| 0.0708 \|
	\| 2.5608 \| 47.0 \| 94 \| 2.6610 \| 0.0708 \|
	\| 2.5608 \| 48.0 \| 96 \| 2.6638 \| 0.0531 \|
	\| 2.5608 \| 49.0 \| 98 \| 2.6642 \| 0.0619 \|
	\| 2.5432 \| 50.0 \| 100 \| 2.6596 \| 0.0796 \|
	\| 2.5432 \| 51.0 \| 102 \| 2.6675 \| 0.0885 \|
	\| 2.5432 \| 52.0 \| 104 \| 2.6964 \| 0.0885 \|
	\| 2.5432 \| 53.0 \| 106 \| 2.7030 \| 0.0531 \|
	\| 2.5432 \| 54.0 \| 108 \| 2.7016 \| 0.0531 \|
	\| 2.5295 \| 55.0 \| 110 \| 2.6918 \| 0.0619 \|
	\| 2.5295 \| 56.0 \| 112 \| 2.6893 \| 0.0619 \|
	\| 2.5295 \| 57.0 \| 114 \| 2.6936 \| 0.0708 \|
	\| 2.5295 \| 58.0 \| 116 \| 2.6905 \| 0.0885 \|
	\| 2.5295 \| 59.0 \| 118 \| 2.6838 \| 0.0796 \|
	\| 2.5207 \| 60.0 \| 120 \| 2.6845 \| 0.0708 \|
	\| 2.5207 \| 61.0 \| 122 \| 2.6896 \| 0.0708 \|
	\| 2.5207 \| 62.0 \| 124 \| 2.6965 \| 0.0796 \|
	\| 2.5207 \| 63.0 \| 126 \| 2.6971 \| 0.1062 \|
	\| 2.5207 \| 64.0 \| 128 \| 2.6982 \| 0.0973 \|
	\| 2.5015 \| 65.0 \| 130 \| 2.7037 \| 0.0885 \|
	\| 2.5015 \| 66.0 \| 132 \| 2.7065 \| 0.0973 \|
	\| 2.5015 \| 67.0 \| 134 \| 2.7078 \| 0.0973 \|
	\| 2.5015 \| 68.0 \| 136 \| 2.7055 \| 0.0973 \|
	\| 2.5015 \| 69.0 \| 138 \| 2.7023 \| 0.0973 \|
	\| 2.4869 \| 70.0 \| 140 \| 2.6923 \| 0.1062 \|
	\| 2.4869 \| 71.0 \| 142 \| 2.6906 \| 0.1062 \|
	\| 2.4869 \| 72.0 \| 144 \| 2.6989 \| 0.1062 \|
	\| 2.4869 \| 73.0 \| 146 \| 2.7078 \| 0.0885 \|
	\| 2.4869 \| 74.0 \| 148 \| 2.7106 \| 0.0973 \|
	\| 2.4638 \| 75.0 \| 150 \| 2.7117 \| 0.0796 \|
	\| 2.4638 \| 76.0 \| 152 \| 2.7119 \| 0.0796 \|
	\| 2.4638 \| 77.0 \| 154 \| 2.7153 \| 0.0708 \|
	\| 2.4638 \| 78.0 \| 156 \| 2.7111 \| 0.0708 \|
	\| 2.4638 \| 79.0 \| 158 \| 2.7086 \| 0.0885 \|
	\| 2.4408 \| 80.0 \| 160 \| 2.7000 \| 0.1150 \|
	\| 2.4408 \| 81.0 \| 162 \| 2.6915 \| 0.1062 \|
	\| 2.4408 \| 82.0 \| 164 \| 2.6907 \| 0.1062 \|
	\| 2.4408 \| 83.0 \| 166 \| 2.6908 \| 0.0973 \|
	\| 2.4408 \| 84.0 \| 168 \| 2.6926 \| 0.0796 \|
	\| 2.4688 \| 85.0 \| 170 \| 2.6984 \| 0.1062 \|
	\| 2.4688 \| 86.0 \| 172 \| 2.7039 \| 0.1062 \|
	\| 2.4688 \| 87.0 \| 174 \| 2.7053 \| 0.0973 \|
	\| 2.4688 \| 88.0 \| 176 \| 2.7098 \| 0.0796 \|
	\| 2.4688 \| 89.0 \| 178 \| 2.7100 \| 0.0885 \|
	\| 2.4379 \| 90.0 \| 180 \| 2.7113 \| 0.1062 \|
	\| 2.4379 \| 91.0 \| 182 \| 2.7121 \| 0.0973 \|
	\| 2.4379 \| 92.0 \| 184 \| 2.7127 \| 0.0973 \|
	\| 2.4379 \| 93.0 \| 186 \| 2.7162 \| 0.0973 \|
	\| 2.4379 \| 94.0 \| 188 \| 2.7189 \| 0.0973 \|
	\| 2.4385 \| 95.0 \| 190 \| 2.7199 \| 0.0885 \|
	\| 2.4385 \| 96.0 \| 192 \| 2.7186 \| 0.0796 \|
	\| 2.4385 \| 97.0 \| 194 \| 2.7182 \| 0.0885 \|
	\| 2.4385 \| 98.0 \| 196 \| 2.7183 \| 0.0885 \|
	\| 2.4385 \| 99.0 \| 198 \| 2.7182 \| 0.0885 \|
	\| 2.4402 \| 100.0 \| 200 \| 2.7181 \| 0.0885 \|


	### Framework versions

	- Transformers 4.44.2
	- Pytorch 2.4.0+cu121
	- Datasets 2.21.0
	- Tokenizers 0.19.1