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--- |
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library_name: transformers |
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license: cc-by-nc-sa-4.0 |
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base_model: audeering/wav2vec2-large-robust-6-ft-age-gender |
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tags: |
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- generated_from_trainer |
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datasets: |
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- arrow |
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metrics: |
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- accuracy |
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model-index: |
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- name: wav2vec2-age-gender-balancedset |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# wav2vec2-age-gender-balancedset |
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This model is a fine-tuned version of [audeering/wav2vec2-large-robust-6-ft-age-gender](https://huggingface.co./audeering/wav2vec2-large-robust-6-ft-age-gender) on the arrow dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.4630 |
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- Accuracy: 0.7529 |
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- F1 Score: 0.7799 |
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- Mse: 0.7442 |
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- Mae: 0.3779 |
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- Mae^m: 0.3325 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 9 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 18 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 30 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Mse | Mae | Mae^m | |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|:--------:|:------:|:------:|:------:| |
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| 1.7071 | 0.6557 | 100 | 1.6599 | 0.3120 | 0.2225 | 3.2857 | 1.2857 | 1.2279 | |
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| 1.28 | 1.3115 | 200 | 1.2170 | 0.5102 | 0.3996 | 1.3032 | 0.7201 | 0.7433 | |
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| 1.2241 | 1.9672 | 300 | 1.1266 | 0.5190 | 0.4383 | 1.3703 | 0.7172 | 0.8739 | |
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| 1.0071 | 2.6230 | 400 | 1.1116 | 0.5743 | 0.5019 | 1.1370 | 0.6006 | 0.7637 | |
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| 0.8658 | 3.2787 | 500 | 1.1158 | 0.6414 | 0.5407 | 1.4548 | 0.6035 | 0.6665 | |
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| 0.6653 | 3.9344 | 600 | 0.9547 | 0.6764 | 0.6021 | 0.8863 | 0.4723 | 0.5315 | |
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| 0.7106 | 4.5902 | 700 | 1.0466 | 0.6706 | 0.5908 | 0.9038 | 0.4840 | 0.6695 | |
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| 0.5326 | 5.2459 | 800 | 1.2520 | 0.6560 | 0.6961 | 0.7813 | 0.4723 | 0.4067 | |
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| 0.4258 | 5.9016 | 900 | 1.1431 | 0.6939 | 0.6080 | 0.8601 | 0.4519 | 0.5134 | |
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| 0.452 | 6.5574 | 1000 | 1.0243 | 0.7114 | 0.7066 | 0.8921 | 0.4431 | 0.3818 | |
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| 0.2399 | 7.2131 | 1100 | 1.2856 | 0.7114 | 0.7095 | 0.7901 | 0.4227 | 0.3683 | |
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| 0.1564 | 7.8689 | 1200 | 1.5014 | 0.6676 | 0.7050 | 1.1924 | 0.5394 | 0.4640 | |
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| 0.3962 | 8.5246 | 1300 | 1.3338 | 0.7230 | 0.7545 | 0.7551 | 0.4111 | 0.3599 | |
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| 0.1475 | 9.1803 | 1400 | 1.7256 | 0.6880 | 0.7230 | 0.8688 | 0.4665 | 0.4102 | |
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| 0.2757 | 9.8361 | 1500 | 1.6456 | 0.7085 | 0.7053 | 0.6501 | 0.3994 | 0.3469 | |
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| 0.1245 | 10.4918 | 1600 | 1.9034 | 0.7055 | 0.7464 | 0.8980 | 0.4490 | 0.3869 | |
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| 0.0902 | 11.1475 | 1700 | 1.8943 | 0.7289 | 0.7195 | 0.8163 | 0.4140 | 0.3626 | |
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| 0.1188 | 11.8033 | 1800 | 2.0529 | 0.7376 | 0.7657 | 0.7172 | 0.3848 | 0.3405 | |
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| 0.0573 | 12.4590 | 1900 | 2.0553 | 0.7172 | 0.7512 | 0.8484 | 0.4286 | 0.3666 | |
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| 0.1123 | 13.1148 | 2000 | 2.1915 | 0.7172 | 0.7538 | 0.7405 | 0.4082 | 0.3548 | |
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| 0.1463 | 13.7705 | 2100 | 2.0914 | 0.7259 | 0.7600 | 0.6589 | 0.3790 | 0.3272 | |
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| 0.3641 | 14.4262 | 2200 | 2.5501 | 0.6997 | 0.7388 | 0.8921 | 0.4490 | 0.3839 | |
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| 0.0495 | 15.0820 | 2300 | 2.5900 | 0.7026 | 0.6986 | 0.9038 | 0.4606 | 0.3989 | |
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| 0.0177 | 15.7377 | 2400 | 2.2336 | 0.7201 | 0.7564 | 0.8921 | 0.4373 | 0.3757 | |
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| 0.0291 | 16.3934 | 2500 | 2.6949 | 0.7347 | 0.7692 | 0.7405 | 0.3907 | 0.3391 | |
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| 0.0799 | 17.0492 | 2600 | 2.5497 | 0.7201 | 0.7479 | 0.7172 | 0.4023 | 0.3496 | |
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| 0.0982 | 17.7049 | 2700 | 2.4087 | 0.7464 | 0.7771 | 0.6822 | 0.3732 | 0.3203 | |
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| 0.072 | 18.3607 | 2800 | 2.2699 | 0.7289 | 0.7658 | 0.7172 | 0.3965 | 0.3402 | |
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| 0.1227 | 19.0164 | 2900 | 2.3906 | 0.7405 | 0.7758 | 0.6706 | 0.3732 | 0.3188 | |
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| 0.0075 | 19.6721 | 3000 | 2.3322 | 0.7376 | 0.7717 | 0.6472 | 0.3732 | 0.3210 | |
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| 0.0019 | 20.3279 | 3100 | 2.4514 | 0.7434 | 0.7779 | 0.6414 | 0.3673 | 0.3133 | |
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| 0.0295 | 20.9836 | 3200 | 2.4432 | 0.7493 | 0.7813 | 0.6735 | 0.3703 | 0.3170 | |
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| 0.0012 | 21.6393 | 3300 | 2.4851 | 0.7522 | 0.7859 | 0.5831 | 0.3440 | 0.2955 | |
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| 0.02 | 22.2951 | 3400 | 2.9030 | 0.7259 | 0.7609 | 0.7347 | 0.3965 | 0.3403 | |
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| 0.0341 | 22.9508 | 3500 | 2.6862 | 0.7347 | 0.7690 | 0.7464 | 0.3965 | 0.3412 | |
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| 0.0136 | 23.6066 | 3600 | 2.6282 | 0.7347 | 0.7685 | 0.7318 | 0.3936 | 0.3415 | |
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| 0.0252 | 24.2623 | 3700 | 2.7268 | 0.7376 | 0.7729 | 0.6297 | 0.3673 | 0.3129 | |
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| 0.0403 | 24.9180 | 3800 | 2.5494 | 0.7434 | 0.7753 | 0.6676 | 0.3703 | 0.3213 | |
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| 0.0013 | 25.5738 | 3900 | 2.4882 | 0.7580 | 0.7890 | 0.6735 | 0.3586 | 0.3083 | |
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| 0.0113 | 26.2295 | 4000 | 2.5213 | 0.7638 | 0.7937 | 0.5627 | 0.3294 | 0.2781 | |
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| 0.0371 | 26.8852 | 4100 | 2.6017 | 0.7638 | 0.7962 | 0.5394 | 0.3294 | 0.2785 | |
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| 0.0014 | 27.5410 | 4200 | 2.5145 | 0.7755 | 0.8044 | 0.5015 | 0.3090 | 0.2616 | |
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| 0.0107 | 28.1967 | 4300 | 2.4742 | 0.7726 | 0.8017 | 0.5131 | 0.3149 | 0.2717 | |
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| 0.0002 | 28.8525 | 4400 | 2.4811 | 0.7609 | 0.7915 | 0.5335 | 0.3294 | 0.2830 | |
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| 0.0001 | 29.5082 | 4500 | 2.4911 | 0.7609 | 0.7921 | 0.5481 | 0.3324 | 0.2842 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.0 |
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- Tokenizers 0.19.1 |
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