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wav2vec2-age-gender-balancedset

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mlmarian/wav2vec2-large-robust-6-ft-age-gender

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  2. model.safetensors +1 -1
  3. runs/Sep18_11-59-32_9fd7fc9597e5/events.out.tfevents.1726661208.9fd7fc9597e5.307.0 +2 -2
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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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+
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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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+
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+ # wav2vec2-age-gender-balancedset
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+
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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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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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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+
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+
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+ ### Framework versions
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+
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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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