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--- |
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base_model: microsoft/wavlm-base-plus |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: wavlm_base-plus_emodb |
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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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# wavlm_base-plus_emodb |
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This model is a fine-tuned version of [microsoft/wavlm-base-plus](https://huggingface.co/microsoft/wavlm-base-plus) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1390 |
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- Uar: 0.6759 |
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- Acc: 0.7426 |
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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: 0.0001 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Uar | Acc | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:| |
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| No log | 0.31 | 1 | 1.3804 | 0.3148 | 0.4559 | |
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| No log | 0.62 | 2 | 1.3739 | 0.2593 | 0.4118 | |
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| No log | 0.92 | 3 | 1.3586 | 0.25 | 0.4044 | |
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| 1.4729 | 1.23 | 4 | 1.3445 | 0.25 | 0.4044 | |
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| 1.4729 | 1.54 | 5 | 1.3265 | 0.3056 | 0.4485 | |
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| 1.4729 | 1.85 | 6 | 1.3054 | 0.4167 | 0.5368 | |
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| 1.3428 | 2.15 | 7 | 1.2888 | 0.4352 | 0.5515 | |
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| 1.3428 | 2.46 | 8 | 1.2719 | 0.4630 | 0.5735 | |
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| 1.3428 | 2.77 | 9 | 1.2511 | 0.5093 | 0.6103 | |
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| 1.2214 | 3.08 | 10 | 1.2465 | 0.5833 | 0.6691 | |
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| 1.2214 | 3.38 | 11 | 1.2409 | 0.5370 | 0.6324 | |
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| 1.2214 | 3.69 | 12 | 1.2366 | 0.5000 | 0.6029 | |
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| 1.2214 | 4.0 | 13 | 1.2346 | 0.5185 | 0.6176 | |
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| 0.7965 | 4.31 | 14 | 1.2130 | 0.6574 | 0.7279 | |
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| 0.7965 | 4.62 | 15 | 1.1881 | 0.7222 | 0.7794 | |
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| 0.7965 | 4.92 | 16 | 1.1775 | 0.7407 | 0.7941 | |
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| 0.9522 | 5.23 | 17 | 1.1707 | 0.7315 | 0.7868 | |
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| 0.9522 | 5.54 | 18 | 1.1667 | 0.7222 | 0.7794 | |
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| 0.9522 | 5.85 | 19 | 1.1636 | 0.7130 | 0.7721 | |
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| 0.8702 | 6.15 | 20 | 1.1628 | 0.7037 | 0.7647 | |
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| 0.8702 | 6.46 | 21 | 1.1557 | 0.7037 | 0.7647 | |
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| 0.8702 | 6.77 | 22 | 1.1444 | 0.7130 | 0.7721 | |
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| 0.7803 | 7.08 | 23 | 1.1378 | 0.7130 | 0.7721 | |
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| 0.7803 | 7.38 | 24 | 1.1331 | 0.7130 | 0.7721 | |
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| 0.7803 | 7.69 | 25 | 1.1339 | 0.7037 | 0.7647 | |
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| 0.7803 | 8.0 | 26 | 1.1363 | 0.6944 | 0.7574 | |
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| 0.5654 | 8.31 | 27 | 1.1382 | 0.6759 | 0.7426 | |
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| 0.5654 | 8.62 | 28 | 1.1394 | 0.6759 | 0.7426 | |
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| 0.5654 | 8.92 | 29 | 1.1395 | 0.6759 | 0.7426 | |
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| 0.7148 | 9.23 | 30 | 1.1390 | 0.6759 | 0.7426 | |
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### Framework versions |
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- Transformers 4.32.0 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.13.3 |
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