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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: wavlm-large-finetuned-iemocap |
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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-large-finetuned-iemocap |
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This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1588 |
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- Accuracy: 0.4811 |
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- F1: 0.4602 |
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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: 3e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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_ratio: 0.1 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| 1.3733 | 0.98 | 25 | 1.3723 | 0.2502 | 0.1002 | |
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| 1.2784 | 1.98 | 50 | 1.3130 | 0.3307 | 0.2503 | |
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| 1.2228 | 2.98 | 75 | 1.2485 | 0.3899 | 0.3398 | |
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| 1.1588 | 3.98 | 100 | 1.2129 | 0.4646 | 0.4650 | |
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| 1.1116 | 4.98 | 125 | 1.1941 | 0.4753 | 0.4655 | |
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| 1.1212 | 5.98 | 150 | 1.1688 | 0.4762 | 0.4639 | |
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| 1.0919 | 6.98 | 175 | 1.1574 | 0.4850 | 0.4710 | |
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| 1.0749 | 7.98 | 200 | 1.1612 | 0.4840 | 0.4639 | |
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| 1.0943 | 8.98 | 225 | 1.1586 | 0.4888 | 0.4677 | |
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| 1.0746 | 9.98 | 250 | 1.1588 | 0.4811 | 0.4602 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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