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--- |
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license: apache-2.0 |
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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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model-index: |
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- name: wav2vec2-base-EMOPIA |
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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-base-EMOPIA |
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9346 |
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- Accuracy: 0.7000 |
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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: 1e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 3 |
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- total_train_batch_size: 12 |
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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: 300 |
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- num_epochs: 15 |
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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 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 1.3867 | 0.99 | 58 | 1.3518 | 0.3286 | |
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| 1.2842 | 1.99 | 116 | 1.1872 | 0.4571 | |
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| 1.0725 | 2.99 | 174 | 1.0476 | 0.5571 | |
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| 0.9343 | 3.99 | 232 | 0.9677 | 0.5714 | |
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| 0.8053 | 4.99 | 290 | 0.9525 | 0.6143 | |
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| 0.7895 | 5.99 | 348 | 0.7735 | 0.6857 | |
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| 0.6867 | 6.99 | 406 | 0.8556 | 0.6429 | |
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| 0.6218 | 7.99 | 464 | 0.8454 | 0.6714 | |
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| 0.558 | 8.99 | 522 | 0.8405 | 0.6571 | |
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| 0.5033 | 9.99 | 580 | 1.0190 | 0.6286 | |
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| 0.4403 | 10.99 | 638 | 0.8249 | 0.7000 | |
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| 0.3995 | 11.99 | 696 | 0.8997 | 0.7143 | |
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| 0.3534 | 12.99 | 754 | 0.9177 | 0.7000 | |
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| 0.3023 | 13.99 | 812 | 0.9544 | 0.6571 | |
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| 0.2752 | 14.99 | 870 | 0.9346 | 0.7000 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.10.1+cu102 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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