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--- |
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tags: |
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- audio-classification |
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- generated_from_trainer |
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- wolof |
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metrics: |
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- accuracy |
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- precision |
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- f1 |
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model-index: |
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- name: wavlm-base |
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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 |
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This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on the galsenai/waxal_dataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.1345 |
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- Accuracy: 0.6783 |
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- Precision: 0.8774 |
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- F1: 0.7615 |
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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: 30 |
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- eval_batch_size: 30 |
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- seed: 0 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 120 |
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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: 32.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:| |
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| 4.4506 | 2.53 | 500 | 4.8601 | 0.0224 | 0.0136 | 0.0066 | |
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| 3.0523 | 5.05 | 1000 | 4.6674 | 0.0720 | 0.0460 | 0.0394 | |
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| 1.949 | 7.58 | 1500 | 4.1533 | 0.1156 | 0.1847 | 0.1064 | |
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| 1.3427 | 10.1 | 2000 | 3.8173 | 0.1448 | 0.2382 | 0.1347 | |
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| 1.0064 | 12.63 | 2500 | 3.5546 | 0.2183 | 0.4464 | 0.2385 | |
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| 0.7985 | 15.15 | 3000 | 3.1172 | 0.3842 | 0.6336 | 0.4258 | |
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| 0.6505 | 17.68 | 3500 | 2.9231 | 0.5165 | 0.7677 | 0.5995 | |
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| 0.5367 | 20.2 | 4000 | 2.4935 | 0.5961 | 0.8182 | 0.6755 | |
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| 0.465 | 22.73 | 4500 | 2.2411 | 0.6412 | 0.8624 | 0.7272 | |
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| 0.4075 | 25.25 | 5000 | 2.1345 | 0.6783 | 0.8774 | 0.7615 | |
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| 0.3793 | 27.78 | 5500 | 2.2535 | 0.6681 | 0.8792 | 0.7543 | |
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| 0.3418 | 30.3 | 6000 | 2.3390 | 0.6662 | 0.8905 | 0.7576 | |
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### Framework versions |
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- Transformers 4.27.0.dev0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.9.1.dev0 |
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- Tokenizers 0.13.2 |
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