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README.md
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---
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base_model: microsoft/wavlm-base
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tags:
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- generated_from_trainer
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datasets:
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- superb
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metrics:
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- accuracy
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model-index:
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- name: wav2vec2-base-ft-keyword-spotting
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results:
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- task:
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name: Audio Classification
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type: audio-classification
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dataset:
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name: superb
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type: superb
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config: ks
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split: validation
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args: ks
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9694027655192704
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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-ft-keyword-spotting
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This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on the superb dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2270
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- Accuracy: 0.9694
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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: 64
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- eval_batch_size: 64
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- seed: 0
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 256
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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: 5.0
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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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| 1.3203 | 1.0 | 199 | 1.2906 | 0.6328 |
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| 0.9587 | 2.0 | 399 | 0.7793 | 0.7355 |
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| 0.6218 | 3.0 | 599 | 0.3858 | 0.9289 |
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| 0.4379 | 4.0 | 799 | 0.2581 | 0.9688 |
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| 0.3779 | 4.98 | 995 | 0.2270 | 0.9694 |
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### Framework versions
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- Transformers 4.34.0.dev0
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- Pytorch 2.0.0.post302
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- Datasets 2.14.5
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- Tokenizers 0.13.3
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