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wav2vec2-base-finetuned-ks

Audio Classification
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PyTorch
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wav2vec2
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update model card README.md

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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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  datasets:
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  name: audiofolder
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  type: audiofolder
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  config: Data_Train
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- split: train[:90%]
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  args: Data_Train
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.838957055214724
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  - name: F1
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  type: f1
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- value: 0.8286657330565742
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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
@@ -35,9 +36,9 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.8341
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- - Accuracy: 0.8390
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- - F1: 0.8287
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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- | 1.6193 | 1.0 | 1304 | 1.6888 | 0.5061 | 0.3780 |
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- | 1.0537 | 2.0 | 2608 | 1.2344 | 0.6794 | 0.6414 |
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- | 0.8454 | 3.0 | 3912 | 0.9588 | 0.7653 | 0.7465 |
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- | 0.7623 | 4.0 | 5216 | 0.8678 | 0.8198 | 0.8107 |
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- | 0.4638 | 5.0 | 6520 | 0.8341 | 0.8390 | 0.8287 |
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  ### Framework versions
 
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  ---
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  license: apache-2.0
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+ base_model: facebook/wav2vec2-base
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  tags:
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  - generated_from_trainer
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  datasets:
 
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  name: audiofolder
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  type: audiofolder
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  config: Data_Train
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+ split: train
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  args: Data_Train
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8127696289905091
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  - name: F1
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  type: f1
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+ value: 0.7948883642136002
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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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  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.9323
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+ - Accuracy: 0.8128
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+ - F1: 0.7949
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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+ | 1.844 | 1.0 | 1449 | 1.7968 | 0.5065 | 0.3818 |
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+ | 0.8796 | 2.0 | 2898 | 1.1875 | 0.6799 | 0.6273 |
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+ | 0.7076 | 3.0 | 4347 | 1.0995 | 0.7584 | 0.7287 |
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+ | 0.4669 | 4.0 | 5796 | 0.9960 | 0.7886 | 0.7675 |
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+ | 0.2156 | 5.0 | 7245 | 0.9323 | 0.8128 | 0.7949 |
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  ### Framework versions