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hubert-base-ls960-finetuned-common_voice

Audio Classification
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hubert
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  1. README.md +17 -17
  2. model.safetensors +1 -1
README.md CHANGED
@@ -20,13 +20,13 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4616
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- - Accuracy: 0.9375
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- - F1: 0.9377
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- - Recall: 0.9375
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- - Precision: 0.9403
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- - Mcc: 0.9225
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- - Auc: 0.9925
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  ## Model description
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@@ -61,16 +61,16 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | Mcc | Auc |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:------:|
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- | 1.601 | 0.96 | 12 | 1.5594 | 0.385 | 0.3217 | 0.3850 | 0.6064 | 0.2666 | 0.7895 |
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- | 1.5467 | 2.0 | 25 | 1.3344 | 0.67 | 0.6516 | 0.6700 | 0.7185 | 0.6030 | 0.9009 |
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- | 1.4062 | 2.96 | 37 | 1.0521 | 0.8 | 0.7964 | 0.8 | 0.8014 | 0.7521 | 0.9436 |
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- | 1.0881 | 4.0 | 50 | 0.8340 | 0.8525 | 0.8502 | 0.8525 | 0.8677 | 0.8201 | 0.9759 |
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- | 0.9348 | 4.96 | 62 | 0.7227 | 0.89 | 0.8894 | 0.89 | 0.8939 | 0.8639 | 0.9801 |
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- | 0.8596 | 6.0 | 75 | 0.5873 | 0.9275 | 0.9276 | 0.9275 | 0.9300 | 0.9100 | 0.9908 |
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- | 0.7917 | 6.96 | 87 | 0.5208 | 0.93 | 0.9298 | 0.93 | 0.9310 | 0.9128 | 0.9940 |
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- | 0.6721 | 8.0 | 100 | 0.4784 | 0.9475 | 0.9476 | 0.9475 | 0.9491 | 0.9348 | 0.9935 |
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- | 0.6297 | 8.96 | 112 | 0.4734 | 0.9325 | 0.9326 | 0.9325 | 0.9363 | 0.9166 | 0.9916 |
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- | 0.6127 | 9.6 | 120 | 0.4616 | 0.9375 | 0.9377 | 0.9375 | 0.9403 | 0.9225 | 0.9925 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1194
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+ - Accuracy: 0.9925
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+ - F1: 0.9925
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+ - Recall: 0.9925
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+ - Precision: 0.9926
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+ - Mcc: 0.9906
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+ - Auc: 0.9994
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | Mcc | Auc |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:------:|
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+ | 0.6461 | 0.96 | 12 | 0.4123 | 0.9725 | 0.9725 | 0.9725 | 0.9736 | 0.9659 | 0.9992 |
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+ | 0.622 | 2.0 | 25 | 0.3858 | 0.9225 | 0.9214 | 0.9225 | 0.9354 | 0.9067 | 0.9943 |
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+ | 0.4827 | 2.96 | 37 | 0.2750 | 0.97 | 0.9699 | 0.97 | 0.9731 | 0.9633 | 0.9988 |
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+ | 0.3907 | 4.0 | 50 | 0.2061 | 0.98 | 0.9800 | 0.9800 | 0.9809 | 0.9752 | 0.9998 |
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+ | 0.3212 | 4.96 | 62 | 0.1654 | 0.99 | 0.9900 | 0.9900 | 0.9902 | 0.9875 | 0.9999 |
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+ | 0.2865 | 6.0 | 75 | 0.1355 | 0.9925 | 0.9925 | 0.9925 | 0.9926 | 0.9906 | 1.0000 |
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+ | 0.278 | 6.96 | 87 | 0.1379 | 0.9925 | 0.9925 | 0.9925 | 0.9926 | 0.9906 | 0.9989 |
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+ | 0.2285 | 8.0 | 100 | 0.1199 | 0.995 | 0.9950 | 0.9950 | 0.9950 | 0.9938 | 0.9993 |
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+ | 0.1975 | 8.96 | 112 | 0.1239 | 0.99 | 0.9900 | 0.9900 | 0.9902 | 0.9875 | 0.9994 |
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+ | 0.1949 | 9.6 | 120 | 0.1194 | 0.9925 | 0.9925 | 0.9925 | 0.9926 | 0.9906 | 0.9994 |
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  ### Framework versions
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