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End of training

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README.md CHANGED
@@ -5,9 +5,24 @@ tags:
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  - generated_from_trainer
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  datasets:
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  - audiofolder
 
 
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  model-index:
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  - name: distilhubert-finetuned-cry-detector
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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
@@ -16,6 +31,9 @@ should probably proofread and complete it, then remove this comment. -->
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  # distilhubert-finetuned-cry-detector
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  This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the audiofolder dataset.
 
 
 
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  ## Model description
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@@ -42,13 +60,17 @@ The following hyperparameters were used during training:
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  - total_train_batch_size: 64
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: cosine
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- - num_epochs: 1
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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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- | No log | 1.0 | 11 | 0.3106 | 0.9379 |
 
 
 
 
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  ### Framework versions
 
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  - generated_from_trainer
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  datasets:
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  - audiofolder
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+ metrics:
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+ - accuracy
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  model-index:
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  - name: distilhubert-finetuned-cry-detector
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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: audiofolder
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+ type: audiofolder
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+ config: default
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+ split: train
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+ args: default
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9717514124293786
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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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  # distilhubert-finetuned-cry-detector
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  This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the audiofolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0882
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+ - Accuracy: 0.9718
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  ## Model description
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  - total_train_batch_size: 64
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: cosine
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+ - num_epochs: 5
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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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+ | No log | 1.0 | 11 | 0.1932 | 0.9435 |
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+ | No log | 2.0 | 22 | 0.1986 | 0.9322 |
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+ | No log | 3.0 | 33 | 0.1046 | 0.9661 |
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+ | No log | 4.0 | 44 | 0.0861 | 0.9718 |
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+ | No log | 5.0 | 55 | 0.0882 | 0.9718 |
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  ### Framework versions
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