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Marcos12886
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distilhubert-finetuned-cry-detector

Audio Classification
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hubert
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End of training

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Files changed (4) hide show
  1. README.md +14 -9
  2. model.safetensors +1 -1
  3. runs/Aug08_10-56-53_DesMar/events.out.tfevents.1723107413.DesMar.18008.0 +3 -0
  4. training_args.bin +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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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
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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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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@@ -60,17 +60,22 @@ 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: 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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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9830508474576272
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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 [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: 0.0703
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+ - Accuracy: 0.9831
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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: 10
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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 | 0.1909 | 0.9435 |
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+ | No log | 2.0 | 22 | 0.1998 | 0.9322 |
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+ | No log | 3.0 | 33 | 0.0904 | 0.9718 |
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+ | No log | 4.0 | 44 | 0.1063 | 0.9661 |
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+ | No log | 5.0 | 55 | 0.0770 | 0.9774 |
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+ | No log | 6.0 | 66 | 0.0832 | 0.9774 |
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+ | No log | 7.0 | 77 | 0.0658 | 0.9774 |
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+ | No log | 8.0 | 88 | 0.0681 | 0.9831 |
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+ | No log | 9.0 | 99 | 0.0701 | 0.9831 |
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+ | No log | 10.0 | 110 | 0.0703 | 0.9831 |
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  ### Framework versions
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