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Marcos12886
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distilhubert-finetuned-cry-detector

Audio Classification
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hubert
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Marcos12886 commited on
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End of training

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Files changed (3) hide show
  1. README.md +9 -8
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -23,7 +23,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9786096256684492
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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
@@ -33,8 +33,8 @@ 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: 0.0748
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- - Accuracy: 0.9786
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  ## Model description
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@@ -61,16 +61,17 @@ 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: 4
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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 | 0.9362 | 11 | 0.1841 | 0.9358 |
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- | No log | 1.9574 | 23 | 0.1308 | 0.9519 |
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- | No log | 2.9787 | 35 | 0.0763 | 0.9733 |
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- | No log | 3.7447 | 44 | 0.0748 | 0.9786 |
 
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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: 0.9754901960784313
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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: 0.0744
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+ - Accuracy: 0.9755
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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 | 0.9412 | 12 | 0.1712 | 0.9461 |
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+ | No log | 1.9608 | 25 | 0.1264 | 0.9608 |
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+ | No log | 2.9804 | 38 | 0.0836 | 0.9706 |
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+ | No log | 4.0 | 51 | 0.0777 | 0.9706 |
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+ | No log | 4.7059 | 60 | 0.0744 | 0.9755 |
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  ### Framework versions
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