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Marcos12886
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distilhubert-finetuned-donateacry

Audio Classification
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Safetensors
hubert
Generated from Trainer
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Marcos12886 commited on
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End of training

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Files changed (5) hide show
  1. README.md +6 -5
  2. model.safetensors +1 -1
  3. runs/Jul03_19-35-23_DesMar/events.out.tfevents.1720028125.DesMar.18240.0 +3 -0
  4. runs/Jul03_19-35-23_DesMar/events.out.tfevents.1720028254.DesMar.18240.1 +3 -0
  5. 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: 0.6966292134831461
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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: 0.9121
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- - Accuracy: 0.6966
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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: linear
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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 | 0.9888 | 11 | 0.9121 | 0.6966 |
 
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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: 0.7808988764044944
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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: 0.6511
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+ - Accuracy: 0.7809
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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: linear
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+ - num_epochs: 2
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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 | 0.9888 | 11 | 0.8102 | 0.7753 |
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+ | No log | 1.9775 | 22 | 0.6511 | 0.7809 |
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  ### Framework versions
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