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distilhubert-finetuned-cry-detector

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  1. README.md +14 -15
  2. checkpoint-170/model.safetensors +1 -1
  3. checkpoint-170/optimizer.pt +1 -1
  4. checkpoint-170/scheduler.pt +1 -1
  5. checkpoint-170/trainer_state.json +19 -19
  6. checkpoint-170/training_args.bin +1 -1
  7. checkpoint-256/model.safetensors +1 -1
  8. checkpoint-256/optimizer.pt +1 -1
  9. checkpoint-256/scheduler.pt +1 -1
  10. checkpoint-256/trainer_state.json +28 -28
  11. checkpoint-256/training_args.bin +1 -1
  12. checkpoint-341/model.safetensors +1 -1
  13. checkpoint-341/optimizer.pt +1 -1
  14. checkpoint-341/scheduler.pt +1 -1
  15. checkpoint-341/trainer_state.json +35 -35
  16. checkpoint-341/training_args.bin +1 -1
  17. checkpoint-426/model.safetensors +1 -1
  18. checkpoint-426/optimizer.pt +1 -1
  19. checkpoint-426/scheduler.pt +1 -1
  20. checkpoint-426/trainer_state.json +43 -43
  21. checkpoint-426/training_args.bin +1 -1
  22. checkpoint-512/model.safetensors +1 -1
  23. checkpoint-512/optimizer.pt +1 -1
  24. checkpoint-512/scheduler.pt +1 -1
  25. checkpoint-512/trainer_state.json +55 -55
  26. checkpoint-512/training_args.bin +1 -1
  27. checkpoint-597/model.safetensors +1 -1
  28. checkpoint-597/optimizer.pt +1 -1
  29. checkpoint-597/scheduler.pt +1 -1
  30. checkpoint-597/trainer_state.json +64 -64
  31. checkpoint-597/training_args.bin +1 -1
  32. checkpoint-85/model.safetensors +1 -1
  33. checkpoint-85/optimizer.pt +1 -1
  34. checkpoint-85/scheduler.pt +1 -1
  35. checkpoint-85/trainer_state.json +11 -11
  36. checkpoint-85/training_args.bin +1 -1
  37. model.safetensors +1 -1
  38. runs/Sep08_20-15-47_ubumarcos/events.out.tfevents.1725819348.ubumarcos +3 -0
  39. training_args.bin +1 -1
README.md CHANGED
@@ -21,11 +21,11 @@ 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 an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0878
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- - Accuracy: 0.9861
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- - Precision: 0.9861
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- - Recall: 0.9861
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- - F1: 0.9861
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  ## Model description
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@@ -44,7 +44,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 0.0001
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 123
@@ -53,20 +53,19 @@ The following hyperparameters were used during training:
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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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  - lr_scheduler_warmup_ratio: 0.001
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- - num_epochs: 8
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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- | No log | 0.9956 | 85 | 0.1204 | 0.9641 | 0.9641 | 0.9641 | 0.9638 |
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- | No log | 1.9912 | 170 | 0.0847 | 0.9773 | 0.9772 | 0.9773 | 0.9773 |
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- | No log | 2.9985 | 256 | 0.1025 | 0.9766 | 0.9769 | 0.9766 | 0.9766 |
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- | No log | 3.9941 | 341 | 0.0869 | 0.9832 | 0.9835 | 0.9832 | 0.9832 |
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- | No log | 4.9898 | 426 | 0.0746 | 0.9832 | 0.9834 | 0.9832 | 0.9832 |
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- | 0.0538 | 5.9971 | 512 | 0.0870 | 0.9861 | 0.9861 | 0.9861 | 0.9861 |
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- | 0.0538 | 6.9927 | 597 | 0.0890 | 0.9861 | 0.9861 | 0.9861 | 0.9861 |
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- | 0.0538 | 7.9649 | 680 | 0.0878 | 0.9861 | 0.9861 | 0.9861 | 0.9861 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on an unknown dataset.
23
  It achieves the following results on the evaluation set:
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+ - Loss: 0.0359
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+ - Accuracy: 0.9883
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+ - Precision: 0.9883
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+ - Recall: 0.9883
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+ - F1: 0.9883
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 123
 
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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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  - lr_scheduler_warmup_ratio: 0.001
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+ - num_epochs: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | No log | 0.9956 | 85 | 0.1341 | 0.9538 | 0.9543 | 0.9538 | 0.9532 |
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+ | No log | 1.9912 | 170 | 0.0831 | 0.9700 | 0.9720 | 0.9700 | 0.9703 |
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+ | No log | 2.9985 | 256 | 0.0570 | 0.9846 | 0.9847 | 0.9846 | 0.9846 |
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+ | No log | 3.9941 | 341 | 0.0340 | 0.9897 | 0.9898 | 0.9897 | 0.9898 |
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+ | No log | 4.9898 | 426 | 0.0460 | 0.9875 | 0.9879 | 0.9875 | 0.9876 |
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+ | 0.0707 | 5.9971 | 512 | 0.0431 | 0.9897 | 0.9899 | 0.9897 | 0.9898 |
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+ | 0.0707 | 6.9927 | 597 | 0.0359 | 0.9883 | 0.9883 | 0.9883 | 0.9883 |
 
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  ### Framework versions
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