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distilhubert-finetuned-cry-detector

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  1. README.md +16 -10
  2. checkpoint-170/config.json +0 -4
  3. checkpoint-170/model.safetensors +1 -1
  4. checkpoint-170/optimizer.pt +1 -1
  5. checkpoint-170/scheduler.pt +1 -1
  6. checkpoint-170/trainer_state.json +20 -20
  7. checkpoint-170/training_args.bin +1 -1
  8. checkpoint-256/config.json +0 -4
  9. checkpoint-256/model.safetensors +1 -1
  10. checkpoint-256/optimizer.pt +1 -1
  11. checkpoint-256/scheduler.pt +1 -1
  12. checkpoint-256/trainer_state.json +28 -28
  13. checkpoint-256/training_args.bin +1 -1
  14. checkpoint-341/config.json +0 -4
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  16. checkpoint-341/optimizer.pt +1 -1
  17. checkpoint-341/trainer_state.json +32 -32
  18. checkpoint-341/training_args.bin +1 -1
  19. checkpoint-426/config.json +0 -4
  20. checkpoint-426/model.safetensors +1 -1
  21. checkpoint-426/optimizer.pt +1 -1
  22. checkpoint-426/scheduler.pt +1 -1
  23. checkpoint-426/trainer_state.json +44 -44
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  25. checkpoint-512/config.json +0 -4
  26. checkpoint-512/model.safetensors +1 -1
  27. checkpoint-512/optimizer.pt +1 -1
  28. checkpoint-512/scheduler.pt +1 -1
  29. checkpoint-512/trainer_state.json +51 -51
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  31. checkpoint-597/config.json +0 -4
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  33. checkpoint-597/optimizer.pt +1 -1
  34. checkpoint-597/scheduler.pt +1 -1
  35. checkpoint-597/trainer_state.json +60 -60
  36. checkpoint-597/training_args.bin +1 -1
  37. checkpoint-683/config.json +1 -4
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  39. checkpoint-683/optimizer.pt +1 -1
  40. checkpoint-683/scheduler.pt +1 -1
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  49. checkpoint-85/config.json +0 -4
  50. checkpoint-85/model.safetensors +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: 0.0625
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- - Accuracy: 0.9824
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- - Precision: 0.9825
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- - Recall: 0.9824
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- - F1: 0.9824
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  ## Model description
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@@ -53,16 +53,22 @@ 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: 4
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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 | 0.1378 | 0.9546 | 0.9543 | 0.9546 | 0.9544 |
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- | No log | 1.9912 | 170 | 0.0802 | 0.9714 | 0.9713 | 0.9714 | 0.9714 |
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- | No log | 2.9985 | 256 | 0.0682 | 0.9780 | 0.9783 | 0.9780 | 0.9781 |
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- | No log | 3.9824 | 340 | 0.0625 | 0.9824 | 0.9825 | 0.9824 | 0.9824 |
 
 
 
 
 
 
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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.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0338
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+ - Accuracy: 0.9912
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+ - Precision: 0.9912
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+ - Recall: 0.9912
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+ - F1: 0.9912
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  ## Model description
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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 | 0.1197 | 0.9538 | 0.9535 | 0.9538 | 0.9536 |
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+ | No log | 1.9912 | 170 | 0.0601 | 0.9832 | 0.9831 | 0.9832 | 0.9831 |
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+ | No log | 2.9985 | 256 | 0.0506 | 0.9868 | 0.9868 | 0.9868 | 0.9868 |
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+ | No log | 3.9941 | 341 | 0.0461 | 0.9861 | 0.9864 | 0.9861 | 0.9862 |
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+ | No log | 4.9898 | 426 | 0.0439 | 0.9890 | 0.9891 | 0.9890 | 0.9890 |
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+ | 0.0779 | 5.9971 | 512 | 0.0396 | 0.9905 | 0.9905 | 0.9905 | 0.9905 |
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+ | 0.0779 | 6.9927 | 597 | 0.0350 | 0.9919 | 0.9919 | 0.9919 | 0.9919 |
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+ | 0.0779 | 8.0 | 683 | 0.0335 | 0.9912 | 0.9912 | 0.9912 | 0.9912 |
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+ | 0.0779 | 8.9956 | 768 | 0.0337 | 0.9912 | 0.9912 | 0.9912 | 0.9912 |
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+ | 0.0779 | 9.9561 | 850 | 0.0338 | 0.9912 | 0.9912 | 0.9912 | 0.9912 |
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  ### Framework versions
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