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roberta-wellness-classifier

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End of training

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  1. README.md +22 -22
  2. model.safetensors +1 -1
  3. runs/Nov04_05-06-26_0ffd57b38425/events.out.tfevents.1730696791.0ffd57b38425.30.0 +3 -0
  4. runs/Nov04_05-07-52_0ffd57b38425/events.out.tfevents.1730696878.0ffd57b38425.30.1 +3 -0
  5. runs/Nov04_05-15-46_0ffd57b38425/events.out.tfevents.1730697350.0ffd57b38425.30.2 +3 -0
  6. runs/Nov04_05-31-50_0ffd57b38425/events.out.tfevents.1730698314.0ffd57b38425.30.3 +3 -0
  7. runs/Nov04_05-33-49_0ffd57b38425/events.out.tfevents.1730698434.0ffd57b38425.30.4 +3 -0
  8. runs/Nov04_05-33-49_0ffd57b38425/events.out.tfevents.1730698690.0ffd57b38425.30.5 +3 -0
  9. runs/Nov04_05-40-45_0ffd57b38425/events.out.tfevents.1730698850.0ffd57b38425.30.6 +3 -0
  10. runs/Nov04_05-44-30_0ffd57b38425/events.out.tfevents.1730699077.0ffd57b38425.30.7 +3 -0
  11. runs/Nov04_05-44-30_0ffd57b38425/events.out.tfevents.1730699680.0ffd57b38425.30.8 +3 -0
  12. runs/Nov04_05-55-32_0ffd57b38425/events.out.tfevents.1730699735.0ffd57b38425.30.9 +3 -0
  13. runs/Nov04_05-57-24_0ffd57b38425/events.out.tfevents.1730699847.0ffd57b38425.30.10 +3 -0
  14. training_args.bin +1 -1
README.md CHANGED
@@ -18,17 +18,17 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.5355
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- - Accuracy: 0.705
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- - Auc: 0.898
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- - Precision Class 0: 0.822
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- - Precision Class 1: 0.81
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- - Precision Class 2: 0.702
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- - Precision Class 3: 0.644
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  - Recall Class 0: 0.698
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- - Recall Class 1: 0.63
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- - Recall Class 2: 0.635
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- - Recall Class 3: 0.776
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  ## Model description
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@@ -48,8 +48,8 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 3e-05
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- - train_batch_size: 16
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- - eval_batch_size: 16
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
@@ -59,16 +59,16 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc | Precision Class 0 | Precision Class 1 | Precision Class 2 | Precision Class 3 | Recall Class 0 | Recall Class 1 | Recall Class 2 | Recall Class 3 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:-----------------:|:-----------------:|:-----------------:|:-----------------:|:--------------:|:--------------:|:--------------:|:--------------:|
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- | 1.0739 | 1.0 | 70 | 0.7343 | 0.743 | 0.904 | 0.703 | 0.667 | 0.688 | 0.871 | 0.849 | 0.741 | 0.841 | 0.622 |
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- | 0.7019 | 2.0 | 140 | 0.6927 | 0.722 | 0.917 | 0.764 | 0.867 | 0.736 | 0.678 | 0.792 | 0.481 | 0.619 | 0.816 |
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- | 0.4696 | 3.0 | 210 | 0.7057 | 0.747 | 0.913 | 0.786 | 0.75 | 0.686 | 0.77 | 0.83 | 0.778 | 0.762 | 0.684 |
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- | 0.3057 | 4.0 | 280 | 0.8772 | 0.78 | 0.906 | 0.843 | 0.933 | 0.674 | 0.826 | 0.811 | 0.519 | 0.952 | 0.724 |
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- | 0.2182 | 5.0 | 350 | 0.8735 | 0.763 | 0.915 | 0.8 | 0.759 | 0.722 | 0.778 | 0.755 | 0.815 | 0.825 | 0.714 |
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- | 0.1634 | 6.0 | 420 | 1.1820 | 0.714 | 0.901 | 0.783 | 0.762 | 0.714 | 0.676 | 0.679 | 0.593 | 0.714 | 0.765 |
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- | 0.116 | 7.0 | 490 | 1.2979 | 0.73 | 0.897 | 0.788 | 0.731 | 0.737 | 0.698 | 0.774 | 0.704 | 0.667 | 0.755 |
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- | 0.0844 | 8.0 | 560 | 1.4513 | 0.705 | 0.9 | 0.833 | 0.773 | 0.705 | 0.647 | 0.66 | 0.63 | 0.683 | 0.765 |
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- | 0.0624 | 9.0 | 630 | 1.5216 | 0.71 | 0.901 | 0.826 | 0.783 | 0.702 | 0.652 | 0.717 | 0.667 | 0.635 | 0.765 |
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- | 0.0446 | 10.0 | 700 | 1.5355 | 0.705 | 0.898 | 0.822 | 0.81 | 0.702 | 0.644 | 0.698 | 0.63 | 0.635 | 0.776 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
20
  It achieves the following results on the evaluation set:
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+ - Loss: 1.8807
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+ - Accuracy: 0.71
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+ - Auc: 0.871
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+ - Precision Class 0: 0.787
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+ - Precision Class 1: 0.857
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+ - Precision Class 2: 0.731
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+ - Precision Class 3: 0.645
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  - Recall Class 0: 0.698
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+ - Recall Class 1: 0.667
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+ - Recall Class 2: 0.603
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+ - Recall Class 3: 0.796
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 3e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
53
  - seed: 42
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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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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc | Precision Class 0 | Precision Class 1 | Precision Class 2 | Precision Class 3 | Recall Class 0 | Recall Class 1 | Recall Class 2 | Recall Class 3 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:-----------------:|:-----------------:|:-----------------:|:-----------------:|:--------------:|:--------------:|:--------------:|:--------------:|
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+ | 1.0423 | 1.0 | 140 | 0.7782 | 0.685 | 0.888 | 0.732 | 0.727 | 0.595 | 0.734 | 0.774 | 0.593 | 0.794 | 0.592 |
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+ | 0.6558 | 2.0 | 280 | 0.7589 | 0.714 | 0.902 | 0.733 | 0.85 | 0.78 | 0.658 | 0.83 | 0.63 | 0.508 | 0.806 |
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+ | 0.4306 | 3.0 | 420 | 1.0251 | 0.73 | 0.89 | 0.738 | 0.857 | 0.64 | 0.795 | 0.849 | 0.667 | 0.873 | 0.592 |
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+ | 0.3002 | 4.0 | 560 | 1.2314 | 0.726 | 0.908 | 0.816 | 0.938 | 0.707 | 0.669 | 0.755 | 0.556 | 0.651 | 0.806 |
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+ | 0.2117 | 5.0 | 700 | 1.3601 | 0.714 | 0.888 | 0.857 | 0.941 | 0.645 | 0.67 | 0.679 | 0.593 | 0.778 | 0.724 |
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+ | 0.1606 | 6.0 | 840 | 1.4648 | 0.718 | 0.887 | 0.784 | 0.933 | 0.682 | 0.679 | 0.755 | 0.519 | 0.714 | 0.755 |
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+ | 0.1135 | 7.0 | 980 | 1.6228 | 0.714 | 0.883 | 0.78 | 0.826 | 0.698 | 0.667 | 0.736 | 0.704 | 0.698 | 0.714 |
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+ | 0.0686 | 8.0 | 1120 | 1.8947 | 0.71 | 0.866 | 0.809 | 0.857 | 0.745 | 0.635 | 0.717 | 0.667 | 0.556 | 0.816 |
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+ | 0.0525 | 9.0 | 1260 | 1.8817 | 0.718 | 0.875 | 0.796 | 0.864 | 0.74 | 0.65 | 0.736 | 0.704 | 0.587 | 0.796 |
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+ | 0.0526 | 10.0 | 1400 | 1.8807 | 0.71 | 0.871 | 0.787 | 0.857 | 0.731 | 0.645 | 0.698 | 0.667 | 0.603 | 0.796 |
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  ### Framework versions
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