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bert-wellness-classifier

Text Classification
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bert
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End of training

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  1. README.md +27 -27
  2. model.safetensors +1 -1
  3. runs/Feb19_08-08-30_8e59040fe121/events.out.tfevents.1739952512.8e59040fe121.30.0 +3 -0
  4. runs/Feb19_08-08-30_8e59040fe121/events.out.tfevents.1739952605.8e59040fe121.30.1 +3 -0
  5. training_args.bin +1 -1
README.md CHANGED
@@ -18,20 +18,20 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.1107
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- - Accuracy: 0.624
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- - Auc: 0.861
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- - Precision Class 0: 0.455
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- - Precision Class 1: 0.75
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- - Precision Class 2: 0.44
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- - Precision Class 3: 0.725
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- - Precision Class 4: 0.675
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- - Precision Class 5: 0.5
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- - Recall Class 0: 0.526
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- - Recall Class 1: 0.522
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- - Recall Class 2: 0.407
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- - Recall Class 3: 0.787
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- - Recall Class 4: 0.812
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  - Recall Class 5: 0.333
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  ## Model description
@@ -51,9 +51,9 @@ 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: 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
@@ -63,16 +63,16 @@ 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 | Precision Class 4 | Precision Class 5 | Recall Class 0 | Recall Class 1 | Recall Class 2 | Recall Class 3 | Recall Class 4 | Recall Class 5 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:-----------------:|:-----------------:|:-----------------:|:-----------------:|:-----------------:|:-----------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|
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- | 1.6524 | 1.0 | 124 | 1.5268 | 0.434 | 0.755 | 0.406 | 0.0 | 0.0 | 0.697 | 0.384 | 0.0 | 0.52 | 0.0 | 0.0 | 0.548 | 0.836 | 0.0 |
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- | 1.4758 | 2.0 | 248 | 1.4056 | 0.472 | 0.79 | 0.382 | 1.0 | 0.0 | 0.646 | 0.442 | 0.222 | 0.52 | 0.05 | 0.0 | 0.738 | 0.791 | 0.056 |
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- | 1.3752 | 3.0 | 372 | 1.3204 | 0.533 | 0.818 | 0.448 | 1.0 | 0.556 | 0.744 | 0.466 | 0.333 | 0.52 | 0.25 | 0.455 | 0.69 | 0.821 | 0.028 |
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- | 1.2936 | 4.0 | 496 | 1.2519 | 0.552 | 0.837 | 0.444 | 1.0 | 0.467 | 0.597 | 0.556 | 0.25 | 0.48 | 0.25 | 0.318 | 0.881 | 0.821 | 0.028 |
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- | 1.2306 | 5.0 | 620 | 1.2009 | 0.547 | 0.848 | 0.464 | 0.75 | 0.429 | 0.773 | 0.505 | 0.286 | 0.52 | 0.3 | 0.136 | 0.81 | 0.836 | 0.111 |
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- | 1.1925 | 6.0 | 744 | 1.1624 | 0.59 | 0.858 | 0.444 | 0.833 | 0.462 | 0.81 | 0.593 | 0.424 | 0.48 | 0.25 | 0.273 | 0.81 | 0.806 | 0.389 |
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- | 1.1481 | 7.0 | 868 | 1.1378 | 0.58 | 0.862 | 0.462 | 0.857 | 0.438 | 0.791 | 0.579 | 0.36 | 0.48 | 0.3 | 0.318 | 0.81 | 0.821 | 0.25 |
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- | 1.1254 | 8.0 | 992 | 1.1256 | 0.585 | 0.865 | 0.48 | 0.875 | 0.381 | 0.795 | 0.582 | 0.391 | 0.48 | 0.35 | 0.364 | 0.833 | 0.791 | 0.25 |
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- | 1.102 | 9.0 | 1116 | 1.1147 | 0.585 | 0.867 | 0.48 | 0.875 | 0.381 | 0.795 | 0.582 | 0.391 | 0.48 | 0.35 | 0.364 | 0.833 | 0.791 | 0.25 |
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- | 1.1002 | 10.0 | 1240 | 1.1122 | 0.594 | 0.868 | 0.48 | 0.846 | 0.368 | 0.745 | 0.612 | 0.391 | 0.48 | 0.55 | 0.318 | 0.833 | 0.776 | 0.25 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.0333
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+ - Accuracy: 0.648
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+ - Auc: 0.878
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+ - Precision Class 0: 0.409
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+ - Precision Class 1: 0.769
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+ - Precision Class 2: 0.382
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+ - Precision Class 3: 0.729
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+ - Precision Class 4: 0.833
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+ - Precision Class 5: 0.478
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+ - Recall Class 0: 0.474
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+ - Recall Class 1: 0.87
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+ - Recall Class 2: 0.481
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+ - Recall Class 3: 0.745
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+ - Recall Class 4: 0.781
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  - Recall Class 5: 0.333
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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: 0.001
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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
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc | Precision Class 0 | Precision Class 1 | Precision Class 2 | Precision Class 3 | Precision Class 4 | Precision Class 5 | Recall Class 0 | Recall Class 1 | Recall Class 2 | Recall Class 3 | Recall Class 4 | Recall Class 5 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:-----------------:|:-----------------:|:-----------------:|:-----------------:|:-----------------:|:-----------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|
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+ | 1.5028 | 1.0 | 62 | 1.1703 | 0.528 | 0.852 | 0.5 | 0.889 | 0.0 | 0.769 | 0.595 | 0.282 | 0.28 | 0.4 | 0.0 | 0.714 | 0.701 | 0.556 |
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+ | 1.1661 | 2.0 | 124 | 1.0814 | 0.575 | 0.868 | 0.6 | 0.515 | 0.375 | 0.935 | 0.712 | 0.333 | 0.36 | 0.85 | 0.136 | 0.69 | 0.627 | 0.611 |
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+ | 1.0576 | 3.0 | 186 | 1.0438 | 0.585 | 0.876 | 0.394 | 0.737 | 0.308 | 0.755 | 0.719 | 0.467 | 0.52 | 0.7 | 0.545 | 0.881 | 0.612 | 0.194 |
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+ | 0.9603 | 4.0 | 248 | 1.0368 | 0.637 | 0.877 | 0.688 | 0.846 | 0.44 | 0.868 | 0.6 | 0.4 | 0.44 | 0.55 | 0.5 | 0.786 | 0.94 | 0.167 |
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+ | 0.8873 | 5.0 | 310 | 1.0208 | 0.571 | 0.877 | 0.667 | 0.75 | 0.333 | 0.886 | 0.651 | 0.311 | 0.48 | 0.6 | 0.091 | 0.738 | 0.612 | 0.639 |
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+ | 0.866 | 6.0 | 372 | 0.9809 | 0.604 | 0.877 | 0.484 | 0.684 | 0.312 | 0.892 | 0.671 | 0.259 | 0.6 | 0.65 | 0.227 | 0.786 | 0.821 | 0.194 |
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+ | 0.8203 | 7.0 | 434 | 0.9894 | 0.637 | 0.882 | 0.519 | 0.75 | 0.4 | 0.8 | 0.696 | 0.4 | 0.56 | 0.6 | 0.455 | 0.857 | 0.821 | 0.222 |
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+ | 0.8024 | 8.0 | 496 | 0.9797 | 0.632 | 0.882 | 0.484 | 0.682 | 0.45 | 0.889 | 0.693 | 0.393 | 0.6 | 0.75 | 0.409 | 0.762 | 0.776 | 0.306 |
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+ | 0.7558 | 9.0 | 558 | 0.9738 | 0.594 | 0.883 | 0.6 | 0.765 | 0.375 | 0.766 | 0.694 | 0.32 | 0.48 | 0.65 | 0.273 | 0.857 | 0.642 | 0.444 |
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+ | 0.7319 | 10.0 | 620 | 0.9632 | 0.632 | 0.884 | 0.519 | 0.722 | 0.36 | 0.8 | 0.708 | 0.44 | 0.56 | 0.65 | 0.409 | 0.857 | 0.761 | 0.306 |
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  ### Framework versions
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