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hebashakeel
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bert-wellness-classifier

Text Classification
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bert
Generated from Trainer
text-embeddings-inference
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End of training

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  1. README.md +25 -25
  2. model.safetensors +1 -1
  3. runs/Feb17_08-13-57_d68c514ae1af/events.out.tfevents.1739780037.d68c514ae1af.30.8 +3 -0
  4. runs/Feb17_08-13-57_d68c514ae1af/events.out.tfevents.1739780091.d68c514ae1af.30.9 +3 -0
  5. training_args.bin +1 -1
README.md CHANGED
@@ -18,21 +18,21 @@ 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: 1.0295
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  - Accuracy: 0.62
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- - Auc: 0.882
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- - Precision Class 0: 0.4
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- - Precision Class 1: 0.792
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- - Precision Class 2: 0.37
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- - Precision Class 3: 0.729
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- - Precision Class 4: 0.817
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- - Precision Class 5: 0.324
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- - Recall Class 0: 0.421
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- - Recall Class 1: 0.826
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- - Recall Class 2: 0.37
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  - Recall Class 3: 0.745
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- - Recall Class 4: 0.766
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- - Recall Class 5: 0.333
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  ## Model description
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@@ -52,8 +52,8 @@ More information needed
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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: 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
@@ -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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- | 0.6147 | 1.0 | 62 | 0.9902 | 0.59 | 0.887 | 0.481 | 0.722 | 0.375 | 0.773 | 0.687 | 0.312 | 0.52 | 0.65 | 0.409 | 0.81 | 0.687 | 0.278 |
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- | 0.7131 | 2.0 | 124 | 0.9972 | 0.594 | 0.887 | 0.448 | 0.722 | 0.429 | 0.773 | 0.705 | 0.359 | 0.52 | 0.65 | 0.409 | 0.81 | 0.642 | 0.389 |
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- | 0.7021 | 3.0 | 186 | 0.9851 | 0.613 | 0.887 | 0.433 | 0.7 | 0.421 | 0.805 | 0.708 | 0.367 | 0.52 | 0.7 | 0.364 | 0.786 | 0.761 | 0.306 |
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- | 0.7151 | 4.0 | 248 | 0.9858 | 0.594 | 0.887 | 0.448 | 0.722 | 0.474 | 0.75 | 0.687 | 0.343 | 0.52 | 0.65 | 0.409 | 0.786 | 0.687 | 0.333 |
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- | 0.6943 | 5.0 | 310 | 0.9856 | 0.604 | 0.887 | 0.448 | 0.765 | 0.421 | 0.773 | 0.681 | 0.355 | 0.52 | 0.65 | 0.364 | 0.81 | 0.731 | 0.306 |
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- | 0.6909 | 6.0 | 372 | 0.9905 | 0.599 | 0.886 | 0.448 | 0.722 | 0.444 | 0.756 | 0.692 | 0.378 | 0.52 | 0.65 | 0.364 | 0.81 | 0.672 | 0.389 |
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- | 0.6618 | 7.0 | 434 | 0.9810 | 0.618 | 0.887 | 0.481 | 0.812 | 0.421 | 0.791 | 0.68 | 0.375 | 0.52 | 0.65 | 0.364 | 0.81 | 0.761 | 0.333 |
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- | 0.6869 | 8.0 | 496 | 0.9845 | 0.608 | 0.887 | 0.448 | 0.812 | 0.421 | 0.791 | 0.676 | 0.355 | 0.52 | 0.65 | 0.364 | 0.81 | 0.746 | 0.306 |
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- | 0.6726 | 9.0 | 558 | 0.9839 | 0.623 | 0.887 | 0.481 | 0.867 | 0.421 | 0.795 | 0.68 | 0.375 | 0.52 | 0.65 | 0.364 | 0.833 | 0.761 | 0.333 |
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- | 0.6683 | 10.0 | 620 | 0.9848 | 0.618 | 0.887 | 0.464 | 0.867 | 0.421 | 0.795 | 0.68 | 0.355 | 0.52 | 0.65 | 0.364 | 0.833 | 0.761 | 0.306 |
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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: 1.0580
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  - Accuracy: 0.62
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+ - Auc: 0.88
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+ - Precision Class 0: 0.417
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+ - Precision Class 1: 0.8
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+ - Precision Class 2: 0.379
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+ - Precision Class 3: 0.673
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+ - Precision Class 4: 0.836
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+ - Precision Class 5: 0.357
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+ - Recall Class 0: 0.526
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+ - Recall Class 1: 0.87
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+ - Recall Class 2: 0.407
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  - Recall Class 3: 0.745
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+ - Recall Class 4: 0.719
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+ - Recall Class 5: 0.303
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  ## Model description
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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
 
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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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+ | 0.6161 | 1.0 | 124 | 1.0082 | 0.608 | 0.886 | 0.452 | 0.684 | 0.45 | 0.778 | 0.706 | 0.345 | 0.56 | 0.65 | 0.409 | 0.833 | 0.716 | 0.278 |
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+ | 0.5787 | 2.0 | 248 | 1.0084 | 0.599 | 0.885 | 0.481 | 0.7 | 0.5 | 0.825 | 0.692 | 0.326 | 0.52 | 0.7 | 0.318 | 0.786 | 0.672 | 0.417 |
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+ | 0.5905 | 3.0 | 372 | 1.0121 | 0.613 | 0.886 | 0.464 | 0.7 | 0.4 | 0.829 | 0.696 | 0.379 | 0.52 | 0.7 | 0.455 | 0.81 | 0.716 | 0.306 |
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+ | 0.5879 | 4.0 | 496 | 1.0145 | 0.637 | 0.888 | 0.619 | 0.824 | 0.375 | 0.766 | 0.707 | 0.357 | 0.52 | 0.7 | 0.409 | 0.857 | 0.791 | 0.278 |
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+ | 0.5653 | 5.0 | 620 | 1.0153 | 0.637 | 0.886 | 0.467 | 0.778 | 0.5 | 0.81 | 0.692 | 0.393 | 0.56 | 0.7 | 0.364 | 0.81 | 0.806 | 0.306 |
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+ | 0.5878 | 6.0 | 744 | 1.0216 | 0.627 | 0.885 | 0.464 | 0.778 | 0.471 | 0.791 | 0.704 | 0.4 | 0.52 | 0.7 | 0.364 | 0.81 | 0.746 | 0.389 |
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+ | 0.557 | 7.0 | 868 | 1.0174 | 0.627 | 0.885 | 0.448 | 0.778 | 0.444 | 0.829 | 0.693 | 0.387 | 0.52 | 0.7 | 0.364 | 0.81 | 0.776 | 0.333 |
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+ | 0.6129 | 8.0 | 992 | 1.0192 | 0.623 | 0.886 | 0.464 | 0.778 | 0.421 | 0.829 | 0.689 | 0.375 | 0.52 | 0.7 | 0.364 | 0.81 | 0.761 | 0.333 |
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+ | 0.6103 | 9.0 | 1116 | 1.0214 | 0.623 | 0.886 | 0.481 | 0.778 | 0.474 | 0.81 | 0.694 | 0.353 | 0.52 | 0.7 | 0.409 | 0.81 | 0.746 | 0.333 |
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+ | 0.6445 | 10.0 | 1240 | 1.0223 | 0.623 | 0.885 | 0.481 | 0.778 | 0.474 | 0.81 | 0.694 | 0.353 | 0.52 | 0.7 | 0.409 | 0.81 | 0.746 | 0.333 |
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  ### Framework versions
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