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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 +24 -24
  2. model.safetensors +1 -1
  3. runs/Feb17_08-11-02_d68c514ae1af/events.out.tfevents.1739779863.d68c514ae1af.30.4 +3 -0
  4. runs/Feb17_08-11-02_d68c514ae1af/events.out.tfevents.1739779912.d68c514ae1af.30.5 +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: 1.0524
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  - Accuracy: 0.624
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  - Auc: 0.879
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- - Precision Class 0: 0.389
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- - Precision Class 1: 0.778
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- - Precision Class 2: 0.467
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- - Precision Class 3: 0.767
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- - Precision Class 4: 0.722
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- - Precision Class 5: 0.342
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- - Recall Class 0: 0.368
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- - Recall Class 1: 0.913
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- - Recall Class 2: 0.259
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- - Recall Class 3: 0.702
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- - Recall Class 4: 0.812
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- - Recall Class 5: 0.394
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  ## Model description
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@@ -51,7 +51,7 @@ 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: 0.001
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  - train_batch_size: 16
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  - eval_batch_size: 16
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  - seed: 42
@@ -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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- | 0.9373 | 1.0 | 62 | 0.9872 | 0.656 | 0.883 | 0.56 | 0.789 | 0.625 | 0.846 | 0.671 | 0.417 | 0.56 | 0.75 | 0.227 | 0.786 | 0.851 | 0.417 |
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- | 0.8006 | 2.0 | 124 | 1.0506 | 0.604 | 0.88 | 0.5 | 0.615 | 0.429 | 0.909 | 0.737 | 0.383 | 0.44 | 0.8 | 0.273 | 0.714 | 0.627 | 0.639 |
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- | 0.7957 | 3.0 | 186 | 1.0198 | 0.613 | 0.885 | 0.542 | 0.737 | 0.306 | 0.795 | 0.692 | 0.5 | 0.52 | 0.7 | 0.5 | 0.833 | 0.672 | 0.333 |
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- | 0.7744 | 4.0 | 248 | 1.0501 | 0.637 | 0.881 | 0.579 | 0.778 | 0.458 | 0.872 | 0.619 | 0.333 | 0.44 | 0.7 | 0.5 | 0.81 | 0.896 | 0.139 |
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- | 0.7193 | 5.0 | 310 | 1.0314 | 0.613 | 0.881 | 0.591 | 0.652 | 0.545 | 0.886 | 0.676 | 0.34 | 0.52 | 0.75 | 0.273 | 0.738 | 0.716 | 0.472 |
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- | 0.7214 | 6.0 | 372 | 1.0134 | 0.632 | 0.881 | 0.481 | 0.667 | 0.5 | 0.872 | 0.703 | 0.371 | 0.52 | 0.7 | 0.364 | 0.81 | 0.776 | 0.361 |
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- | 0.6628 | 7.0 | 434 | 1.0454 | 0.651 | 0.882 | 0.536 | 0.867 | 0.4 | 0.818 | 0.701 | 0.435 | 0.6 | 0.65 | 0.455 | 0.857 | 0.806 | 0.278 |
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- | 0.6689 | 8.0 | 496 | 1.0253 | 0.627 | 0.883 | 0.5 | 0.636 | 0.421 | 0.892 | 0.694 | 0.412 | 0.56 | 0.7 | 0.364 | 0.786 | 0.746 | 0.389 |
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- | 0.6225 | 9.0 | 558 | 1.0357 | 0.618 | 0.883 | 0.542 | 0.867 | 0.412 | 0.804 | 0.697 | 0.341 | 0.52 | 0.65 | 0.318 | 0.881 | 0.687 | 0.417 |
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- | 0.612 | 10.0 | 620 | 1.0199 | 0.637 | 0.883 | 0.5 | 0.765 | 0.391 | 0.804 | 0.708 | 0.429 | 0.52 | 0.65 | 0.409 | 0.881 | 0.761 | 0.333 |
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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: 1.0364
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  - Accuracy: 0.624
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  - Auc: 0.879
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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: 0.381
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+ - Precision Class 3: 0.739
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+ - Precision Class 4: 0.769
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+ - Precision Class 5: 0.378
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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: 0.296
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+ - Recall Class 3: 0.723
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+ - Recall Class 4: 0.781
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+ - Recall Class 5: 0.424
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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: 1e-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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  | 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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+ | 0.622 | 1.0 | 62 | 0.9793 | 0.623 | 0.885 | 0.542 | 0.737 | 0.5 | 0.791 | 0.653 | 0.41 | 0.52 | 0.7 | 0.273 | 0.81 | 0.731 | 0.444 |
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+ | 0.7187 | 2.0 | 124 | 0.9794 | 0.618 | 0.885 | 0.52 | 0.722 | 0.467 | 0.773 | 0.671 | 0.405 | 0.52 | 0.65 | 0.318 | 0.81 | 0.731 | 0.417 |
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+ | 0.7135 | 3.0 | 186 | 0.9800 | 0.613 | 0.885 | 0.5 | 0.722 | 0.412 | 0.773 | 0.69 | 0.389 | 0.52 | 0.65 | 0.318 | 0.81 | 0.731 | 0.389 |
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+ | 0.7326 | 4.0 | 248 | 0.9819 | 0.599 | 0.886 | 0.481 | 0.722 | 0.412 | 0.767 | 0.681 | 0.368 | 0.52 | 0.65 | 0.318 | 0.786 | 0.701 | 0.389 |
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+ | 0.7179 | 5.0 | 310 | 0.9814 | 0.599 | 0.886 | 0.481 | 0.722 | 0.421 | 0.767 | 0.687 | 0.368 | 0.52 | 0.65 | 0.364 | 0.786 | 0.687 | 0.389 |
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+ | 0.7099 | 6.0 | 372 | 0.9816 | 0.599 | 0.885 | 0.481 | 0.722 | 0.421 | 0.767 | 0.687 | 0.368 | 0.52 | 0.65 | 0.364 | 0.786 | 0.687 | 0.389 |
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+ | 0.6899 | 7.0 | 434 | 0.9806 | 0.604 | 0.885 | 0.481 | 0.722 | 0.421 | 0.767 | 0.691 | 0.378 | 0.52 | 0.65 | 0.364 | 0.786 | 0.701 | 0.389 |
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+ | 0.7175 | 8.0 | 496 | 0.9805 | 0.599 | 0.886 | 0.481 | 0.722 | 0.389 | 0.767 | 0.681 | 0.378 | 0.52 | 0.65 | 0.318 | 0.786 | 0.701 | 0.389 |
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+ | 0.7057 | 9.0 | 558 | 0.9802 | 0.599 | 0.886 | 0.481 | 0.722 | 0.389 | 0.767 | 0.681 | 0.378 | 0.52 | 0.65 | 0.318 | 0.786 | 0.701 | 0.389 |
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+ | 0.7027 | 10.0 | 620 | 0.9802 | 0.599 | 0.886 | 0.481 | 0.722 | 0.389 | 0.767 | 0.681 | 0.378 | 0.52 | 0.65 | 0.318 | 0.786 | 0.701 | 0.389 |
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  ### Framework versions
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