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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_03-59-23_8dab684b9a4e/events.out.tfevents.1739764763.8dab684b9a4e.30.2 +3 -0
  4. runs/Feb17_03-59-23_8dab684b9a4e/events.out.tfevents.1739764842.8dab684b9a4e.30.3 +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: 0.9911
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- - Accuracy: 0.634
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  - Auc: 0.886
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- - Precision Class 0: 0.368
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- - Precision Class 1: 0.76
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- - Precision Class 2: 0.394
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- - Precision Class 3: 0.706
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- - Precision Class 4: 0.794
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- - Precision Class 5: 0.455
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  - Recall Class 0: 0.368
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  - Recall Class 1: 0.826
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- - Recall Class 2: 0.481
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- - Recall Class 3: 0.766
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- - Recall Class 4: 0.781
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- - Recall Class 5: 0.303
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  ## Model description
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@@ -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: 0.001
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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
@@ -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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- | 1.487 | 1.0 | 62 | 1.1345 | 0.547 | 0.863 | 0.471 | 0.727 | 0.0 | 0.783 | 0.646 | 0.306 | 0.32 | 0.4 | 0.0 | 0.857 | 0.627 | 0.611 |
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- | 1.137 | 2.0 | 124 | 1.1035 | 0.561 | 0.871 | 0.667 | 0.593 | 0.417 | 0.853 | 0.722 | 0.329 | 0.16 | 0.8 | 0.227 | 0.69 | 0.582 | 0.722 |
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- | 1.0435 | 3.0 | 186 | 0.9909 | 0.608 | 0.88 | 0.361 | 0.75 | 0.435 | 0.795 | 0.676 | 0.474 | 0.52 | 0.6 | 0.455 | 0.833 | 0.746 | 0.25 |
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- | 0.9615 | 4.0 | 248 | 1.0186 | 0.623 | 0.884 | 0.667 | 0.917 | 0.385 | 0.796 | 0.554 | 0.455 | 0.4 | 0.55 | 0.227 | 0.929 | 0.925 | 0.139 |
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- | 0.8951 | 5.0 | 310 | 0.9772 | 0.613 | 0.884 | 0.692 | 0.68 | 0.429 | 0.791 | 0.712 | 0.345 | 0.36 | 0.85 | 0.136 | 0.81 | 0.701 | 0.556 |
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- | 0.8591 | 6.0 | 372 | 0.9818 | 0.642 | 0.879 | 0.483 | 0.923 | 0.6 | 0.892 | 0.655 | 0.389 | 0.56 | 0.6 | 0.273 | 0.786 | 0.851 | 0.389 |
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- | 0.8471 | 7.0 | 434 | 0.9825 | 0.646 | 0.885 | 0.5 | 0.923 | 0.474 | 0.8 | 0.671 | 0.409 | 0.56 | 0.6 | 0.409 | 0.857 | 0.851 | 0.25 |
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- | 0.8187 | 8.0 | 496 | 0.9950 | 0.637 | 0.884 | 0.471 | 0.652 | 0.556 | 0.833 | 0.688 | 0.333 | 0.64 | 0.75 | 0.455 | 0.833 | 0.791 | 0.167 |
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- | 0.772 | 9.0 | 558 | 0.9836 | 0.618 | 0.884 | 0.5 | 0.812 | 0.435 | 0.755 | 0.73 | 0.359 | 0.44 | 0.65 | 0.455 | 0.881 | 0.687 | 0.389 |
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- | 0.7256 | 10.0 | 620 | 0.9703 | 0.642 | 0.883 | 0.538 | 0.765 | 0.4 | 0.795 | 0.701 | 0.435 | 0.56 | 0.65 | 0.455 | 0.833 | 0.806 | 0.278 |
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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: 1.0146
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+ - Accuracy: 0.61
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  - Auc: 0.886
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+ - Precision Class 0: 0.35
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+ - Precision Class 1: 0.731
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+ - Precision Class 2: 0.375
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+ - Precision Class 3: 0.686
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+ - Precision Class 4: 0.762
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+ - Precision Class 5: 0.429
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  - Recall Class 0: 0.368
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  - Recall Class 1: 0.826
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+ - Recall Class 2: 0.444
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+ - Recall Class 3: 0.745
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+ - Recall Class 4: 0.75
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+ - Recall Class 5: 0.273
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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: 0.0003
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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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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+ | 0.7936 | 1.0 | 31 | 1.0036 | 0.623 | 0.881 | 0.467 | 0.812 | 0.312 | 0.857 | 0.726 | 0.467 | 0.56 | 0.65 | 0.455 | 0.857 | 0.672 | 0.389 |
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+ | 0.7417 | 2.0 | 62 | 0.9934 | 0.646 | 0.882 | 0.471 | 0.765 | 0.5 | 0.833 | 0.697 | 0.435 | 0.64 | 0.65 | 0.455 | 0.833 | 0.791 | 0.278 |
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+ | 0.7247 | 3.0 | 93 | 0.9742 | 0.632 | 0.884 | 0.5 | 0.7 | 0.385 | 0.783 | 0.703 | 0.444 | 0.56 | 0.7 | 0.455 | 0.857 | 0.776 | 0.222 |
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+ | 0.7387 | 4.0 | 124 | 0.9862 | 0.656 | 0.884 | 0.647 | 0.833 | 0.391 | 0.736 | 0.739 | 0.438 | 0.44 | 0.75 | 0.409 | 0.929 | 0.761 | 0.389 |
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+ | 0.7202 | 5.0 | 155 | 0.9747 | 0.651 | 0.885 | 0.5 | 0.875 | 0.45 | 0.822 | 0.692 | 0.4 | 0.56 | 0.7 | 0.409 | 0.881 | 0.806 | 0.278 |
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+ | 0.6958 | 6.0 | 186 | 0.9923 | 0.627 | 0.884 | 0.481 | 0.812 | 0.4 | 0.776 | 0.746 | 0.375 | 0.52 | 0.65 | 0.455 | 0.905 | 0.701 | 0.333 |
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+ | 0.695 | 7.0 | 217 | 0.9753 | 0.646 | 0.884 | 0.5 | 0.875 | 0.476 | 0.8 | 0.684 | 0.407 | 0.48 | 0.7 | 0.455 | 0.857 | 0.806 | 0.306 |
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+ | 0.7126 | 8.0 | 248 | 0.9798 | 0.646 | 0.884 | 0.5 | 0.824 | 0.5 | 0.783 | 0.701 | 0.385 | 0.52 | 0.7 | 0.455 | 0.857 | 0.806 | 0.278 |
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+ | 0.7102 | 9.0 | 279 | 0.9798 | 0.642 | 0.883 | 0.519 | 0.778 | 0.5 | 0.791 | 0.701 | 0.37 | 0.56 | 0.7 | 0.455 | 0.81 | 0.806 | 0.278 |
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+ | 0.6941 | 10.0 | 310 | 0.9802 | 0.642 | 0.884 | 0.519 | 0.778 | 0.5 | 0.791 | 0.701 | 0.37 | 0.56 | 0.7 | 0.455 | 0.81 | 0.806 | 0.278 |
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  ### Framework versions
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