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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_04-06-40_f7dacc966242/events.out.tfevents.1739765200.f7dacc966242.30.2 +3 -0
  4. runs/Feb17_04-06-40_f7dacc966242/events.out.tfevents.1739765307.f7dacc966242.30.3 +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: 1.0555
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- - Accuracy: 0.624
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- - Auc: 0.867
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- - Precision Class 0: 0.4
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- - Precision Class 1: 0.762
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- - Precision Class 2: 0.429
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- - Precision Class 3: 0.72
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- - Precision Class 4: 0.7
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- - Precision Class 5: 0.5
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- - Recall Class 0: 0.421
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- - Recall Class 1: 0.696
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- - Recall Class 2: 0.444
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- - Recall Class 3: 0.766
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- - Recall Class 4: 0.766
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  - Recall Class 5: 0.364
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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: 0.0002
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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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- | 1.6248 | 1.0 | 62 | 1.4733 | 0.439 | 0.777 | 0.371 | 0.0 | 0.2 | 0.706 | 0.399 | 0.0 | 0.52 | 0.0 | 0.045 | 0.571 | 0.821 | 0.0 |
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- | 1.4241 | 2.0 | 124 | 1.3340 | 0.524 | 0.821 | 0.464 | 0.625 | 0.5 | 0.627 | 0.514 | 0.25 | 0.52 | 0.25 | 0.182 | 0.762 | 0.806 | 0.083 |
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- | 1.3082 | 3.0 | 186 | 1.2389 | 0.547 | 0.849 | 0.448 | 0.714 | 0.345 | 0.816 | 0.531 | 0.455 | 0.52 | 0.25 | 0.455 | 0.738 | 0.776 | 0.139 |
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- | 1.2177 | 4.0 | 248 | 1.1702 | 0.608 | 0.862 | 0.478 | 0.722 | 0.35 | 0.625 | 0.694 | 0.533 | 0.44 | 0.65 | 0.318 | 0.952 | 0.746 | 0.222 |
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- | 1.1415 | 5.0 | 310 | 1.1146 | 0.594 | 0.869 | 0.48 | 0.733 | 0.417 | 0.698 | 0.607 | 0.389 | 0.48 | 0.55 | 0.227 | 0.881 | 0.806 | 0.194 |
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- | 1.1024 | 6.0 | 372 | 1.0959 | 0.59 | 0.87 | 0.462 | 0.833 | 0.368 | 0.75 | 0.672 | 0.375 | 0.48 | 0.5 | 0.318 | 0.857 | 0.672 | 0.417 |
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- | 1.0609 | 7.0 | 434 | 1.0660 | 0.623 | 0.874 | 0.5 | 0.846 | 0.381 | 0.783 | 0.667 | 0.438 | 0.44 | 0.55 | 0.364 | 0.857 | 0.776 | 0.389 |
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- | 1.0444 | 8.0 | 496 | 1.0565 | 0.623 | 0.875 | 0.5 | 0.857 | 0.364 | 0.755 | 0.676 | 0.448 | 0.48 | 0.6 | 0.364 | 0.881 | 0.746 | 0.361 |
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- | 1.0295 | 9.0 | 558 | 1.0497 | 0.623 | 0.875 | 0.5 | 0.857 | 0.348 | 0.783 | 0.68 | 0.433 | 0.48 | 0.6 | 0.364 | 0.857 | 0.761 | 0.361 |
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- | 1.0067 | 10.0 | 620 | 1.0471 | 0.623 | 0.876 | 0.5 | 0.857 | 0.348 | 0.755 | 0.676 | 0.464 | 0.48 | 0.6 | 0.364 | 0.881 | 0.746 | 0.361 |
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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: 1.0043
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+ - Accuracy: 0.653
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+ - Auc: 0.876
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+ - Precision Class 0: 0.389
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+ - Precision Class 1: 0.792
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+ - Precision Class 2: 0.467
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+ - Precision Class 3: 0.755
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+ - Precision Class 4: 0.746
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+ - Precision Class 5: 0.48
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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: 0.519
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+ - Recall Class 3: 0.787
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+ - Recall Class 4: 0.781
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  - Recall Class 5: 0.364
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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: 0.0001
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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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  | 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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+ | 1.0015 | 1.0 | 62 | 1.0351 | 0.613 | 0.877 | 0.5 | 0.857 | 0.308 | 0.76 | 0.671 | 0.464 | 0.48 | 0.6 | 0.364 | 0.905 | 0.701 | 0.361 |
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+ | 0.9701 | 2.0 | 124 | 1.0177 | 0.623 | 0.879 | 0.5 | 0.867 | 0.348 | 0.755 | 0.686 | 0.452 | 0.48 | 0.65 | 0.364 | 0.881 | 0.716 | 0.389 |
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+ | 0.9532 | 3.0 | 186 | 1.0052 | 0.618 | 0.881 | 0.5 | 0.812 | 0.304 | 0.766 | 0.694 | 0.429 | 0.52 | 0.65 | 0.318 | 0.857 | 0.746 | 0.333 |
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+ | 0.9447 | 4.0 | 248 | 1.0016 | 0.618 | 0.882 | 0.545 | 0.812 | 0.308 | 0.75 | 0.71 | 0.441 | 0.48 | 0.65 | 0.364 | 0.929 | 0.657 | 0.417 |
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+ | 0.9253 | 5.0 | 310 | 0.9870 | 0.627 | 0.882 | 0.522 | 0.857 | 0.368 | 0.745 | 0.689 | 0.419 | 0.48 | 0.6 | 0.318 | 0.905 | 0.761 | 0.361 |
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+ | 0.9146 | 6.0 | 372 | 0.9955 | 0.608 | 0.881 | 0.522 | 0.867 | 0.292 | 0.745 | 0.703 | 0.4 | 0.48 | 0.65 | 0.318 | 0.905 | 0.672 | 0.389 |
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+ | 0.9142 | 7.0 | 434 | 0.9812 | 0.637 | 0.882 | 0.545 | 0.857 | 0.409 | 0.74 | 0.689 | 0.467 | 0.48 | 0.6 | 0.409 | 0.881 | 0.761 | 0.389 |
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+ | 0.9176 | 8.0 | 496 | 0.9838 | 0.627 | 0.882 | 0.545 | 0.857 | 0.36 | 0.74 | 0.69 | 0.467 | 0.48 | 0.6 | 0.409 | 0.881 | 0.731 | 0.389 |
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+ | 0.9133 | 9.0 | 558 | 0.9820 | 0.623 | 0.882 | 0.545 | 0.8 | 0.36 | 0.735 | 0.69 | 0.467 | 0.48 | 0.6 | 0.409 | 0.857 | 0.731 | 0.389 |
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+ | 0.8981 | 10.0 | 620 | 0.9816 | 0.623 | 0.882 | 0.545 | 0.8 | 0.36 | 0.735 | 0.69 | 0.467 | 0.48 | 0.6 | 0.409 | 0.857 | 0.731 | 0.389 |
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  ### Framework versions
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