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---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-wellness-classifier_teacher
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-wellness-classifier_teacher
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.
It achieves the following results on the evaluation set:
- Loss: 0.7694
- Accuracy: 0.688
- Auc: 0.9
- Precision Class 0: 0.862
- Precision Class 1: 0.739
- Precision Class 2: 0.613
- Precision Class 3: 0.538
- Recall Class 0: 0.781
- Recall Class 1: 0.81
- Recall Class 2: 0.704
- Recall Class 3: 0.483
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc | Precision Class 0 | Precision Class 1 | Precision Class 2 | Precision Class 3 | Recall Class 0 | Recall Class 1 | Recall Class 2 | Recall Class 3 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:-----------------:|:-----------------:|:-----------------:|:-----------------:|:--------------:|:--------------:|:--------------:|:--------------:|
| 1.3247 | 1.0 | 63 | 1.1856 | 0.569 | 0.819 | 0.759 | 0.727 | 0.414 | 0.727 | 0.688 | 0.381 | 0.889 | 0.276 |
| 1.0767 | 2.0 | 126 | 1.0166 | 0.587 | 0.857 | 0.667 | 0.643 | 1.0 | 0.462 | 0.75 | 0.429 | 0.259 | 0.828 |
| 0.9285 | 3.0 | 189 | 0.9616 | 0.633 | 0.875 | 0.846 | 0.486 | 1.0 | 0.528 | 0.688 | 0.857 | 0.37 | 0.655 |
| 0.8628 | 4.0 | 252 | 0.8910 | 0.624 | 0.885 | 0.88 | 0.514 | 0.64 | 0.5 | 0.688 | 0.857 | 0.593 | 0.414 |
| 0.7828 | 5.0 | 315 | 0.8369 | 0.679 | 0.888 | 0.88 | 0.667 | 0.75 | 0.514 | 0.688 | 0.857 | 0.556 | 0.655 |
| 0.7489 | 6.0 | 378 | 0.7962 | 0.706 | 0.899 | 0.857 | 0.762 | 0.704 | 0.545 | 0.75 | 0.762 | 0.704 | 0.621 |
| 0.6981 | 7.0 | 441 | 0.8118 | 0.679 | 0.896 | 0.88 | 0.708 | 0.633 | 0.533 | 0.688 | 0.81 | 0.704 | 0.552 |
| 0.6634 | 8.0 | 504 | 0.7915 | 0.688 | 0.898 | 0.889 | 0.708 | 0.655 | 0.517 | 0.75 | 0.81 | 0.704 | 0.517 |
| 0.6651 | 9.0 | 567 | 0.7777 | 0.67 | 0.9 | 0.862 | 0.739 | 0.576 | 0.5 | 0.781 | 0.81 | 0.704 | 0.414 |
| 0.6591 | 10.0 | 630 | 0.7694 | 0.688 | 0.9 | 0.862 | 0.739 | 0.613 | 0.538 | 0.781 | 0.81 | 0.704 | 0.483 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0+cpu
- Datasets 3.0.1
- Tokenizers 0.20.0
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