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---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-wellness-classifier
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. -->
# roberta-wellness-classifier
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8807
- Accuracy: 0.71
- Auc: 0.871
- Precision Class 0: 0.787
- Precision Class 1: 0.857
- Precision Class 2: 0.731
- Precision Class 3: 0.645
- Recall Class 0: 0.698
- Recall Class 1: 0.667
- Recall Class 2: 0.603
- Recall Class 3: 0.796
## 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: 3e-05
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:-----------------:|:-----------------:|:-----------------:|:-----------------:|:--------------:|:--------------:|:--------------:|:--------------:|
| 1.0423 | 1.0 | 140 | 0.7782 | 0.685 | 0.888 | 0.732 | 0.727 | 0.595 | 0.734 | 0.774 | 0.593 | 0.794 | 0.592 |
| 0.6558 | 2.0 | 280 | 0.7589 | 0.714 | 0.902 | 0.733 | 0.85 | 0.78 | 0.658 | 0.83 | 0.63 | 0.508 | 0.806 |
| 0.4306 | 3.0 | 420 | 1.0251 | 0.73 | 0.89 | 0.738 | 0.857 | 0.64 | 0.795 | 0.849 | 0.667 | 0.873 | 0.592 |
| 0.3002 | 4.0 | 560 | 1.2314 | 0.726 | 0.908 | 0.816 | 0.938 | 0.707 | 0.669 | 0.755 | 0.556 | 0.651 | 0.806 |
| 0.2117 | 5.0 | 700 | 1.3601 | 0.714 | 0.888 | 0.857 | 0.941 | 0.645 | 0.67 | 0.679 | 0.593 | 0.778 | 0.724 |
| 0.1606 | 6.0 | 840 | 1.4648 | 0.718 | 0.887 | 0.784 | 0.933 | 0.682 | 0.679 | 0.755 | 0.519 | 0.714 | 0.755 |
| 0.1135 | 7.0 | 980 | 1.6228 | 0.714 | 0.883 | 0.78 | 0.826 | 0.698 | 0.667 | 0.736 | 0.704 | 0.698 | 0.714 |
| 0.0686 | 8.0 | 1120 | 1.8947 | 0.71 | 0.866 | 0.809 | 0.857 | 0.745 | 0.635 | 0.717 | 0.667 | 0.556 | 0.816 |
| 0.0525 | 9.0 | 1260 | 1.8817 | 0.718 | 0.875 | 0.796 | 0.864 | 0.74 | 0.65 | 0.736 | 0.704 | 0.587 | 0.796 |
| 0.0526 | 10.0 | 1400 | 1.8807 | 0.71 | 0.871 | 0.787 | 0.857 | 0.731 | 0.645 | 0.698 | 0.667 | 0.603 | 0.796 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
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