File size: 5,109 Bytes
1760e3a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
---
library_name: transformers
license: cc-by-nc-4.0
base_model: mental/mental-bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: mentalBERT-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. -->
# mentalBERT-wellness-classifier
This model is a fine-tuned version of [mental/mental-bert-base-uncased](https://huggingface.co/mental/mental-bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0471
- Accuracy: 0.722
- Auc: 0.915
- Precision Class 0: 0.765
- Precision Class 1: 0.762
- Precision Class 2: 0.745
- Precision Class 3: 0.684
- Recall Class 0: 0.736
- Recall Class 1: 0.593
- Recall Class 2: 0.651
- Recall Class 3: 0.796
- F1 Score Class 0: 0.75
- F1 Score Class 1: 0.667
- F1 Score Class 2: 0.695
- F1 Score Class 3: 0.736
## 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 | F1 Score Class 0 | F1 Score Class 1 | F1 Score Class 2 | F1 Score Class 3 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:-----------------:|:-----------------:|:-----------------:|:-----------------:|:--------------:|:--------------:|:--------------:|:--------------:|:----------------:|:----------------:|:----------------:|:----------------:|
| 0.0887 | 1.0 | 140 | 1.2702 | 0.73 | 0.91 | 0.783 | 0.773 | 0.746 | 0.693 | 0.679 | 0.63 | 0.698 | 0.806 | 0.727 | 0.694 | 0.721 | 0.745 |
| 0.2058 | 2.0 | 280 | 1.0340 | 0.718 | 0.916 | 0.75 | 0.72 | 0.736 | 0.694 | 0.736 | 0.667 | 0.619 | 0.786 | 0.743 | 0.692 | 0.672 | 0.737 |
| 0.1921 | 3.0 | 420 | 1.0234 | 0.739 | 0.919 | 0.733 | 0.731 | 0.75 | 0.737 | 0.83 | 0.704 | 0.667 | 0.745 | 0.779 | 0.717 | 0.706 | 0.741 |
| 0.184 | 4.0 | 560 | 1.0486 | 0.73 | 0.917 | 0.719 | 0.773 | 0.738 | 0.723 | 0.774 | 0.63 | 0.714 | 0.745 | 0.745 | 0.694 | 0.726 | 0.734 |
| 0.1666 | 5.0 | 700 | 1.0084 | 0.747 | 0.918 | 0.769 | 0.739 | 0.738 | 0.743 | 0.755 | 0.63 | 0.762 | 0.765 | 0.762 | 0.68 | 0.75 | 0.754 |
| 0.1558 | 6.0 | 840 | 1.0314 | 0.739 | 0.917 | 0.759 | 0.773 | 0.738 | 0.721 | 0.774 | 0.63 | 0.714 | 0.765 | 0.766 | 0.694 | 0.726 | 0.743 |
| 0.138 | 7.0 | 980 | 1.0733 | 0.73 | 0.915 | 0.776 | 0.773 | 0.75 | 0.693 | 0.717 | 0.63 | 0.667 | 0.806 | 0.745 | 0.694 | 0.706 | 0.745 |
| 0.1481 | 8.0 | 1120 | 1.0656 | 0.722 | 0.914 | 0.787 | 0.773 | 0.741 | 0.678 | 0.698 | 0.63 | 0.635 | 0.816 | 0.74 | 0.694 | 0.684 | 0.741 |
| 0.1433 | 9.0 | 1260 | 1.0520 | 0.718 | 0.915 | 0.765 | 0.762 | 0.741 | 0.678 | 0.736 | 0.593 | 0.635 | 0.796 | 0.75 | 0.667 | 0.684 | 0.732 |
| 0.1577 | 10.0 | 1400 | 1.0471 | 0.722 | 0.915 | 0.765 | 0.762 | 0.745 | 0.684 | 0.736 | 0.593 | 0.651 | 0.796 | 0.75 | 0.667 | 0.695 | 0.736 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|