Depression_Detection_Model_v2
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0711
- Accuracy: 0.9825
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
https://github.com/DoryDing/Depression_Detection_Dataset.git
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 376 | 0.0954 | 0.9683 |
| 0.1028 | 2.0 | 752 | 0.1432 | 0.9683 |
| 0.0364 | 3.0 | 1128 | 0.0711 | 0.9825 |
| 0.0139 | 4.0 | 1504 | 0.1701 | 0.97 |
| 0.0139 | 5.0 | 1880 | 0.1093 | 0.9808 |
| 0.0059 | 6.0 | 2256 | 0.1272 | 0.9775 |
Framework versions
- Transformers 4.31.0
- Pytorch 1.13.1
- Datasets 2.14.0
- Tokenizers 0.13.3
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Model tree for DoryDing/Depression_Detection_Model_v2
Base model
distilbert/distilbert-base-uncased