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
- Downloads last month
- 40
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for DoryDing/Depression_Detection_Model_v2
Base model
distilbert/distilbert-base-uncased