metadata
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-depression-detection-hpc
results: []
roberta-depression-detection-hpc
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1689
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: 4e-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
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.2808 | 1.0 | 215 | 0.1689 |
| 0.144 | 2.0 | 430 | 0.2712 |
| 0.0704 | 3.0 | 645 | 0.2503 |
| 0.0791 | 4.0 | 860 | 0.2868 |
| 0.0488 | 5.0 | 1075 | 0.2350 |
| 0.061 | 6.0 | 1290 | 0.2155 |
| 0.036 | 7.0 | 1505 | 0.2738 |
| 0.0003 | 8.0 | 1720 | 0.2611 |
| 0.0434 | 9.0 | 1935 | 0.2930 |
| 0.0001 | 10.0 | 2150 | 0.2974 |
| 0.0001 | 11.0 | 2365 | 0.3735 |
| 0.0 | 12.0 | 2580 | 0.3582 |
| 0.0 | 13.0 | 2795 | 0.3533 |
| 0.0 | 14.0 | 3010 | 0.3443 |
| 0.0 | 15.0 | 3225 | 0.3436 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.1.0+cpu
- Datasets 2.10.1
- Tokenizers 0.13.2