<!-- 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-base-detect-depression-large-dataset-v3
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6044
- Accuracy: 0.6918
- F1: 0.7921
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: 5e-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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.6532 | 1.0 | 876 | 0.5777 | 0.6527 | 0.7536 |
| 0.6325 | 2.0 | 1752 | 0.5926 | 0.7322 | 0.8342 |
| 0.6348 | 3.0 | 2628 | 0.5959 | 0.7433 | 0.8461 |
| 0.635 | 4.0 | 3504 | 0.5781 | 0.7436 | 0.8449 |
| 0.6177 | 5.0 | 4380 | 0.6044 | 0.6918 | 0.7921 |
Framework versions
- Transformers 4.30.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
