mental_bert
This model is a fine-tuned version of mental/mental-bert-base-uncased on hackathon-somos-nlp-2023/DiagTrast. It achieves the following results on the evaluation and test sets:
- Evaluation Loss: 0.9179
- Test Loss: 0.9831
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: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 100
- training_steps: 2000
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.4138 | 6.25 | 100 | 1.1695 |
| 1.0912 | 12.5 | 200 | 1.1862 |
| 0.8699 | 18.75 | 300 | 0.9926 |
| 0.7713 | 25.0 | 400 | 1.0570 |
| 0.6655 | 31.25 | 500 | 1.0891 |
| 0.6127 | 37.5 | 600 | 1.0389 |
| 0.5461 | 43.75 | 700 | 0.9947 |
| 0.5167 | 50.0 | 800 | 1.0043 |
| 0.45 | 56.25 | 900 | 0.9688 |
| 0.436 | 62.5 | 1000 | 0.9482 |
| 0.3896 | 68.75 | 1100 | 1.0424 |
| 0.3624 | 75.0 | 1200 | 0.9242 |
| 0.3821 | 81.25 | 1300 | 1.0748 |
| 0.3156 | 87.5 | 1400 | 1.0121 |
| 0.3099 | 93.75 | 1500 | 0.9404 |
| 0.2829 | 100.0 | 1600 | 0.8997 |
| 0.2712 | 106.25 | 1700 | 0.8902 |
| 0.2596 | 112.5 | 1800 | 0.9054 |
| 0.2622 | 118.75 | 1900 | 1.0317 |
| 0.2631 | 125.0 | 2000 | 0.9179 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3