mental_classification
This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6424
- Accuracy: 0.8623
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: 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: 14
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.356 | 1.4046 | 184 | 1.6835 | 0.5908 |
| 1.2119 | 2.8092 | 368 | 1.1011 | 0.7648 |
| 0.6548 | 4.2137 | 552 | 0.8192 | 0.8241 |
| 0.3782 | 5.6183 | 736 | 0.6968 | 0.8375 |
| 0.1931 | 7.0229 | 920 | 0.6587 | 0.8528 |
| 0.1127 | 8.4275 | 1104 | 0.6390 | 0.8566 |
| 0.081 | 9.8321 | 1288 | 0.6382 | 0.8566 |
| 0.0532 | 11.2366 | 1472 | 0.6433 | 0.8623 |
| 0.0416 | 12.6412 | 1656 | 0.6424 | 0.8623 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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Base model
distilbert/distilbert-base-uncased