mental-roberta_stress_classification
This model is a fine-tuned version of mental/mental-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0096
- Accuracy: 0.9984
- F1: 0.9984
- Precision: 0.9984
- Recall: 0.9984
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
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.0006 | 1.0 | 8000 | 0.0239 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
| 0.0002 | 2.0 | 16000 | 0.0096 | 0.9984 | 0.9984 | 0.9984 | 0.9984 |
Framework versions
- Transformers 4.38.0
- Pytorch 2.2.1+cu121
- Datasets 2.14.7
- Tokenizers 0.15.2
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Model tree for dstefa/mental-roberta_stress_classification
Base model
mental/mental-roberta-base