metadata
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: roberta-base_stress_classification
results: []
roberta-base_stress_classification
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0642
- Accuracy: 0.9844
- F1: 0.9844
- Precision: 0.9848
- Recall: 0.9844
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.3508 | 1.0 | 160 | 0.2642 | 0.9031 | 0.9027 | 0.9102 | 0.9031 |
| 0.4855 | 2.0 | 320 | 0.1365 | 0.975 | 0.9750 | 0.9751 | 0.975 |
| 0.0655 | 3.0 | 480 | 0.0642 | 0.9844 | 0.9844 | 0.9848 | 0.9844 |
| 0.0884 | 4.0 | 640 | 0.0699 | 0.9875 | 0.9875 | 0.9878 | 0.9875 |
| 0.0006 | 5.0 | 800 | 0.0741 | 0.9844 | 0.9844 | 0.9845 | 0.9844 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0