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: 0.1800
- Accuracy: 0.9647
- F1: 0.9647
- Precision: 0.9647
- Recall: 0.9647
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 |
|---|---|---|---|---|---|---|---|
| 0.704 | 1.0 | 8000 | 0.6933 | 0.5 | 0.3333 | 0.25 | 0.5 |
| 0.6926 | 2.0 | 16000 | 0.6980 | 0.5 | 0.3333 | 0.25 | 0.5 |
| 0.0099 | 3.0 | 24000 | 0.1800 | 0.9647 | 0.9647 | 0.9647 | 0.9647 |
| 0.2727 | 4.0 | 32000 | 0.2243 | 0.9526 | 0.9526 | 0.9527 | 0.9526 |
| 0.0618 | 5.0 | 40000 | 0.2128 | 0.9536 | 0.9536 | 0.9546 | 0.9536 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2