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---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: roberta-base_stress_classification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base_stress_classification
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0389
- Accuracy: 0.9938
- F1: 0.9938
- Precision: 0.9938
- Recall: 0.9938
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2345 | 1.0 | 160 | 0.1980 | 0.9437 | 0.9437 | 0.9449 | 0.9437 |
| 0.2676 | 2.0 | 320 | 0.1086 | 0.9844 | 0.9844 | 0.9848 | 0.9844 |
| 0.0393 | 3.0 | 480 | 0.1011 | 0.9812 | 0.9812 | 0.9816 | 0.9812 |
| 0.1025 | 4.0 | 640 | 0.0389 | 0.9938 | 0.9938 | 0.9938 | 0.9938 |
| 0.0004 | 5.0 | 800 | 0.0654 | 0.9875 | 0.9875 | 0.9876 | 0.9875 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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