---
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: 0.1800
- 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