---
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: 0.0642
- 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