---
license: cc-by-nc-4.0
base_model: mental/mental-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: mental-roberta_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. -->

# mental-roberta_stress_classification

This model is a fine-tuned version of [mental/mental-roberta-base](https://huggingface.co/mental/mental-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0096
- Accuracy: 0.9984
- F1: 0.9984
- Precision: 0.9984
- Recall: 0.9984

## 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: 2

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0006        | 1.0   | 8000  | 0.0239          | 0.9966   | 0.9966 | 0.9966    | 0.9966 |
| 0.0002        | 2.0   | 16000 | 0.0096          | 0.9984   | 0.9984 | 0.9984    | 0.9984 |


### Framework versions

- Transformers 4.38.0
- Pytorch 2.2.1+cu121
- Datasets 2.14.7
- Tokenizers 0.15.2