---
base_model: klue/roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: 0320_cosmetic2_roberta
  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. -->

# 0320_cosmetic2_roberta

This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4218
- Accuracy: 0.8535
- F1: 0.8554
- Precision: 0.8632
- Recall: 0.8535

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4995        | 1.0   | 273  | 0.3611          | 0.8713   | 0.8724 | 0.8767    | 0.8713 |
| 0.4616        | 2.0   | 546  | 0.4809          | 0.8419   | 0.8435 | 0.8658    | 0.8419 |
| 0.2517        | 3.0   | 819  | 0.7009          | 0.8640   | 0.8651 | 0.8772    | 0.8640 |
| 0.6884        | 4.0   | 1092 | 0.7427          | 0.7978   | 0.8007 | 0.8425    | 0.7978 |
| 0.4318        | 5.0   | 1365 | 0.4725          | 0.8640   | 0.8647 | 0.8660    | 0.8640 |
| 0.2824        | 6.0   | 1638 | 0.6081          | 0.875    | 0.8759 | 0.8798    | 0.875  |
| 0.317         | 7.0   | 1911 | 0.5933          | 0.8676   | 0.8665 | 0.8672    | 0.8676 |
| 0.2067        | 8.0   | 2184 | 0.6951          | 0.8676   | 0.8671 | 0.8702    | 0.8676 |
| 0.037         | 9.0   | 2457 | 0.6081          | 0.8860   | 0.8851 | 0.8896    | 0.8860 |
| 0.1009        | 10.0  | 2730 | 0.7525          | 0.8640   | 0.8642 | 0.8651    | 0.8640 |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2