---
license: mit
base_model: beomi/KcELECTRA-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: 0322_cosmetic3_kcelectra
  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. -->

# 0322_cosmetic3_kcelectra

This model is a fine-tuned version of [beomi/KcELECTRA-base](https://huggingface.co/beomi/KcELECTRA-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3637
- Accuracy: 0.8700
- F1: 0.8703
- Precision: 0.8789
- Recall: 0.8700

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4536        | 1.0   | 277  | 0.3556          | 0.8768   | 0.8734 | 0.8873    | 0.8768 |
| 0.2608        | 2.0   | 554  | 0.5060          | 0.8261   | 0.8252 | 0.8415    | 0.8261 |
| 0.1171        | 3.0   | 831  | 0.5406          | 0.8623   | 0.8571 | 0.8768    | 0.8623 |
| 0.1393        | 4.0   | 1108 | 0.5734          | 0.8768   | 0.8752 | 0.8862    | 0.8768 |
| 0.2115        | 5.0   | 1385 | 0.6661          | 0.8913   | 0.8915 | 0.8924    | 0.8913 |
| 0.0939        | 6.0   | 1662 | 0.5506          | 0.9058   | 0.9054 | 0.9057    | 0.9058 |
| 0.1122        | 7.0   | 1939 | 0.6672          | 0.8986   | 0.8985 | 0.8987    | 0.8986 |
| 0.2413        | 8.0   | 2216 | 0.7136          | 0.8949   | 0.8949 | 0.8950    | 0.8949 |
| 0.001         | 9.0   | 2493 | 0.6689          | 0.9058   | 0.9058 | 0.9058    | 0.9058 |
| 0.0013        | 10.0  | 2770 | 0.6764          | 0.9094   | 0.9094 | 0.9094    | 0.9094 |


### Framework versions

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