---
library_name: transformers
base_model: skt/kobert-base-v1
tags:
- generated_from_trainer
metrics:
- f1
- precision
- recall
model-index:
- name: GS_bert
  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. -->

# GS_bert

This model is a fine-tuned version of [skt/kobert-base-v1](https://huggingface.co/skt/kobert-base-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1479
- F1: 0.2907
- Precision: 0.3056
- Recall: 0.2796

## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|
| 0.1873        | 1.0   | 90   | 0.1755          | 0.0291 | 0.0315    | 0.0273 |
| 0.1681        | 2.0   | 180  | 0.1618          | 0.0381 | 0.0407    | 0.0361 |
| 0.1692        | 3.0   | 270  | 0.1617          | 0.0421 | 0.0444    | 0.0403 |
| 0.1715        | 4.0   | 360  | 0.1616          | 0.0397 | 0.0426    | 0.0375 |
| 0.1677        | 5.0   | 450  | 0.1615          | 0.0423 | 0.0444    | 0.0407 |
| 0.1676        | 6.0   | 540  | 0.1615          | 0.0421 | 0.0444    | 0.0403 |
| 0.1682        | 7.0   | 630  | 0.1616          | 0.0394 | 0.0426    | 0.0370 |
| 0.1665        | 8.0   | 720  | 0.1613          | 0.0476 | 0.05      | 0.0458 |
| 0.1658        | 9.0   | 810  | 0.1610          | 0.0616 | 0.0667    | 0.0579 |
| 0.1674        | 10.0  | 900  | 0.1599          | 0.0987 | 0.1056    | 0.0935 |
| 0.1613        | 11.0  | 990  | 0.1587          | 0.1206 | 0.1278    | 0.1153 |
| 0.1608        | 12.0  | 1080 | 0.1568          | 0.1722 | 0.1796    | 0.1667 |
| 0.1612        | 13.0  | 1170 | 0.1550          | 0.1910 | 0.2019    | 0.1829 |
| 0.1581        | 14.0  | 1260 | 0.1532          | 0.2423 | 0.2537    | 0.2338 |
| 0.1575        | 15.0  | 1350 | 0.1516          | 0.2862 | 0.3       | 0.2759 |
| 0.1548        | 16.0  | 1440 | 0.1505          | 0.2878 | 0.3019    | 0.2773 |
| 0.1517        | 17.0  | 1530 | 0.1493          | 0.2979 | 0.3130    | 0.2866 |
| 0.1507        | 18.0  | 1620 | 0.1486          | 0.2907 | 0.3056    | 0.2796 |
| 0.1519        | 19.0  | 1710 | 0.1481          | 0.2907 | 0.3056    | 0.2796 |
| 0.1497        | 20.0  | 1800 | 0.1479          | 0.2907 | 0.3056    | 0.2796 |


### Framework versions

- Transformers 4.50.0.dev0
- Pytorch 2.5.1+cu121
- Datasets 3.4.1
- Tokenizers 0.21.0