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# KcELECTRA-small-v2022-finetuned-in-vehicle

This model is a fine-tuned version of [beomi/KcELECTRA-small-v2022](https://huggingface.co/beomi/KcELECTRA-small-v2022) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5573
- Accuracy: 0.8908
- F1: 0.8752

## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 3.3864        | 1.0   | 150  | 3.3222          | 0.0992   | 0.0179 |
| 3.2362        | 2.0   | 300  | 3.0647          | 0.2342   | 0.1422 |
| 2.9257        | 3.0   | 450  | 2.7001          | 0.3467   | 0.2377 |
| 2.5953        | 4.0   | 600  | 2.3780          | 0.46     | 0.3409 |
| 2.3033        | 5.0   | 750  | 2.1041          | 0.5025   | 0.3911 |
| 2.0477        | 6.0   | 900  | 1.8675          | 0.5583   | 0.4585 |
| 1.8309        | 7.0   | 1050 | 1.6711          | 0.6167   | 0.5354 |
| 1.6455        | 8.0   | 1200 | 1.5126          | 0.6667   | 0.5924 |
| 1.4883        | 9.0   | 1350 | 1.3677          | 0.7325   | 0.6660 |
| 1.3473        | 10.0  | 1500 | 1.2435          | 0.7542   | 0.6914 |
| 1.23          | 11.0  | 1650 | 1.1460          | 0.7517   | 0.6911 |
| 1.1299        | 12.0  | 1800 | 1.0577          | 0.7792   | 0.7251 |
| 1.0437        | 13.0  | 1950 | 0.9860          | 0.7908   | 0.7397 |
| 0.9683        | 14.0  | 2100 | 0.9219          | 0.8092   | 0.7625 |
| 0.8973        | 15.0  | 2250 | 0.8622          | 0.8342   | 0.8028 |
| 0.8417        | 16.0  | 2400 | 0.8129          | 0.8408   | 0.8122 |
| 0.7891        | 17.0  | 2550 | 0.7719          | 0.85     | 0.8264 |
| 0.743         | 18.0  | 2700 | 0.7323          | 0.8583   | 0.8353 |
| 0.7037        | 19.0  | 2850 | 0.7005          | 0.8583   | 0.8341 |
| 0.6715        | 20.0  | 3000 | 0.6696          | 0.8717   | 0.8494 |
| 0.6396        | 21.0  | 3150 | 0.6469          | 0.8767   | 0.8546 |
| 0.6117        | 22.0  | 3300 | 0.6285          | 0.8808   | 0.8613 |
| 0.5897        | 23.0  | 3450 | 0.6137          | 0.88     | 0.8618 |
| 0.5684        | 24.0  | 3600 | 0.5956          | 0.88     | 0.8604 |
| 0.5522        | 25.0  | 3750 | 0.5851          | 0.8808   | 0.8622 |
| 0.5429        | 26.0  | 3900 | 0.5741          | 0.8867   | 0.8697 |
| 0.53          | 27.0  | 4050 | 0.5672          | 0.8892   | 0.8730 |
| 0.5234        | 28.0  | 4200 | 0.5616          | 0.8892   | 0.8733 |
| 0.5184        | 29.0  | 4350 | 0.5580          | 0.89     | 0.8738 |
| 0.5138        | 30.0  | 4500 | 0.5573          | 0.8908   | 0.8752 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
{"language": ["ko"], "license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "pipeline_tag": "text-classification", "widget": [{"text": "\uc624\ub298 \ub0a0\uc528 \uc54c\ub824\uc918.", "example_title": "Example 1"}, {"text": "\ubc29\ud0c4\uc18c\ub144\ub2e8 \ub178\ub798 \ucc3e\uc544\uc918.", "example_title": "Example 2"}], "model-index": [{"name": "KcELECTRA-small-v2022-finetuned-in-vehicle", "results": []}]}
transformers pytorch tensorboard safetensors electra text-classification generated_from_trainer ko license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #electra #text-classification #generated_from_trainer #ko #license-mit #autotrain_compatible #endpoints_compatible #region-us 

KcELECTRA-small-v2022-finetuned-in-vehicle
==========================================


This model is a fine-tuned version of beomi/KcELECTRA-small-v2022 on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5573
* Accuracy: 0.8908
* F1: 0.8752


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: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #electra #text-classification #generated_from_trainer #ko #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #electra #text-classification #generated_from_trainer #ko #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2