README.md

---
library_name: transformers
license: apache-2.0
base_model: monologg/koelectra-base-v3-discriminator
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: MyMbti_classification_model
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# MyMbti_classification_model

This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5286
- Accuracy: 0.1898
- F1: 0.1547

## Model description

이 모델은 16개의 MBTI를 라벨로 분류해 해당 라벨을 예측하는 모델입니다.
모델의 정확도가 낮은것은 학습에 사용한 데이터가 정제되지 않았습니다.
테스트용으로 만들었기 때문에 성능은 보장하지 못합니다.

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|
| 2.6213        | 0.1673 | 500   | 2.6180          | 0.1142   | 0.0241 |
| 2.6412        | 0.3347 | 1000  | 2.6167          | 0.1318   | 0.0336 |
| 2.5861        | 0.5020 | 1500  | 2.6111          | 0.1320   | 0.0385 |
| 2.6183        | 0.6693 | 2000  | 2.6133          | 0.1222   | 0.0461 |
| 2.5954        | 0.8367 | 2500  | 2.5958          | 0.1411   | 0.0607 |
| 2.5828        | 1.0040 | 3000  | 2.5822          | 0.1479   | 0.0703 |
| 2.5803        | 1.1714 | 3500  | 2.5685          | 0.1553   | 0.0826 |
| 2.5615        | 1.3387 | 4000  | 2.5566          | 0.1645   | 0.0977 |
| 2.5463        | 1.5060 | 4500  | 2.5531          | 0.1687   | 0.1111 |
| 2.5511        | 1.6734 | 5000  | 2.5446          | 0.1679   | 0.1170 |
| 2.5242        | 1.8407 | 5500  | 2.5342          | 0.1726   | 0.1215 |
| 2.5191        | 2.0080 | 6000  | 2.5246          | 0.1825   | 0.1384 |
| 2.4866        | 2.1754 | 6500  | 2.5306          | 0.1834   | 0.1428 |
| 2.5005        | 2.3427 | 7000  | 2.5325          | 0.1803   | 0.1399 |
| 2.5131        | 2.5100 | 7500  | 2.5195          | 0.1877   | 0.1473 |
| 2.4918        | 2.6774 | 8000  | 2.5204          | 0.1876   | 0.1489 |
| 2.4755        | 2.8447 | 8500  | 2.5218          | 0.1877   | 0.1568 |
| 2.4223        | 3.0120 | 9000  | 2.5286          | 0.1898   | 0.1547 |
| 2.4297        | 3.1794 | 9500  | 2.5364          | 0.1874   | 0.1599 |
| 2.4213        | 3.3467 | 10000 | 2.5432          | 0.1866   | 0.1584 |
| 2.4619        | 3.5141 | 10500 | 2.5393          | 0.1879   | 0.1585 |
| 2.4383        | 3.6814 | 11000 | 2.5424          | 0.1849   | 0.1590 |
| 2.4368        | 3.8487 | 11500 | 2.5414          | 0.1866   | 0.1599 |


### Framework versions

- Transformers 4.55.2
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4