| | --- |
| | library_name: transformers |
| | language: |
| | - ko |
| | license: apache-2.0 |
| | base_model: monologg/koelectra-base-v3-discriminator |
| | tags: |
| | - text-classification |
| | - KoELECTRA |
| | - Korean-NLP |
| | - topic-classification |
| | - news-classification |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - precision |
| | - recall |
| | - f1 |
| | model-index: |
| | - name: ynay-model |
| | 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. --> |
| |
|
| | # ynay-model |
| |
|
| | This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator) on the klue-ynat dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.4107 |
| | - Accuracy: 0.8618 |
| | - Precision: 0.8528 |
| | - Recall: 0.8736 |
| | - F1: 0.8627 |
| |
|
| | ## 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: 64 |
| | - eval_batch_size: 64 |
| | - 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: 3 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | | 0.3902 | 1.0 | 714 | 0.4777 | 0.8340 | 0.8092 | 0.8725 | 0.8364 | |
| | | 0.2974 | 2.0 | 1428 | 0.3960 | 0.8566 | 0.8475 | 0.8691 | 0.8571 | |
| | | 0.2159 | 3.0 | 2142 | 0.4107 | 0.8618 | 0.8528 | 0.8736 | 0.8627 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.57.1 |
| | - Pytorch 2.8.0+cu126 |
| | - Datasets 4.0.0 |
| | - Tokenizers 0.22.1 |
| | |
