---
library_name: transformers
base_model: monologg/koelectra-small-v3-discriminator
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: koelectra
  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. -->

# koelectra

This model is a fine-tuned version of [monologg/koelectra-small-v3-discriminator](https://huggingface.co/monologg/koelectra-small-v3-discriminator) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5056
- Accuracy: 0.7975

## 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: 16
- eval_batch_size: 16
- 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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 100  | 0.6845          | 0.6675   |
| No log        | 2.0   | 200  | 0.5746          | 0.7575   |
| No log        | 3.0   | 300  | 0.4979          | 0.7875   |
| No log        | 4.0   | 400  | 0.4853          | 0.795    |
| 0.5347        | 5.0   | 500  | 0.4678          | 0.8      |
| 0.5347        | 6.0   | 600  | 0.5199          | 0.7725   |
| 0.5347        | 7.0   | 700  | 0.4832          | 0.7975   |
| 0.5347        | 8.0   | 800  | 0.5078          | 0.7925   |
| 0.5347        | 9.0   | 900  | 0.5008          | 0.795    |
| 0.2996        | 10.0  | 1000 | 0.5056          | 0.7975   |


### Framework versions

- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2