| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: monologg/kobert |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: kobert |
| 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. --> |
|
|
| # kobert |
|
|
| This model is a fine-tuned version of [monologg/kobert](https://huggingface.co/monologg/kobert) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.6932 |
| - Accuracy: 0.5225 |
|
|
| ## 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 | 0.6960 | 0.4775 | |
| | No log | 2.0 | 200 | 0.6924 | 0.5225 | |
| | No log | 3.0 | 300 | 0.6948 | 0.4775 | |
| | No log | 4.0 | 400 | 0.6944 | 0.4775 | |
| | 0.6980 | 5.0 | 500 | 0.6947 | 0.5225 | |
| | 0.6980 | 6.0 | 600 | 0.6927 | 0.5225 | |
| | 0.6980 | 7.0 | 700 | 0.6942 | 0.4775 | |
| | 0.6980 | 8.0 | 800 | 0.6933 | 0.4775 | |
| | 0.6980 | 9.0 | 900 | 0.6942 | 0.4775 | |
| | 0.6944 | 10.0 | 1000 | 0.6932 | 0.5225 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.0.0 |
| - Pytorch 2.10.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
| |
