| | --- |
| | base_model: monologg/koelectra-small-v3-discriminator |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - generator |
| | metrics: |
| | - accuracy |
| | - f1 |
| | - precision |
| | - recall |
| | model-index: |
| | - name: chkpt |
| | results: |
| | - task: |
| | name: Text Classification |
| | type: text-classification |
| | dataset: |
| | name: generator |
| | type: generator |
| | config: default |
| | split: train |
| | args: default |
| | metrics: |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.8826086956521739 |
| | - name: F1 |
| | type: f1 |
| | value: 0.8275730495029622 |
| | - name: Precision |
| | type: precision |
| | value: 0.7789981096408317 |
| | - name: Recall |
| | type: recall |
| | value: 0.8826086956521739 |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # chkpt |
| |
|
| | This model is a fine-tuned version of [monologg/koelectra-small-v3-discriminator](https://huggingface.co/monologg/koelectra-small-v3-discriminator) on the generator dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 1.2815 |
| | - Accuracy: 0.8826 |
| | - F1: 0.8276 |
| | - Precision: 0.7790 |
| | - Recall: 0.8826 |
| |
|
| | ## 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: 32 |
| | - eval_batch_size: 128 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 1 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | | No log | 1.0 | 29 | 1.2815 | 0.8826 | 0.8276 | 0.7790 | 0.8826 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.35.2 |
| | - Pytorch 2.1.1 |
| | - Datasets 2.15.0 |
| | - Tokenizers 0.15.0 |
| |
|
