Instructions to use yuji246/koelectra with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yuji246/koelectra with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yuji246/koelectra")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yuji246/koelectra") model = AutoModelForSequenceClassification.from_pretrained("yuji246/koelectra") - Notebooks
- Google Colab
- Kaggle
koelectra
This model is a fine-tuned version of monologg/koelectra-small-v3-discriminator on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4923
- Accuracy: 0.8
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 | 0.6866 | 0.56 |
| No log | 2.0 | 200 | 0.5962 | 0.7375 |
| No log | 3.0 | 300 | 0.5511 | 0.7525 |
| No log | 4.0 | 400 | 0.5071 | 0.77 |
| 0.5472 | 5.0 | 500 | 0.4768 | 0.7975 |
| 0.5472 | 6.0 | 600 | 0.4714 | 0.805 |
| 0.5472 | 7.0 | 700 | 0.4928 | 0.8 |
| 0.5472 | 8.0 | 800 | 0.4894 | 0.8 |
| 0.5472 | 9.0 | 900 | 0.4952 | 0.7975 |
| 0.3122 | 10.0 | 1000 | 0.4923 | 0.8 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for yuji246/koelectra
Base model
monologg/koelectra-small-v3-discriminator