# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("ss531/koelectra")
model = AutoModelForSequenceClassification.from_pretrained("ss531/koelectra")Quick Links
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: 0.5056
- 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
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Model tree for ss531/koelectra
Base model
monologg/koelectra-small-v3-discriminator
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ss531/koelectra")