Text Classification
Transformers
Safetensors
Korean
electra
korean_NLP
KoELECTRA
Generated from Trainer
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("siromii/ynat_model")
model = AutoModelForSequenceClassification.from_pretrained("siromii/ynat_model")Quick Links
ynat_model
This model is a fine-tuned version of lang-brain-4 on the klue-ynat dataset. It achieves the following results on the evaluation set:
- Loss: 0.8140
- Accuracy: 0.8472
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: 64
- eval_batch_size: 64
- 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1827 | 1.0 | 714 | 0.5506 | 0.8402 |
| 0.1315 | 2.0 | 1428 | 0.5777 | 0.8507 |
| 0.09 | 3.0 | 2142 | 0.6708 | 0.8470 |
| 0.0672 | 4.0 | 2856 | 0.7595 | 0.8470 |
| 0.034 | 5.0 | 3570 | 0.8140 | 0.8472 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="siromii/ynat_model")