Text Classification
Transformers
Safetensors
Korean
electra
korean_NLP
KoELECTRA
Generated from Trainer
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("bgh0796/ynat_model")
model = AutoModelForSequenceClassification.from_pretrained("bgh0796/ynat_model")Quick Links
ynat_model
This model is a fine-tuned version of monologg/koelectra-base-v3-discriminator on the klue-ynat dataset.
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
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
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Model tree for bgh0796/ynat_model
Base model
monologg/koelectra-base-v3-discriminator
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bgh0796/ynat_model")