Text Classification
Transformers
Safetensors
Korean
electra
KoELECTRA
Korean-NLP
topic-classification
news-classification
Generated from Trainer
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("heejinoh/ynat-model")
model = AutoModelForSequenceClassification.from_pretrained("heejinoh/ynat-model")Quick Links
ynat-model
This model is a fine-tuned version of monologg/koelectra-base-v3-discriminator on the klue-ynat dataset. It achieves the following results on the evaluation set:
- Loss: 0.4125
- Accuracy: 0.8613
- Precision: 0.8498
- Recall: 0.8760
- F1: 0.8621
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 with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.3895 | 1.0 | 714 | 0.4585 | 0.8414 | 0.8252 | 0.8698 | 0.8444 |
| 0.2936 | 2.0 | 1428 | 0.4038 | 0.8564 | 0.8466 | 0.8699 | 0.8566 |
| 0.2234 | 3.0 | 2142 | 0.4125 | 0.8613 | 0.8498 | 0.8760 | 0.8621 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
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Model tree for heejinoh/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="heejinoh/ynat-model")