# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Eomts/KoSBi_classifier_v1")
model = AutoModelForSequenceClassification.from_pretrained("Eomts/KoSBi_classifier_v1")Quick Links
KoSBi_classifier_v1
This model is a fine-tuned version of beomi/KcELECTRA-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4059
- Accuracy: 0.8078
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4457 | 1.0 | 1700 | 0.4230 | 0.7934 |
| 0.3779 | 2.0 | 3400 | 0.4059 | 0.8078 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for Eomts/KoSBi_classifier_v1
Base model
beomi/KcELECTRA-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Eomts/KoSBi_classifier_v1")