# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Developer9215/klue-ner-koelectra")
model = AutoModelForTokenClassification.from_pretrained("Developer9215/klue-ner-koelectra")Quick Links
klue-ner-koelectra
This model is a fine-tuned version of monologg/koelectra-base-v3-discriminator on an unknown 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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: 20
Training results
Framework versions
- Transformers 4.54.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2
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Model tree for Developer9215/klue-ner-koelectra
Base model
monologg/koelectra-base-v3-discriminator
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Developer9215/klue-ner-koelectra")