Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Zudy07/naver_review with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Zudy07/naver_review with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Zudy07/naver_review")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Zudy07/naver_review") model = AutoModelForSequenceClassification.from_pretrained("Zudy07/naver_review", device_map="auto") - Notebooks
- Google Colab
- Kaggle
naver_review
This model is a fine-tuned version of beomi/kcbert-base on the None dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.0776
- eval_runtime: 198.3836
- eval_samples_per_second: 252.022
- eval_steps_per_second: 1.971
- epoch: 1.0
- step: 1172
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: 128
- eval_batch_size: 128
- 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 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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Model tree for Zudy07/naver_review
Base model
beomi/kcbert-base