Instructions to use joon09/kor-naver-ner-name-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joon09/kor-naver-ner-name-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="joon09/kor-naver-ner-name-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("joon09/kor-naver-ner-name-v2") model = AutoModelForTokenClassification.from_pretrained("joon09/kor-naver-ner-name-v2") - Notebooks
- Google Colab
- Kaggle
kor-naver-ner-name-v2
This model was trained from scratch 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.0+cu117
- Datasets 2.2.2
- Tokenizers 0.13.3
- Downloads last month
- 89