Instructions to use kobkrit/wangchanberta-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kobkrit/wangchanberta-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="kobkrit/wangchanberta-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("kobkrit/wangchanberta-ner") model = AutoModelForTokenClassification.from_pretrained("kobkrit/wangchanberta-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b95c94ce3b8f785febdc5797502bf484e9a9c76cc6779b1109d3f66339bbd16f
- Size of remote file:
- 419 MB
- SHA256:
- f447c28631b07b8770513062ffd0c8281ae6c3f431023d4127e3c8853f45fc55
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