Instructions to use Film8844/wangchanberta-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Film8844/wangchanberta-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Film8844/wangchanberta-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Film8844/wangchanberta-ner") model = AutoModelForTokenClassification.from_pretrained("Film8844/wangchanberta-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fcb5cabd2c82e3d2f650630891c8d6f39c4724c99c8c54a4ed0c181e618fbfa8
- Size of remote file:
- 419 MB
- SHA256:
- 30eadac0001e1a02c516742bd4b911ad0e95aef81095604766b0f79c86fb2506
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.