Instructions to use tonoadisorn/wangchanberta-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tonoadisorn/wangchanberta-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="tonoadisorn/wangchanberta-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tonoadisorn/wangchanberta-ner") model = AutoModelForTokenClassification.from_pretrained("tonoadisorn/wangchanberta-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tonoadisorn/wangchanberta-ner: direct link, hf CLI and curl.
- Browser
- Download file 419 MB
-
https://huggingface.co/tonoadisorn/wangchanberta-ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tonoadisorn/wangchanberta-ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tonoadisorn/wangchanberta-ner/resolve/main/pytorch_model.bin
419 MB
- Xet hash:
- 2e39c6dd3065fd94ddb423c30c982e963dd817d0a75defe3992fbe44c7da5c46
- Size of remote file:
- 419 MB
- SHA256:
- 38a794ce0b562a87c96ab3fa2c7a3bbfb8628b9d7fb5a3a84f95ffdbe68b2c27
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