Instructions to use pitiwat/argument_wangchanberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pitiwat/argument_wangchanberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="pitiwat/argument_wangchanberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("pitiwat/argument_wangchanberta") model = AutoModelForTokenClassification.from_pretrained("pitiwat/argument_wangchanberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from pitiwat/argument_wangchanberta: direct link, hf CLI and curl.
- Browser
- Download file 837 MB
-
https://huggingface.co/pitiwat/argument_wangchanberta/resolve/main/optimizer.pt
- Command line
-
hf download hf://pitiwat/argument_wangchanberta/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/pitiwat/argument_wangchanberta/resolve/main/optimizer.pt
837 MB
- Xet hash:
- 15cb825522588f50bc9214d841efe600e6f694f4425e30eaf9db9c8699f081cc
- Size of remote file:
- 837 MB
- SHA256:
- 81c53e4af05b64bb2d9847e8dc29413b4c6d57a5ddb4f5cd5c5ace69e8548f36
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