Instructions to use tmills/cnlpt-negation-roberta-sharpseed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tmills/cnlpt-negation-roberta-sharpseed with Transformers:
# Load model directly from transformers import CnlpModelForClassification model = CnlpModelForClassification.from_pretrained("tmills/cnlpt-negation-roberta-sharpseed", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tmills/cnlpt-negation-roberta-sharpseed: direct link, hf CLI and curl.
- Browser
- Download file 501 MB
-
https://huggingface.co/tmills/cnlpt-negation-roberta-sharpseed/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tmills/cnlpt-negation-roberta-sharpseed/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tmills/cnlpt-negation-roberta-sharpseed/resolve/main/pytorch_model.bin
501 MB
- Xet hash:
- a3df536e9dd840fe9776e80ae67c3065ec8a8f9a21cb7a9e2153fb6ed6e2165d
- Size of remote file:
- 501 MB
- SHA256:
- f7e59caba98b7563442d71ed2e1d6a2c20d9e76ad2b8d521ff0781b2deba591b
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