Instructions to use Shenzy2/NER4DesignTutor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shenzy2/NER4DesignTutor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Shenzy2/NER4DesignTutor")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Shenzy2/NER4DesignTutor") model = AutoModelForTokenClassification.from_pretrained("Shenzy2/NER4DesignTutor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Shenzy2/NER4DesignTutor: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/Shenzy2/NER4DesignTutor/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Shenzy2/NER4DesignTutor/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Shenzy2/NER4DesignTutor/resolve/main/pytorch_model.bin
431 MB
- Xet hash:
- 44445c125b763f3550fdd943781a5bfce0ff5824e9b9697cb95684f51fe0efc4
- Size of remote file:
- 431 MB
- SHA256:
- 2e74f922c7afff731370d3eecf4ccf38a5310d3bdf529d261af2a2f130740786
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