Instructions to use WhiteRoomProdigy/amicus-ner-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WhiteRoomProdigy/amicus-ner-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="WhiteRoomProdigy/amicus-ner-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("WhiteRoomProdigy/amicus-ner-v1") model = AutoModelForTokenClassification.from_pretrained("WhiteRoomProdigy/amicus-ner-v1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- baff42fe0e096eb2b4cb45670ec43e3911401fdd8d00b37846f4fc9c0bf7b570
- Size of remote file:
- 871 MB
- SHA256:
- 29e3c7a3e1dde6182175d05fb1ba794db97698e1dfb13b17adbeb800fa6ac5b7
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