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:
- 71934530d3111987827057c30783e65a7d01cd14e95c34930bda75ba871817e2
- Size of remote file:
- 1.47 kB
- SHA256:
- 0028cf1990d373ebc1ca66f80cb82e27b55770fecbc00637f4bde3571fec6cdc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.