Instructions to use crabz/FERNET-CC_sk-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use crabz/FERNET-CC_sk-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="crabz/FERNET-CC_sk-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("crabz/FERNET-CC_sk-ner") model = AutoModelForTokenClassification.from_pretrained("crabz/FERNET-CC_sk-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f8f3191ef1a48c854e9892644cd8aa9a1c87d6afe0724d4837b0844ab4d49a41
- Size of remote file:
- 649 MB
- SHA256:
- e0c3906ba915bf0042fac6356ec62f06093be2413385825d669ecb57fc58cffb
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