Instructions to use UGARIT/grc-ner-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UGARIT/grc-ner-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="UGARIT/grc-ner-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("UGARIT/grc-ner-bert") model = AutoModelForTokenClassification.from_pretrained("UGARIT/grc-ner-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9f2f9a58a74fa9f9fb4d1b18c3207b178eab301de3e8c335d0d3b4890571134e
- Size of remote file:
- 4.09 kB
- SHA256:
- e3fbc8399115666d9f122d90acf36c2334aa64ca15e6d86d505c9d53cc11e2d5
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