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:
- a27f4bbb68a01f2a2409519482e4229bb13415cfd8ca56073142afb5c4977ecc
- Size of remote file:
- 1.06 kB
- SHA256:
- 74971fdf77702f2b62c524375aa1c538b11ad0b4c171649f0fd219c0757c7268
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