Instructions to use rjac/ner-distilbert-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rjac/ner-distilbert-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="rjac/ner-distilbert-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("rjac/ner-distilbert-cased") model = AutoModelForTokenClassification.from_pretrained("rjac/ner-distilbert-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f9165cc29e842f157ccfe9bcf61171cd19a417382273ef8f919da75d663cf20b
- Size of remote file:
- 261 MB
- SHA256:
- a21acc14617ed20b92e3641c47472d37b6aa67a3e5a5313e5a5fe02055678e89
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