Instructions to use dswah/address-ner-de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dswah/address-ner-de with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dswah/address-ner-de")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dswah/address-ner-de") model = AutoModelForTokenClassification.from_pretrained("dswah/address-ner-de") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 88e6e354e315975eb0c0f231bfb2e299362f00ea478b070cff38d463b96dea1b
- Size of remote file:
- 4.08 kB
- SHA256:
- 330ce7f48fc0d2d2b8ae174a875ee72712c8fc42e88ba0755bdfebcbd9f892a5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.