Instructions to use kulkarni-harsh/address-extraction-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kulkarni-harsh/address-extraction-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="kulkarni-harsh/address-extraction-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("kulkarni-harsh/address-extraction-ner") model = AutoModelForTokenClassification.from_pretrained("kulkarni-harsh/address-extraction-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from kulkarni-harsh/address-extraction-ner: direct link, hf CLI and curl.
- Browser
- Download file 669 kB
-
https://huggingface.co/kulkarni-harsh/address-extraction-ner/resolve/main/tokenizer.json
- Command line
-
hf download hf://kulkarni-harsh/address-extraction-ner/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/kulkarni-harsh/address-extraction-ner/resolve/main/tokenizer.json
669 kB
File too large to display, you can check the raw version instead.