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")# 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") - Notebooks
- Google Colab
- Kaggle
Dataset
#1
by shrikrishnaholla - opened
Hey! Which dataset has been used for this? Would love to know. Also, if you could add a model card, it would also be very helpful
Hi, I've added a rough model card with dataset & base model information.
I do plan to publish a git repository with relevant code snippets.
Thanks for comment, btw!
kulkarni-harsh changed discussion status to closed