Token Classification
Transformers
Safetensors
PyTorch
bert
ner
turkish
tr
dbmdz
bert-base-cased
bert-base-turkish-cased
Instructions to use nextgeo/address-extraction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nextgeo/address-extraction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="nextgeo/address-extraction")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("nextgeo/address-extraction") model = AutoModelForTokenClassification.from_pretrained("nextgeo/address-extraction", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from nextgeo/address-extraction: direct link, hf CLI and curl.
- Browser
- Download file 4.66 kB
-
https://huggingface.co/nextgeo/address-extraction/resolve/main/training_args.bin
- Command line
-
hf download hf://nextgeo/address-extraction/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/nextgeo/address-extraction/resolve/main/training_args.bin
4.66 kB
- Xet hash:
- 5349725be93db4c86e1efdbc8e3ef2ddf4fbe49d5e331087b7d7b265feca1b11
- Size of remote file:
- 4.66 kB
- SHA256:
- 52ec3649ec97e031b39c9b67ba30182a38c953393e011aa2fefb33da76b0ad9c
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