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 labels.json from nextgeo/address-extraction: direct link, hf CLI and curl.
- Browser
- Download file 319 Bytes
-
https://huggingface.co/nextgeo/address-extraction/resolve/main/labels.json
- Command line
-
hf download hf://nextgeo/address-extraction/labels.json
-
curl -L -o labels.json https://huggingface.co/nextgeo/address-extraction/resolve/main/labels.json
319 Bytes
| {"1": "\u00dclke", "2": "\u0130l", "3": "\u0130l\u00e7e", "4": "Mahalle", "5": "Cadde", "6": "Sokak", "7": "Bina Ad\u0131", "8": "Bina Numaras\u0131", "9": "Yer Ad\u0131", "10": "Site", "11": "Adres Detay", "12": "Blok No", "13": "Bulvar", "14": "Daire No", "15": "Posta Kodu", "16": "Kat", "0": "[PAD]", "17": "[UNK]"} |