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")# pip install -U transformers accelerate # 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 663 Bytes
-
https://huggingface.co/nextgeo/address-extraction/resolve/06410f5ed0cd054dc2169bc982c6f074a7067733/labels.json
- Command line
-
hf download hf://nextgeo/address-extraction@06410f5ed0cd054dc2169bc982c6f074a7067733/labels.json
-
curl -L -o labels.json https://huggingface.co/nextgeo/address-extraction/resolve/06410f5ed0cd054dc2169bc982c6f074a7067733/labels.json
663 Bytes
| {"1": "B-\u00dclke", "2": "I-\u00dclke", "3": "B-\u0130l", "4": "I-\u0130l", "5": "B-\u0130l\u00e7e", "6": "I-\u0130l\u00e7e", "7": "B-Mahalle", "8": "I-Mahalle", "9": "B-Cadde", "10": "I-Cadde", "11": "B-Sokak", "12": "I-Sokak", "13": "B-Bina Ad\u0131", "14": "I-Bina Ad\u0131", "15": "B-Bina Numaras\u0131", "16": "I-Bina Numaras\u0131", "17": "B-Yer Ad\u0131", "18": "I-Yer Ad\u0131", "19": "B-Site", "20": "I-Site", "21": "B-Adres Detay", "22": "I-Adres Detay", "23": "B-Blok No", "24": "I-Blok No", "25": "B-Bulvar", "26": "I-Bulvar", "27": "B-Daire No", "28": "I-Daire No", "29": "B-Posta Kodu", "30": "I-Posta Kodu", "31": "B-Kat", "32": "I-Kat", "0": "O"} |