Instructions to use qwazer/rubert-address-elements with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qwazer/rubert-address-elements with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="qwazer/rubert-address-elements")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("qwazer/rubert-address-elements") model = AutoModelForTokenClassification.from_pretrained("qwazer/rubert-address-elements", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from qwazer/rubert-address-elements: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/qwazer/rubert-address-elements/resolve/main/training_args.bin
- Command line
-
hf download hf://qwazer/rubert-address-elements/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/qwazer/rubert-address-elements/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- 6d8b57b7fb5a76718c2c3a3072b00f4f00fe3cc02b8f00d617c6ed0489a6cedf
- Size of remote file:
- 4.98 kB
- SHA256:
- f243e11b909d40555978b91e26e36429850e734e393d261609d625f8bcd8cbf0
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