Instructions to use HueyNemud/byT5-address-normalization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HueyNemud/byT5-address-normalization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("HueyNemud/byT5-address-normalization") model = AutoModelForSeq2SeqLM.from_pretrained("HueyNemud/byT5-address-normalization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- edb60752ed3678b353c755c179f0be1befb0fd957f52eeca826296db466e2ff4
- Size of remote file:
- 5.46 kB
- SHA256:
- 8284f1f77f6ecc02226c0bf2817ae9f6e1918533afebdfb22503e59031685627
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.