Instructions to use PragmaticMachineLearning/address-norm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PragmaticMachineLearning/address-norm with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("PragmaticMachineLearning/address-norm") model = AutoModelForSeq2SeqLM.from_pretrained("PragmaticMachineLearning/address-norm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Update generation_config.json
595dd11 -
Adding `safetensors` variant of this model (#2)
- Adding `safetensors` variant of this model (cc10bef80a10c67df1226a0b4c1adf0b0fa4ef20) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>9ffb60e -
Upload tokenizer (#1)
- Upload tokenizer (10b19c3d759652f7b2f4b1c704fe2a70cf126634)c998803 -
Upload 4 files
4c98190 -
initial commit
8ec1fd4