Instructions to use simonmun/AjouFamilyRegisterBERT_Deagu_2_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use simonmun/AjouFamilyRegisterBERT_Deagu_2_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="simonmun/AjouFamilyRegisterBERT_Deagu_2_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("simonmun/AjouFamilyRegisterBERT_Deagu_2_2") model = AutoModelForMaskedLM.from_pretrained("simonmun/AjouFamilyRegisterBERT_Deagu_2_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from simonmun/AjouFamilyRegisterBERT_Deagu_2_2: direct link, hf CLI and curl.
- Browser
- Download file 334 MB
-
https://huggingface.co/simonmun/AjouFamilyRegisterBERT_Deagu_2_2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://simonmun/AjouFamilyRegisterBERT_Deagu_2_2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/simonmun/AjouFamilyRegisterBERT_Deagu_2_2/resolve/main/pytorch_model.bin
334 MB
- Xet hash:
- 978a161d9e5d593a3e861046c201b37c66e498044b92a1c3c861c4209b26c177
- Size of remote file:
- 334 MB
- SHA256:
- dfac28f2a094132cab729ec3d5b9a18e70ecba2ccca5989875fa6e21c49e7aa3
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