Instructions to use mykor/fnet-base-ko with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mykor/fnet-base-ko with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mykor/fnet-base-ko")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mykor/fnet-base-ko") model = AutoModelForMaskedLM.from_pretrained("mykor/fnet-base-ko", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mykor/fnet-base-ko: direct link, hf CLI and curl.
- Browser
- Download file 390 MB
-
https://huggingface.co/mykor/fnet-base-ko/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mykor/fnet-base-ko/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mykor/fnet-base-ko/resolve/main/pytorch_model.bin
390 MB
- Xet hash:
- 1210f4621f6c7de569fc616a465da64293071dea4530cb8edb902bbd377c48f9
- Size of remote file:
- 390 MB
- SHA256:
- 7a8b32dad4bb2ac61b3d86e41b50f47de99657ac5eee6eb06374ccc7dfdcc010
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