Instructions to use beomi/kcbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use beomi/kcbert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="beomi/kcbert-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("beomi/kcbert-base") model = AutoModelForMaskedLM.from_pretrained("beomi/kcbert-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from beomi/kcbert-base: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/beomi/kcbert-base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://beomi/kcbert-base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/beomi/kcbert-base/resolve/main/flax_model.msgpack
436 MB
- Xet hash:
- ac865e4345130d30afc05b6c34c3e0e300a3c4710c45ba454e8bb34775ca7623
- Size of remote file:
- 436 MB
- SHA256:
- cf2b5bbcf68cf684587aadc34bef5ab7a264c60d9d97845618569b96bd989cef
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