Instructions to use beomi/KcBERT-v2023 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use beomi/KcBERT-v2023 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="beomi/KcBERT-v2023")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("beomi/KcBERT-v2023") model = AutoModelForMaskedLM.from_pretrained("beomi/KcBERT-v2023", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from beomi/KcBERT-v2023: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/beomi/KcBERT-v2023/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://beomi/KcBERT-v2023/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/beomi/KcBERT-v2023/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- 71607d3ad1a4448820dae38206803a5c5628c97f57562770b89cc792743fe35e
- Size of remote file:
- 499 MB
- SHA256:
- f9185e9d53f56717069e7b370582c8fed81b0d77ab5aed1a32f2e1d4c2bd466e
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