Instructions to use monologg/kobert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use monologg/kobert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="monologg/kobert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("monologg/kobert") model = AutoModel.from_pretrained("monologg/kobert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from monologg/kobert: direct link, hf CLI and curl.
- Browser
- Download file 369 MB
-
https://huggingface.co/monologg/kobert/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://monologg/kobert/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/monologg/kobert/resolve/main/flax_model.msgpack
369 MB
- Xet hash:
- 9d8486d41ae351025a1390d7e7203ead6c7bd37076fbfaae00c797ac7449591b
- Size of remote file:
- 369 MB
- SHA256:
- 714e4f707702f2d5df7d4846fa4a19a8e05c156d097536606fd02286fe79bed8
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