Instructions to use tgool/kobert-lm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tgool/kobert-lm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="tgool/kobert-lm")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("tgool/kobert-lm") model = AutoModelForMaskedLM.from_pretrained("tgool/kobert-lm") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5846b3a8bd07ea0fa22bee4ed86d80452aaf8427716e0c6f7dc3798f25e84093
- Size of remote file:
- 369 MB
- SHA256:
- 25db7de2c981e82a7a3e7b9829c82e12437b921ba3c033acde61edc83862adea
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