Instructions to use yeongjoon/Kconvo-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yeongjoon/Kconvo-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="yeongjoon/Kconvo-roberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("yeongjoon/Kconvo-roberta") model = AutoModelForMaskedLM.from_pretrained("yeongjoon/Kconvo-roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Update README.md
3840cc7 -
Update README.md (#2)
- Update README.md (f30b7378356f7ef126dc246e533c49a62a141909) Co-authored-by: TakSungHeo <TakSung@users.noreply.huggingface.co>36fa30a
-
Update README.md (#1)
- Update README.md (df6a46cae0801b2fb52efb80951c06297289614d) Co-authored-by: TakSungHeo <TakSung@users.noreply.huggingface.co>baf42a4
-
Upload 5 files
2178fd7 -
initial commit
2abd60c