Instructions to use acul3/Roberta-Large-Indo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use acul3/Roberta-Large-Indo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="acul3/Roberta-Large-Indo")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("acul3/Roberta-Large-Indo") model = AutoModelForMaskedLM.from_pretrained("acul3/Roberta-Large-Indo") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 444ab165549881110986bf3d87e65d35ab9c813dcf5524f39b3a5bdcc0ce16a6
- Size of remote file:
- 1.42 GB
- SHA256:
- 0af11ebbcd5621ac3371cb24d95485b363b924d774b7a34de29845ed2d169fdf
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