Instructions to use macedonizer/mk-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use macedonizer/mk-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="macedonizer/mk-roberta-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("macedonizer/mk-roberta-base") model = AutoModelForMaskedLM.from_pretrained("macedonizer/mk-roberta-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7f7fd6cac90c34e6e4144b926d52869424e274dcdddcb344cc173e8143830a32
- Size of remote file:
- 334 MB
- SHA256:
- de14c83d1079fa3df3415d4a6f3e0032b5961d15576af2ca7c7364111c32abf6
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