Instructions to use macedonizer/sl-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use macedonizer/sl-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="macedonizer/sl-roberta-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("macedonizer/sl-roberta-base") model = AutoModelForMaskedLM.from_pretrained("macedonizer/sl-roberta-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8ff610178106b70aa17d966c35d600bcf55c612ad3f80c2ca5e667c6668d87ba
- Size of remote file:
- 334 MB
- SHA256:
- 561010603b27bd5d9fe39e24ae8dd9e5a11fd6affaba2cde35265b1de4800a79
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