Instructions to use macedonizer/sr-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use macedonizer/sr-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="macedonizer/sr-roberta-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("macedonizer/sr-roberta-base") model = AutoModelForMaskedLM.from_pretrained("macedonizer/sr-roberta-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d7f430ffd438381120316bd4d0c1fe06cfa1d67a2e6eac31576d54d9484d31d1
- Size of remote file:
- 334 MB
- SHA256:
- 1a7a45741b1ca52bf309634dc0d9711eeb0d3986111a09e2a4157bdba74a2698
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