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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("macedonizer/sr-roberta-base") model = AutoModelForMaskedLM.from_pretrained("macedonizer/sr-roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from macedonizer/sr-roberta-base: direct link, hf CLI and curl.
- Browser
- Download file 334 MB
-
https://huggingface.co/macedonizer/sr-roberta-base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://macedonizer/sr-roberta-base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/macedonizer/sr-roberta-base/resolve/main/flax_model.msgpack
334 MB
- Xet hash:
- d7f430ffd438381120316bd4d0c1fe06cfa1d67a2e6eac31576d54d9484d31d1
- Size of remote file:
- 334 MB
- SHA256:
- 1a7a45741b1ca52bf309634dc0d9711eeb0d3986111a09e2a4157bdba74a2698
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