Instructions to use EMBEDDIA/sloberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EMBEDDIA/sloberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="EMBEDDIA/sloberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("EMBEDDIA/sloberta") model = AutoModelForMaskedLM.from_pretrained("EMBEDDIA/sloberta", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download sentencepiece.bpe.model from EMBEDDIA/sloberta: direct link, hf CLI and curl.
- Browser
- Download file 800 kB
-
https://huggingface.co/EMBEDDIA/sloberta/resolve/main/sentencepiece.bpe.model
- Command line
-
hf download hf://EMBEDDIA/sloberta/sentencepiece.bpe.model
-
curl -L -o sentencepiece.bpe.model https://huggingface.co/EMBEDDIA/sloberta/resolve/main/sentencepiece.bpe.model
800 kB
- Xet hash:
- 0f337c05a28ba1dc90724f36ac19b6d65b23ee2ec13e1cb283be70b8cabcd1cb
- Size of remote file:
- 800 kB
- SHA256:
- 34b589385a2320549143ab23b0ccf82cc99a82685701cdabe0fad847bd0479ff
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