Instructions to use Andrija/SRoBERTa-L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Andrija/SRoBERTa-L with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Andrija/SRoBERTa-L")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Andrija/SRoBERTa-L") model = AutoModelForMaskedLM.from_pretrained("Andrija/SRoBERTa-L", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Roberta model called SRoBERTa trained on WOL dataset (OSCAR + Leipzig + srWac) for Serbian language. Attention heads distilled 6, batch size 64, group size 64, epochs 2, test split 0.05(~1mil groups)
4e75a39 Download training_args.bin from Andrija/SRoBERTa-L: direct link, hf CLI and curl.
- Browser
- Download file 2.67 kB
-
https://huggingface.co/Andrija/SRoBERTa-L/resolve/main/training_args.bin
- Command line
-
hf download hf://Andrija/SRoBERTa-L/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Andrija/SRoBERTa-L/resolve/main/training_args.bin
2.67 kB
- Xet hash:
- 8fddec2beaf82320bbc8e8886119c41a69c8f5a0e2081f24ed55f21cbdf88865
- Size of remote file:
- 2.67 kB
- SHA256:
- 96bbea3cb7f41d85346a77688e8deb7feb8fbca21221425e774b101409de381a
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