Instructions to use Ericu950/SyllaMoBert-grc-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ericu950/SyllaMoBert-grc-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Ericu950/SyllaMoBert-grc-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaMoBert-grc-v1") model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaMoBert-grc-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Ericu950/SyllaMoBert-grc-v1: direct link, hf CLI and curl.
- Browser
- Download file 5.71 kB
-
https://huggingface.co/Ericu950/SyllaMoBert-grc-v1/resolve/main/training_args.bin
- Command line
-
hf download hf://Ericu950/SyllaMoBert-grc-v1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Ericu950/SyllaMoBert-grc-v1/resolve/main/training_args.bin
5.71 kB
- Xet hash:
- c2fdbe634bcd4b32199a97582773a9488d6dadfdf89ed083d2c670164e2e319d
- Size of remote file:
- 5.71 kB
- SHA256:
- 6bd057175fc978c2419777b3adf1d2506653af027fe474c401f7cdd0f0ea3313
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