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")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaMoBert-grc-v1") model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaMoBert-grc-v1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c2fdbe634bcd4b32199a97582773a9488d6dadfdf89ed083d2c670164e2e319d
- Size of remote file:
- 5.71 kB
- SHA256:
- 6bd057175fc978c2419777b3adf1d2506653af027fe474c401f7cdd0f0ea3313
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.