Instructions to use sivan22/BEREL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sivan22/BEREL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="sivan22/BEREL")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("sivan22/BEREL") model = AutoModelForMaskedLM.from_pretrained("sivan22/BEREL") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a4f8651495f13d727dc9a1044e0b9cf05950203cfc8ee98b3aec513ffce83872
- Size of remote file:
- 738 MB
- SHA256:
- fd4a02e9c9d74eaf9a68edbf723eef2f5e1edb5dce68057eb9ca7cb5a6295eb2
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