Instructions to use ALTAH/wojood_Tah with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ALTAH/wojood_Tah with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ALTAH/wojood_Tah")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ALTAH/wojood_Tah") model = AutoModelForMaskedLM.from_pretrained("ALTAH/wojood_Tah") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 306e5c9690c243fe40d66034bee9bca9cb6a6a899ef053efa3e8009da7e623ee
- Size of remote file:
- 8.39 MB
- SHA256:
- 01954c0ecb1e0856bd1770fbbbfdf111765a4205e6125ceec341b62171b4c445
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.