Instructions to use tau/tavbert-ar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tau/tavbert-ar with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="tau/tavbert-ar")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("tau/tavbert-ar") model = AutoModelForMaskedLM.from_pretrained("tau/tavbert-ar", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9fed07b3659b276877e63ec9c76cb573aedef454ea5cab46e07f7216ab23dfb3
- Size of remote file:
- 350 MB
- SHA256:
- 8698054675af696bb0f18c1268ef07d2d2e1497714a96a01551b25347f225197
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