Instructions to use tau/tavbert-tr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tau/tavbert-tr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="tau/tavbert-tr")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("tau/tavbert-tr") model = AutoModelForMaskedLM.from_pretrained("tau/tavbert-tr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0fb1428cbaddec982056955afb6182b4a65562bb10a0f336f952e1f302178a49
- Size of remote file:
- 350 MB
- SHA256:
- ef2c5f9cc42662893a6b826ceb725f99cd0b89a71c6770d578b708f8bc91cd7e
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