Instructions to use Llamacha/QuBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Llamacha/QuBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Llamacha/QuBERTa")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Llamacha/QuBERTa") model = AutoModelForMaskedLM.from_pretrained("Llamacha/QuBERTa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5f4791785b8ca889ca515c1aa04165171eeaabed2d79723f720c38050e276af2
- Size of remote file:
- 334 MB
- SHA256:
- 74e80c5ab76271608eb5496010dfa71d319960f81b7fe939fef3fce8353d08d8
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