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:
- d012d2caa2cfaa96104f78878c1baeabf5e18702c4a6ed2a232457072a77c3d8
- Size of remote file:
- 2.8 kB
- SHA256:
- adafae55e9a2269a351f578f418558bc1f0da154d790e406a69397d76315015d
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