Instructions to use buddhilive/albert-zero with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use buddhilive/albert-zero with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="buddhilive/albert-zero")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("buddhilive/albert-zero") model = AutoModelForMaskedLM.from_pretrained("buddhilive/albert-zero") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6bf654c6ea78bc3ce89743e1acb5b2a1e92dabf74cc84fbab371d4230de0ea49
- Size of remote file:
- 44.9 MB
- SHA256:
- e1f598d58222df6135bde8b220cf8b76047ed1fc7c127b006c434633f4885cd2
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