Instructions to use iamatif2003/Rubz with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iamatif2003/Rubz with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="iamatif2003/Rubz")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("iamatif2003/Rubz") model = AutoModel.from_pretrained("iamatif2003/Rubz") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4153f988d5a87dc2faf4beca1177707e464bb087ed5779409b2ef04f0e87107c
- Size of remote file:
- 1.77 GB
- SHA256:
- e9b1fb5177dc72a3503434e9fcc89c06edd7513b93550f0c6b59d01eff658727
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