Instructions to use SparseCL/GTE-SparseCL-arguana with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SparseCL/GTE-SparseCL-arguana with Transformers:
# Load model directly from transformers import NewModelForCL model = NewModelForCL.from_pretrained("SparseCL/GTE-SparseCL-arguana", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 501817236ef246ac63e7e822af56d6a314528b3b17c862a8b5c0a1c7483eb278
- Size of remote file:
- 4.09 kB
- SHA256:
- c9d28c1554748c4e852ecb4292a6890e4f0b1fc576494b1f2b7a646ac50c07a2
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