Instructions to use SparseCL/GTE-SparseCL-hotpotqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SparseCL/GTE-SparseCL-hotpotqa with Transformers:
# Load model directly from transformers import NewModelForCL model = NewModelForCL.from_pretrained("SparseCL/GTE-SparseCL-hotpotqa", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3957805ce2cbb4335db38f5c2281278a463180eb523e21c5375aa0d7907c37e1
- Size of remote file:
- 4.09 kB
- SHA256:
- 53bcb467ae282416feede09e3300466752c00ba2834e3bae1945161f3c4e1224
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