Instructions to use SparseCL/GTE-SparseCL-msmarco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SparseCL/GTE-SparseCL-msmarco with Transformers:
# Load model directly from transformers import NewModelForCL model = NewModelForCL.from_pretrained("SparseCL/GTE-SparseCL-msmarco", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ed33b4d0a69ade4be8eb5cdead768a5e1f2daaead6b048b34ed734a3ed412634
- Size of remote file:
- 4.09 kB
- SHA256:
- 8f1d1860c201152194fec8da63c1ddd15c9b31b41cc737ce034071ce733c789b
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