Instructions to use SparseCL/BGE-SparseCL-arguana with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SparseCL/BGE-SparseCL-arguana with Transformers:
# Load model directly from transformers import AutoTokenizer, our_BertForCL tokenizer = AutoTokenizer.from_pretrained("SparseCL/BGE-SparseCL-arguana") model = our_BertForCL.from_pretrained("SparseCL/BGE-SparseCL-arguana", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 45136b54daa1c9c46a42f5badee7317878859b18fcf7b20136bf2ce2cbc9601d
- Size of remote file:
- 871 MB
- SHA256:
- d444b25fa6162e97022ad0dbc0e4fae80be2f1175feb12e606ca70a74555fb9b
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