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:
- b9941ff759f0361ef9543f5e61a0ec6a9c28429246557f261b803bef98d1b534
- Size of remote file:
- 4.09 kB
- SHA256:
- 9de40a7aef8bfe75b782cb7fd1e4244b76ed6f0b70ef2c290ed413203bee48ad
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