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:
# pip install -U transformers accelerate # 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
Download checkpoint-446/scheduler.pt from SparseCL/BGE-SparseCL-arguana: direct link, hf CLI and curl.
- Browser
- Download file 627 Bytes
-
https://huggingface.co/SparseCL/BGE-SparseCL-arguana/resolve/main/checkpoint-446/scheduler.pt
- Command line
-
hf download hf://SparseCL/BGE-SparseCL-arguana/checkpoint-446/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/SparseCL/BGE-SparseCL-arguana/resolve/main/checkpoint-446/scheduler.pt
627 Bytes
- Xet hash:
- bc89c7af7c6162b48a2f22bc57f66f4d656c07f686b6ce2ec8d3bdee67066c3e
- Size of remote file:
- 627 Bytes
- SHA256:
- e87a0295a20de0a7d18f617b38f6653507c08e0fc8b2cb6049c2a7531faba1f3
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