Instructions to use shomez/blink-biencoder-description-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shomez/blink-biencoder-description-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="shomez/blink-biencoder-description-encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("shomez/blink-biencoder-description-encoder") model = AutoModel.from_pretrained("shomez/blink-biencoder-description-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.onnx from shomez/blink-biencoder-description-encoder: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/shomez/blink-biencoder-description-encoder/resolve/main/model.onnx
- Command line
-
hf download hf://shomez/blink-biencoder-description-encoder/model.onnx
-
curl -L -o model.onnx https://huggingface.co/shomez/blink-biencoder-description-encoder/resolve/main/model.onnx
1.34 GB
- Xet hash:
- 3a2e40d81ddc0a6861ee8eeee9d53a85c30ec85d2ecd12d8a9f73416ed2b023c
- Size of remote file:
- 1.34 GB
- SHA256:
- 85b3d7832309f59bd9638bc15a9cf3a365a220721e35fcb2d73dee36373f7031
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